@conference {cBonet22, title = {Channel Redundancy and Overlap in Convolutional Neural Networks with Channel-Wise NNK Graphs}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2022}, month = {05/2022}, abstract = {

Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret. However, convolutional layers consist of multiple channels that are activated by different types of inputs, which suggests that more insights may be gained by studying the channels and how they relate to each other. In this paper, we first analyze theoretically channel-wise non-negative kernel (CW-NNK) regression graphs, which allow us to quantify the overlap between channels and, indirectly, the intrinsic dimension of the data representation manifold. We find that redundancy between channels is significant and varies with the layer depth and the level of regularization during training. Additionally, we observe that there is a correlation between channel overlap in the last convolutional layer and generalization performance. Our experimental results demonstrate that these techniques can lead to a better understanding of deep representations.\ 

}, author = {Bonet, D. and Ortega, A. and Ruiz-Hidalgo, J. and Shekkizhar, S.} } @article {canet2022context, title = {Context-unsupervised adversarial network for video sensors}, journal = {Sensors}, volume = {22}, year = {2022}, pages = {3171}, abstract = {

Foreground object segmentation is a crucial first step for surveillance systems based on networks of video sensors. This problem in the context of dynamic scenes has been widely explored in the last two decades, but it still has open research questions due to challenges such as strong shadows, background clutter and illumination changes. After years of solid work based on statistical background pixel modeling, most current proposals use convolutional neural networks (CNNs) either to model the background or to make the foreground/background decision. Although these new techniques achieve outstanding results, they usually require specific training for each scene, which is unfeasible if we aim at designing software for embedded video systems and smart cameras. Our approach to the problem does not require specific context or scene training, and thus no manual labeling. We propose a network for a refinement step on top of conventional state-of-the-art background subtraction systems. By using a statistical technique to produce a rough mask, we do not need to train the network for each scene. The proposed method can take advantage of the specificity of the classic techniques, while obtaining the highly accurate segmentation that a deep learning system provides. We also show the advantage of using an adversarial network to improve the generalization ability of the network and produce more consistent results than an equivalent non-adversarial network. The results provided were obtained by training the network on a common database, without fine-tuning for specific scenes. Experiments on the unseen part of the CDNet database provided 0.82 a F-score, and 0.87 was achieved for LASIESTA databases, which is a database unrelated to the training one. On this last database, the results outperformed by 8.75\% those available in the official table. The results achieved for CDNet are well above those of the methods not based on CNNs, and according to the literature, among the best for the context-unsupervised CNNs systems.

}, author = {Canet Tarr{\'e}s, Gemma and Pard{\`a}s, Montse} } @conference {cHernandez22a, title = {Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours.}, booktitle = {Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)}, year = {2022}, month = {09/2022}, abstract = {

Sentinel lymph node status is a crucial prognosis factor for melanomas; nonetheless, the invasive surgery required to obtain it always puts the patient at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited datasets. We propose a pre-training strategy based on self-supervised contrastive learning to extract better image feature representations and an attention-based Multiple Instance Learning approach to enhance the model{\textquoteright}s performance. With this work, we quantitatively demonstrate that combining both methods improves various classification metrics and qualitatively show that contrastive learning encourages the network to output higher attention scores to tumour tissue and lower scores to image artifacts.

}, author = {Carlos Hernandez and Marc Combalia and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {cBonet21, title = {Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation}, booktitle = {Asia Pacific Signal and Information Processing Association Annual Summit, APSIPA}, year = {2021}, month = {12/2021}, address = {Tokyo, Japan}, abstract = {

State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to determine when to stop training the model, as this has a significant impact on generalization. Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels, where analyzing intermediate data representations and the model{\textquoteright}s evolution can be challenging owing to the curse of dimensionality. We present channel-wise DeepNNK (CW-DeepNNK), a novel channel-wise generalization estimate based on non-negative kernel regression (NNK) graphs with which we perform local polytope interpolation on low-dimensional channels. This method leads to instance-based interpretability of both the learned data representations and the relationship between channels. Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel. Our experiments demonstrate that our proposed method has advantages as compared to the standard criterion based on validation set performance.

}, url = {https://arxiv.org/abs/2107.12972}, author = {Bonet, D. and Ortega, A. and Ruiz-Hidalgo, J. and Shekkizhar, S.} } @conference {cVentura20, title = {Can Everybody Sign Now? Exploring Sign Language Video Generation from 2D Poses}, booktitle = {ECCV 2020 Workshop on Sign Language recognition, Production and Translation (SLRTP)}, year = {2020}, month = {08/2020}, abstract = {

Recent work have addressed the generation of human poses represented by 2D/3D coordinates of human joints for sign language. We use the state of the art in Deep Learning for motion transfer and evaluate them on How2Sign, an American Sign Language dataset, to generate videos of signers performing sign language given a 2D pose skeleton. We evaluate the generated videos quantitatively and qualitatively showing that the current models are not enough to generated adequate videos for Sign Language due to lack of detail in hands.

}, author = {Lucas Ventura and Amanda Duarte and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xGonzalez-i-Calabuig20, title = {Curriculum Learning for Recurrent Video Object Segmentation}, year = {2020}, abstract = {

Video object segmentation (VOS) is a computer vision task that aims at determining\ the pixels of an object of interest along a video sequence. This thesis explores different\ curriculum learning strategies for a deep neural network trained to solve this task.

Curriculum learning defines a methodology where the training data are not randomly presented to the model, instead, they are organized in a meaningful way. Simple concepts are first presented and gradually become more complex. Four different\ curriculum strategies are explored: schedule sampling, frame skipping, the effect of\ temporal and spatial recurrence variations and loss penalization by the object{\textquoteright}s area.

This work focuses on the RVOS neural architecture, a recurrent architecture originally tested on the DAVIS and YouTube-VOS datasets for one-shot video object segmentation, over the cars class of the KITTI-MOTS dataset. Even though this architecture is a fast solution for the VOS task, the model struggles with the KITTI-MOTS\ dataset, whose videos are more crowded and challenging.

For the schedule sampling curriculum, both the classic and inverse implementations are evaluated. Results show how inverse schedule sampling strategies improve\ the model{\textquoteright}s performance instead of the classic approach, the forward one. The different frame skipping schemes are also beneficial, but only when training with the\ ground truth mask instead of the predicted ones. Lastly, both the curriculums that\ vary the temporal and spatial recurrence or penalize the loss by the object{\textquoteright}s area have\ shown poor model{\textquoteright}s performance.

These results show how curriculum learning strategies affect greatly the performance of recurrent neural networks. Moreover, the results on the inverse schedule\ sampling and frame skipping strategies invite to further explore this schemes to exploit\ their benefits.




Curriculum Learning for Recurrent Video Object Segmentationfrom\ Universitat Polit{\`e}cnica de Catalunya
}, author = {Gonzalez-i-Calabuig, Maria}, editor = {Xavier Gir{\'o}-i-Nieto and Ventura, C.} } @conference {cGonzalez-i-Calabuig, title = {Curriculum Learning for Recurrent Video Object Segmentation}, booktitle = {ECCV 2020 Women in Computer Vision Workshop}, year = {2020}, month = {08/2020}, abstract = {

Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks.\ This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture.\ Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones.



Curriculum Learning for Recurrent Video Object Segmentation from Universitat Polit{\`e}cnica de Catalunya
}, author = {Gonzalez-i-Calabuig, Maria and Ventura, C. and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xDomenech19, title = {Clasificaci{\'o}n de im{\'a}genes dermatosc{\'o}picas utilizando Redes Neuronales Convolucionales e informaci{\'o}n de metadatos}, year = {2019}, abstract = {

Convolutional Neural Networks (CNNs) is a technology that is evolving very quickly. The CNNs are used in image recognition to improve computer vision systems in image classification, object detection and segmentation tasks. For all the mentioned features we can apply the CNN in the field of medicine to automate the techniques of obtaining diagnosis and facilitate the tasks of doctors.

The purpose of this project is to classify images of different skin lesions in order to detect possible diseases such as skin cancer (melanoma) through CNNs and metadata information. The images used in this project have been provided by the Dermatology Department of the Clinical Hospital of Barcelona

}, author = {Teresa Domenech}, editor = {Ver{\'o}nica Vilaplana} } @mastersthesis {xTarres19, title = {Clasificaci{\'o}n de lesiones de piel con un ensemble de redes neuronales residuales}, year = {2019}, abstract = {

Convolutional Neural Networks have gained popularity in the recent years due to their performance regarding image analysis, both in classification and segmentation. Especially in the medical field, it is increasingly common to use automatic techniques to help specialists with the diagnosis.

In this thesis, the problem of skin lesion classification is studied. The study is based on the ISIC Challenges, given the collaboration with Hospital Cl{\'\i}nic de Barcelona, and we help in the development of the database for the ISIC Challenge 2019.

One of the key points of the development is obtaining a model that manages to classify with accuracy a database provided. To do so, we study residual neural networks and an ensemble of them to further improve the results.

The purpose of this project, therefore is the study, analysis and evaluation of the variants and modifications of residual neural networks so that it adapts to our problem using an ensemble of them. In the process, the neural network will have to tackle the problem of class imbalance

}, author = {Laia Tarr{\'e}s}, editor = {Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cIsart, title = {CNN-based bacilli detection in sputum samples for tuberculosis diagnosis}, booktitle = {International Symposium on Biomedical Imaging (ISBI 2019)}, year = {2019}, month = {04/2019}, abstract = {

Tuberculosis (TB) is one of the infectious diseases that causes more deaths in low and middle-income countries. A low-cost method to diagnose TB consists in analyzing sputum smear samples through microscope observation. Manual identification and counting of bacilli is a very time consuming task and the sensitivity of the diagnosis depends on the availability of skilled technicians. We propose a computer vision technique based on a convolutional neural network (CNN) to automatically segment and count bacilli in sputum samples and predict the infection level.

}, author = {Antoni Isart and Mateu Espasa and Ver{\'o}nica Vilaplana and Elisa Sayrol} } @conference {cSalgueiro, title = {Comparative study of upsampling methods for super-resolution in remote sensing}, booktitle = {International Conference on Machine Vision}, year = {2019}, month = {11/2019}, abstract = {

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a low-resolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved.\  The best results are obtained training a model with RGB-IR channels and using progressive upsampling.\ 

}, keywords = {deep learning, Remote sensing, Super-resolution, WorldView-2}, author = {Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @phdthesis {dSalvador, title = {Computer Vision beyond the visible: Image understanding through language}, volume = {Phd}, year = {2019}, school = {Universitat Politecnica de Catalunya}, address = {Barcelona}, abstract = {

In the past decade, deep neural networks have revolutionized computer vision. High performing deep neural architectures trained for visual recognition tasks have pushed the field towards methods relying on learned image representations instead of hand-crafted ones, in the seek of designing end-to-end learning methods to solve challenging tasks, ranging from long-lasting ones such as image classification to newly emerging tasks like image captioning.\ 

As this thesis is framed in the context of the rapid evolution of computer vision, we present contributions that are aligned with three major changes in paradigm that the field has recently experienced, namely 1) the power of re-utilizing deep features from pre-trained neural networks for different tasks, 2) the advantage of formulating problems with end-to-end solutions given enough training data, and 3) the growing interest of describing visual data with natural language rather than pre-defined categorical label spaces, which can in turn enable visual understanding beyond scene recognition.

The first part of the thesis is dedicated to the problem of visual instance search, where we particularly focus on obtaining meaningful and discriminative image representations which allow efficient and effective retrieval of similar images given a visual query. Contributions in this part of the thesis involve the construction of sparse Bag-of-Words image representations from convolutional features from a pre-trained image classification neural network, and an analysis of the advantages of fine-tuning a pre-trained object detection network using query images as training data.

The second part of the thesis presents contributions to the problem of image-to-set prediction, understood as the task of predicting a variable-sized collection of unordered elements for an input image. We conduct a thorough analysis of current methods for multi-label image classification, which are able to solve the task in an end-to-end manner by simultaneously estimating both the label distribution and the set cardinality. Further, we extend the analysis of set prediction methods to semantic instance segmentation, and present an end-to-end recurrent model that is able to predict sets of objects (binary masks and categorical labels) in a sequential manner.

Finally, the third part of the dissertation takes insights learned in the previous two parts in order to present deep learning solutions to connect images with natural language in the context of cooking recipes and food images. First, we propose a retrieval-based solution in which the written recipe and the image are encoded into compact representations that allow the retrieval of one given the other. Second, as an alternative to the retrieval approach, we propose a generative model to predict recipes directly from food images, which first predicts ingredients as sets and subsequently generates the rest of the recipe one word at a time by conditioning both on the image and the predicted ingredients.

}, url = {https://www.tdx.cat/handle/10803/667162}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @article {aPujol-Miro19, title = {Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks}, journal = {Image and Vision Computing}, volume = {83-84}, year = {2019}, month = {04/2019}, chapter = {51-60}, abstract = {

This document presents a novel method based in Convolutional Neural Networks (CNN) to obtain correspondence matchings between sets of keypoints of several unorganized 3D point cloud captures, independently of the sensor used. The proposed technique extends a state-of-the-art method for correspondence matching in standard 2D images to sets of unorganized 3D point clouds. The strategy consists in projecting the 3D neighborhood of the keypoint onto an RGBD patch, and the classification of patch pairs using CNNs. The objective evaluation of the proposed 3D point matching based in CNNs outperforms existing 3D feature descriptors, especially when intensity or color data is available.

}, doi = {https://doi.org/10.1016/j.imavis.2019.02.013}, author = {A. Pujol-Mir{\'o} and Casas, J. and Ruiz-Hidalgo, J.} } @conference {cDuarteb, title = {Cross-modal Neural Sign Language Translation}, booktitle = {Proceedings of the 27th ACM International Conference on Multimedia - Doctoral Symposium}, year = {2019}, month = {10/2019}, publisher = {ACM}, organization = {ACM}, address = {Nice, France}, abstract = {

Sign Language is the primary means of communication for the majority of the Deaf and hard-of-hearing communities. Current computational approaches in this general research area have focused specifically on sign language recognition and the translation of sign language to text. However, the reverse problem of translating from spoken to sign language has so far not been widely explored.

The goal of this doctoral research is to explore sign language translation in this generalized setting, i.e. translating from spoken language to sign language and vice versa. Towards that end, we propose a concrete methodology for tackling the problem of speech to sign language translation and introduce How2Sign, the first public, continuous American Sign Language dataset that enables such research. With a parallel corpus of almost 60 hours of sign language videos (collected with both RGB and depth sensor data) and the corresponding speech transcripts for over 2500 instructional videos, How2Sign is a public dataset of unprecedented scale that can be used to advance not only sign language translation, but also a wide range of sign language understanding tasks.

Poster at ACM Multimedia 2019

Xavier Giro and Amanda Duarte in ACM Multimedia 2019.

}, doi = {10.1145/3343031.3352587}, url = {https://dl.acm.org/citation.cfm?id=3352587}, author = {Amanda Duarte}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @inbook {bCasamitjana18, title = {Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation}, booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017}, volume = { 10670}, number = {Lecture Notes in Computer Science}, year = {2018}, pages = {381-391}, publisher = {Springer}, organization = {Springer}, edition = {Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds)}, address = {Cham}, abstract = {

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture, reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions, such as brain tumor segmentation, by focusing training only on the vecinity of the tumor area. We report results on BraTS2017 Training and Validation sets.

}, isbn = {978-3-319-75238-9}, issn = {978-3-319-75237-2}, doi = {https://doi.org/10.1007/978-3-319-75238-9_33}, url = {https://link.springer.com/chapter/10.1007/978-3-319-75238-9_33}, author = {Adri{\`a} Casamitjana and Marcel Cat{\`a} and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cPetrone, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

In the last decade, magnetic resonance imaging has unveiled specific AD alterations at different stages of the AD pathophysiologic continuum that conform what has been established as the AD signature. To which extent MRI can detect structural changes at the preclinical asymptomatic stage of AD - the preclinical AD signature- is still an area open for exploration. Our work supports the idea that there are brain volumetric changes specific to preclinical AD subjects and defines the preclinical AD signature based on longitudinal data. While some regions show a pattern of atrophy that overlaps with the AD signature, other specific regions exhibit changes that are unique to this early asymptomatic AD stage.

}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aPetrone18, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature.}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {14}, year = {2018}, month = {07/2018}, pages = {P1235}, doi = {10.1016/j.jalz.2018.06.1737}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @mastersthesis {xPelegri18, title = {Clasificaci{\'o}n de im{\'a}genes histol{\'o}gicas mediante redes neuronales convolucionales}, year = {2018}, author = {Joel Bustos Pelegr{\'\i}}, editor = {Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cFojo, title = {Comparing Fixed and Adaptive Computation Time for Recurrent Neural Network}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2018}, month = {02/2018}, address = {Vancouver, Canada}, abstract = {

Deep networks commonly perform better than shallow ones, but allocating the proper amount of computation for each particular input sample remains an open problem. This issue is particularly challenging in sequential tasks, where the required complexity may vary for different tokens in the input sequence. Adaptive Computation Time (ACT) was proposed as a method for dynamically adapting the computation at each step for Recurrent Neural Networks (RNN). ACT introduces two main modifications to the regular RNN formulation: (1) more than one RNN steps may be executed between an input sample is fed to the layer and and this layer generates an output,\  and (2) this number of steps is dynamically predicted depending on the input token and the hidden state of the network. In our work, we aim at gaining intuition about the contribution of these two factors to the overall performance boost observed when augmenting RNNs with ACT. We design a new baseline, Repeat-RNN, which performs a constant number of RNN state updates larger than one before generating an output. Surprisingly, such uniform distribution of the computational resources matches the performance of ACT in the studied tasks. We hope that this finding motivates new research efforts towards designing RNN architectures that are able to dynamically allocate computational resources.

Reproducing and Analyzing Adaptive Computation Time in PyTorch and TensorFlow from Universitat Polit{\`e}cnica de Catalunya
}, author = {Fojo, Daniel and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @conference {cSuris, title = {Cross-modal Embeddings for Video and Audio Retrieval}, booktitle = {ECCV 2018 Women in Computer Vision Workshop}, year = {2018}, month = {09/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

The increasing amount of online videos brings several opportunities for training self-supervised neural networks. The creation of large scale datasets of videos such as the YouTube-8M allows us to deal with this large amount of data in manageable way. In this work, we find new ways of exploiting this dataset by taking advantage of the multi-modal information it provides. By means of a neural network, we are able to create links between audio and visual documents, by projecting them into a common region of the feature space, obtaining joint audio-visual embeddings. These links are used to retrieve audio samples that fit well to a given silent video, and also to retrieve images that match a given a query audio. The results in terms of Recall@K obtained over a subset of YouTube-8M videos show the potential of this unsupervised approach for cross-modal feature learning. We train embeddings for both scales and assess their quality in a retrieval problem, formulated as using the feature extracted from one modality to retrieve the most similar videos based on the features computed in the other modality.

Cross Modal Embeddings for Video and Audio Retrieval $\#$WiCV18 from Universitat Polit{\`e}cnica de Catalunya
}, isbn = {978-3-030-11018-5}, doi = {10.1007/978-3-030-11018-5_62}, url = {https://doi.org/10.1007/978-3-030-11018-5_62}, author = {Sur{\'\i}s, D{\'\i}dac and Amanda Duarte and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xJimenez, title = {Class Weighted Convolutional Features for Image Retrieval}, year = {2017}, abstract = {

Program: Master{\textquoteright}s Degree in Telecommunications Engineering

Grade: A with honours (10.0/10.0)

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or ne-tuning a model every time new images are added to the database is neither ecient nor scalable. Convolutional Neural Networks trained for image classi cation over large datasets have been proven e ective feature extractors when transferred to the task of image retrieval. The most successful approaches are based in encoding the activations of convolutional layers as they convey the image spatial information. Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network. In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions of the image from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a rst fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using o -the-shelf models trained on the object classes of ImageNet.

[Project page]

Class Weighted Convolutional Features for Image Retrieval from Xavier Giro-i-Nieto
}, keywords = {Convolutional Neural Networks, deep learning, image retrieval, Transfer Learning, Visual Instance Search}, author = {Jim{\'e}nez, Albert}, editor = {Xavier Gir{\'o}-i-Nieto and {\'A}lvarez, Jose M} } @conference {cJimenez, title = {Class Weighted Convolutional Features for Visual Instance Search}, booktitle = {28th British Machine Vision Conference (BMVC)}, year = {2017}, month = {09/2017}, address = {London, UK}, abstract = {

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images.\ In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable.\ Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors when transferred to the task of image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. \ Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network.\ In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a first fast search.\ Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using off-the-shelf models trained on the object classes of ImageNet.

Class Weighted Convolutional Features for Image Retrieval from Xavier Giro-i-Nieto
}, author = {Jim{\'e}nez, Albert and {\'A}lvarez, Jose M and Xavier Gir{\'o}-i-Nieto} } @article {aMartinez17, title = {A closed-loop approach for tracking a humanoid robot using particle filtering and depth data}, journal = {Intelligent Service Robotics}, volume = {10}, year = {2017}, month = {10/2017}, pages = {297{\textendash}312}, abstract = {

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.

}, keywords = {Humanoid robot, ~Particle Filter, ~RGB-D sensor, ~ROS, ~Tracking}, issn = {1861-2776}, doi = {10.1007/s11370-017-0230-0}, url = {http://rdcu.be/s1RE}, author = {Mart{\'\i}nez, P. A. and X. Lin and Castel{\'a}n, M and Casas, J. and Arechavaleta, G.} } @conference {cvan Sabben17, title = {Collaborative voting of 3D features for robust gesture estimation}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2017}, month = {03/2017}, address = {New Orleans, USA}, abstract = {

Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each selected point (anchor point) contributes to the estimation by casting votes. The main idea is to detect a body part by accumulating the inference of other trained body parts. The collaborative voting encodes the global context of human pose, while 3D features represent local appearance. Body parts contributing to the detection are interpreted as a voting process. Experimental results for different 3D features prove the validity of the proposed algorithm.

}, author = {van Sabben, D. and Ruiz-Hidalgo, J. and Suau, X. and Casas, J.} } @conference {cGorriza, title = {Cost-Effective Active Learning for Melanoma Segmentation}, booktitle = {ML4H: Machine Learning for Health Workshop at NIPS 2017}, year = {2017}, month = {11/2017}, address = {Long Beach, CA, USA}, abstract = {

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using Dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance.

Active Deep Learning for Medical Imaging from Xavier Giro-i-Nieto
}, url = {https://arxiv.org/abs/1711.09168}, author = {G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} } @conference {cPoignant16, title = {The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {05/2016}, address = {Portoro{\v z} (Slovenia)}, abstract = {

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.

}, keywords = {active learning, Annotation tool, collaborative annotation, multimedia, person annotation}, isbn = {978-2-9517408-9-1}, url = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/456_Paper.pdf}, author = {Johann Poignant and Mateusz Budnik and Herv{\'e} Bredin and Claude Barras and Mickael Stefas and Pierrick Bruneau and Gilles Adda and Laurent Besacier and Hazim Ekenel and Gil Francopoulo and Javier Hernando and Joseph Mariani and Morros, J.R. and Georges Qu{\'e}not and Sophie Rosset and Thomas Tamisier} } @mastersthesis {xFernandez, title = {Clustering and Prediction of Adjective-Noun Pairs for Affective Computing}, year = {2016}, abstract = {

Student: D{\`e}lia Fern{\`a}ndez

Advisors: V{\'\i}ctor Campos (UPC), Brendan Jou (Columbia University), Xavier Gir{\'o}-i-Nieto (UPC) and Shih-Fu Chang (Columbia University)

Grade: A+ (10.0/10.0) - Best Master Thesis award (Class 2016)

One of the main problems in visual affective computing is overcoming the affective gap between low-level visual features and the emotional content of the image. One rising method to capture visual affection is through the use of Adjective-Noun Pairs (ANP), a mid-level affect representation. This thesis addresses two challenges related to ANPs: representing ANPs in a structured ontology and improving ANP detectability. The first part develops two techniques to exploit relations between adjectives and nouns for automatic ANP clustering. The second part introduces and analyzes a novel deep neural network for ANP prediction. Based on the hypothesis of a different contribution of the adjective and the noun depending of the ANP, the novel network fuses the feature representations of adjectives and nouns from two independently trained convolutional neural networks.

}, author = {Fern{\`a}ndez, D{\`e}lia}, editor = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @mastersthesis {xPanizo15, title = {Classification techniques for Alzheimer{\textquoteright}s disease early diagnosis}, year = {2015}, abstract = {

Advisor: Ver{\'o}nica Vilaplana

Studies: Telecommunication Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Alzheimer{\textquoteright}s disease currently affects more than 36 million people in the world. A patient{\textquoteright}s brain suffers changes during the earliest stages of the disease and long before showing any clinical symptoms. For that reason, researchers focus their efforts towards defining which changes occur and where do they take place, with the goal of detecting indicators to predict the development of the disease. Specifically, the entity Fundaci{\'o}n Pascual Maragall para la investigaci{\'o}n contra el Alzheimer studies the processes of the brain all along the disease{\textquoteright}s stages using images obtained through different MRI techniques. The huge volume of data generated in this kind of investigation is a big obstacle to carry out analysis and extracting conclusions. The aim of this thesis is making this process easier by using data mining techniques. The goal is to develop a basic classification system to distinguish in which stage of the disease a patient is in, using data extracted from cerebral images. This system must form the basis for a future data mining system that satisfies the necessities of the Fundaci{\'o}n Pascual Maragall researchers. In addition to the classification system, this project focuses on distinguishing which is the most relevant data in the classification and on optimizing the classification in the pre-clinical stage of the disease.

}, author = {Eva Panizo}, editor = {Ver{\'o}nica Vilaplana} } @mastersthesis {xCabezas, title = {Co-filtering human interaction and object segmentation}, year = {2015}, abstract = {

Advisors: Axel Carlier and Vincent Charvillat (ENSEEIHT-Universit{\'e} de Toulouse) / Amaia Salvador and\ Xavier Gir{\'o}-i-Nieto\ (UPC)\ 

Degree: Audiovisual Engineering (4 years) at\ Telecom BCN-ETSETB\ (UPC)

Grade: A with honors (9.6/10)

For so many years the problem of object segmentation has been present in image processing field. Click{\textquoteright}n{\textquoteright}Cut, an already existing web tool for interactive object segmentation, helps us to obtain segmentations of the objects by clicking in green (foreground clicks) inside the object to segment, and in red(background clicks) outside the object to segment. However, the behaviour of all human in front of this web tool is not equal. For this reason, it can be possible that these human interactions do not help us to obtain a good object segmentation as would be the result of a bad human interaction. The main aim of this project is to implement some techniques that allow us to treat with these bad human interactions in order to obtain the best object segmentation possible.

Co-filtering human interaction and object segmentation from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/2099.1/25821}, author = {Cabezas, Ferran}, editor = {Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent} } @mastersthesis {xVela15, title = {Combinar im{\'a}genes y h{\'a}bitos musicales para mejorar los sistemas de recomendaci{\'o}n de m{\'u}sica}, year = {2015}, url = {http://hdl.handle.net/2099.1/26236}, author = {Enrique Sa{\~n}oso Vela}, editor = {Ver{\'o}nica Vilaplana and Josep Ramon Morros} } @conference {cSalvadora, title = {Cultural Event Recognition with Visual ConvNets and Temporal Models}, booktitle = {CVPR ChaLearn Looking at People Workshop 2015}, year = {2015}, month = {06/2015}, abstract = {

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the \ ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.

[Preprint in arXiv][Workshop site][Slides on GDrive]

Cultural Event Recognition with Visual ConvNets and Temporal Models from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W09/papers/Salvador_Cultural_Event_Recognition_2015_CVPR_paper.pdf}, author = {Amaia Salvador and Zeppelzauer, Matthias and Manchon-Vizuete, Daniel and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto} } @conference {cCarlier, title = {Click{\textquoteright}n{\textquoteright}Cut: Crowdsourced Interactive Segmentation with Object Candidates}, booktitle = {3rd International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM)}, year = {2014}, month = {11/2014}, address = {Orlando, Florida (USA)}, abstract = {

This paper introduces Click{\textquoteright}n{\textquoteright}Cut, a novel web tool for interactive object segmentation addressed to crowdsourcing tasks. Click{\textquoteright}n{\textquoteright}Cut combines bounding boxes and clicks generated by workers to obtain accurate object segmentations. These segmentations are created by combining precomputed object candidates in a light computational fashion that allows an immediate response from the interface. Click{\textquoteright}n{\textquoteright}Cut has been tested with a crowdsourcing campaign to annotate a subset of the Berkeley Segmentation Dataset (BSDS). Results show competitive results with state of the art, especially in time to converge to a high quality segmentation. The data collection campaign included golden standard tests to detect cheaters.

[Related master thesis by Amaia Salvador]

[Related Phd thesis by Axel Carlier]

[CrowdMM website]

}, keywords = {Crowdsourcing, figure-ground segmentation, human computing, object candidates}, doi = {10.1145/2660114.2660125}, url = {http://dx.doi.org/10.1145/2660114.2660125}, author = {Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Marques, Oge and Charvillat, Vincent} } @mastersthesis {xGutierrez14, title = {Comparaci{\'o} d{\textquoteright}algoritmes de classificaci{\'o} de tipus de pla en imatges de futbol}, year = {2014}, abstract = {

The purpose of this project is to analyze and evaluate football image viewpoint classification algorithms and to implement, if it is possible, a set of upgrades to improve the results of this classification. In particular, the analysis of a publication of the State of the Art and its subsequent comparison to an algorithm created by the Image Processing Group (GPI) of the Polytechnic University of Catalonia is formulated. Sometimes, during the analysis process of sporting events is interesting to automate the extraction of semantic content. In this context, the algorithms compared in this project are football image viewpoint classification algorithms. To carry through the classification, these algorithms use different descriptors calculated on the images. This project originates from the need to compare the image processing group algorithm to present techniques, in order to assess the obtained results and have a better understanding of the current State of the Art. Thus, the State of the Art algorithm will be implemented, analyzed and improved if possible. Then, it will be compared to the one created by the image processing group using an extensive database previously selected. Finally, the results will be presented and analyzed.

}, url = {http://upcommons.upc.edu/bitstream/handle/2099.1/20890/Final_Report.pdf?sequence=4\&isAllowed=y}, author = {Coronado Guti{\'e}rrez, D}, editor = {David Varas and Marqu{\'e}s, F.} } @mastersthesis {xTella, title = {Contextless Object Recognition with Shape-enriched SIFT and Bags of Features}, year = {2014}, abstract = {
Final thesis slides (1) from Xavi Gir{\'o}

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Matthias Zeppelzauer\ (TU Wien)

Degree: Telecommunications Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

Currently, there are highly competitive results in the field of object recognition based on the aggregation of point-based features. The aggregation process, typically with an average or max-pooling of the features generates a single vector that represents the image or region that contains the object.

The aggregated point-based features typically describe the texture around the points with descriptors such as SIFT. These descriptors present limitations for wired and textureless objects. A possible solution is the addition of shape-based information. Shape descriptors have been previously used to encode shape information and thus, recognise those types of objects. But generally an alignment step is required in order to match every point from one shape to other ones. The computational cost of the similarity assessment is high.

We purpose to enrich location and texture-based features with shape-based ones. Two main architectures are explored: On the one side, to enrich the SIFT descriptors with shape information before they are aggregated. On the other side, to create the standard Bag of Words histogram and concatenate a shape histogram, classifying them as a single vector.

We evaluate the proposed techniques and the novel features on the Caltech-101 dataset.

Results show that shape features increase the final performance. Our extension of the Bag of Words with a shape-based histogram(BoW+S) results in better performance. However, for a high number of shape features, BoW+S and enriched SIFT architectures tend to converge.

Final grade: A with honors (10/10)

}, keywords = {Bag of Words, Interest Points, object candidates, Object detection, segmentation, shape coding, SIFT, textureless objects, wired objects.}, url = {http://hdl.handle.net/2099.1/22390}, author = {Tella, Marcel}, editor = {Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xMartos13, title = {Content-based Video Summarisation to Object Maps}, year = {2013}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto\ and\ Horst Eidenberger

School: Vienna University of Technology (Austria)

The amount of digital video content available in the web is constantly increasing. Its handling requires efficient technologies: text search on large databases provides users a great amount of videos; the content results are accessible by a description. Users need a fast and visual way to access relevant video content effectively. Quick visualisation of content using static image summarisation is a sophisticated problem. However, it is worth it because it may solve video navigation problems. Users can very rapidly get an idea of the video with no need to browse through it with a sliding bar as normally done.

In this work a system for automatic video summarisation is developed. It creates an object map the segments of which are extracted from an input video. It allows enhancing video browsing and large video databases management generating a visual index so that the user can rapidly grasp the most relevant content. Finally, accessing them with a simple action requires several technologies that define a complex information processing.

Firstly, shot boundary detection algorithms are required to reduce time redundancy of the video. Secondly, different relevant objects are extracted from each keyframe (faces, cars, etc.). We also describe a workflow to train detection models using multiple open source solutions. Furthermore, faces are a particular and very relevant semantic class. For this reason, we use clustering methods in order to recognise them in an unsupervised recognition process. The image composition of all selected objects and faces is the final stage of the architecture. Composition is defined as the combination of distinct parts to form a whole, therefore, objects have to be rendered in the map in a visually attractive manner.

To validate our approach and assess end-user satisfaction, we conducted a user study in which we compare requirements collected by analysing related literature. We analyse redundancy and informativeness as well as pleasantness.

The results show that our approach effectively creates an image representation for videos and is able to summarise customisable content in an attractive way.


Manel Martos, "Content-based Video Summarization to Object Maps" from Image Processing Group on Vimeo.

}, url = {http://hdl.handle.net/2099.1/19359}, author = {Martos, Manel}, editor = {Xavier Gir{\'o}-i-Nieto and Eidenberger, Horst} } @conference {cSalvador13 , title = {Crowdsourced Object Segmentation with a Game}, booktitle = {ACM Workshop on Crowdsourcing for Multimedia (CrowdMM)}, year = {2013}, month = {10/2013}, address = {Barcelona}, abstract = {

We introduce a new algorithm for image segmentation based on crowdsourcing through a game : Ask{\textquoteright}nSeek. The game provides information on the objects of an image, under the form of clicks that are either on the object, or on the background. These logs are then used in order to determine the best segmentation for an object among a set of candidates generated by the state-of-the-art CPMC algorithm. We also introduce a simulator that allows the generation of game logs and therefore gives insight about the number of games needed on an image to perform acceptable segmentation.

Amaia Salvador, "Crowdsourced Object Segmentation with a Game" from Xavi Gir{\'o}
}, isbn = {978-1-4503-2396-3}, doi = {http://dx.doi.org/10.1145/2506364.2506367}, url = {http://dx.doi.org/10.1145/2506364.2506367}, author = {Amaia Salvador and Carlier, Axel and Xavier Gir{\'o}-i-Nieto and Marques, Oge and Charvillat, Vincent} } @mastersthesis {xSalvador13, title = {Crowdsourced Object Segmentation with a Game}, year = {2013}, abstract = {

Co-advised with Axel Carlier (INP Toulouse), Vincent Charvillat\ (INP Toulouse) and Oge Marques (Florida Atlantic University).

Amaia Salvador, "Crowdsourced Object Segmentation with a Game" from Image Processing Group on Vimeo.

}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Charvillat, Vincent and Marques, Oge} } @conference {cLopez-Mendez12, title = {Can our TV robustly understand human gestures? Real-Time Gesture Localization in Range Data}, booktitle = {Conference on Visual Media Production}, year = {2012}, month = {12/2012}, publisher = {ACM}, organization = {ACM}, address = {London, UK}, abstract = {

The {\textquoteright}old{\textquoteright} remote falls short of requirements when confronted with digital convergence for living room displays. Enriched options to watch, manage and interact with content on large displays demand improved means of interaction. Concurrently, gesture recognition is increasingly present in human-computer interaction for gaming applications. In this paper we propose a gesture localization framework for interactive display of audio-visual content. The proposed framework works with range data captured from a single consumer depth camera. We focus on still gestures because they are generally user friendly (users do not have to make complex and tiring movements) and allow formulating the problem in terms of object localization. Our method is based on random forests, which have shown an excellent performance on classification and regression tasks. In this work, however, we aim at a specific class of localization problems involving highly unbalanced data: positive examples appear during a small fraction of space and time. We study the impact of this natural unbalance on the random forest learning and we propose a framework to robustly detect gestures on range images in real applications. Our experiments with offline data show the effectiveness of our approach. We also present a real-time application where users can control the TV display with a reduced set of still gestures.

}, isbn = {978-1-4503-1311-7/12/11}, doi = {10.1145/2414688.2414691}, url = {http://www.cvmp-conference.org/2012-Papers}, author = {L{\'o}pez-M{\'e}ndez, A. and Casas, J.} } @conference {cSalvador11, title = {A compact 3D representation for multi-view video}, booktitle = {2011 International Conference on 3D Imaging}, year = {2011}, pages = {1{\textendash}8}, abstract = {

This paper presents a methodology for obtaining a 3D reconstruction of a dynamic scene in multi-camera settings. Our target is to derive a compact representation of the 3D scene which is effective and accurate, whatever the number of cameras and even for very-wide baseline settings. Easing realtime 3D scene capture has outstanding applications in 2D and 3D content production, free viewpoint video of natural scenes and interactive video applications.\ 

The method proposed here has several original contributions on how to accelerate the process: it exploits spatial and temporal consistency for speeding up reconstruction, dividing the problem in two parts. First, 3D surfaces are efficiently sampled to obtain a silhouette-consistent set of colored surface points and normals, using a novel algorithm presented in this paper. Then, a fast, greedy meshing algorithm retrieves topologically correct continuous surfaces from the dense sets of oriented points, providing a suitable representation for multi-view video.\ 

Compared to other techniques in the literature, the presented approach is capable of retrieving 3D surfaces of foreground objects in real-time by exploiting the computing capabilities of GPUs. This is feasible due to the parallelized design of the surface sampling algorithm. The reconstructed surfaces can effectively be used for interactive representations.\ 

The presented methodology also offers good scalability to large multi-view video settings.

}, isbn = {978-1-61284-349-0}, doi = {10.1109/IC3D.2011.6584371}, url = {http://www.3dstereomedia.eu}, author = {Salvador, J. and Casas, J.} } @conference {cAlcoverro11a, title = {Connected Operators on 3D data for human body analysis}, booktitle = {2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2011}, pages = {9{\textendash}14}, abstract = {

This paper presents a novel method for filtering and extraction of human body features from 3D data, either from multi-view images or range sensors. The proposed algorithm consists in processing the geodesic distances on a 3D surface representing the human body in order to find prominent maxima representing salient points of the human body. We introduce a 3D surface graph representation and filtering strategies to enhance robustness to noise and artifacts present in this kind of data. We conduct several experiments on different datasets involving 2 multi-view setups and 2 range data sensors: Kinect and Mesa SR4000. In all of them, the proposed algorithm shows a promising performance towards human body analysis with 3D data.

}, isbn = {978-1-4577-0529-8}, doi = {10.1109/CVPRW.2011.5981772}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5981772}, author = {Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and M. Pard{\`a}s and Casas, J.} } @article {aTenorio11, title = {Correlation between a semiautomated method based on ultrasound texture analysis and standard ultrasound diagnosis using white matter damage in preterm neonates as a model}, journal = {Journal of ultrasound in medicine}, volume = {30}, number = {10}, year = {2011}, pages = {1365{\textendash}1377}, issn = {0278-4297}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21968487}, author = {Tenorio, V. and Bonet, E. and Botet, F. and Marqu{\'e}s, F. and Amat, I. and Gratacos, E.} } @conference {cValero10a, title = {Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data}, booktitle = {IEEE International Conference on Image Processing, ICIP 2010}, year = {2010}, pages = {2565{\textendash}2568}, address = {Hong Kong, China}, doi = {10.1109/ICIP.2010.5652595}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5652595}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @inbook {bMarcotegui10, title = {Compression}, booktitle = {Mathematical morphology from theories to applications}, year = {2010}, pages = {385{\textendash}391}, publisher = {Wiley}, organization = {Wiley}, edition = {L. Najman and H. Talbot (Eds.)}, chapter = {16}, isbn = {978-1848212152}, url = {http://cataleg.upc.edu/record=b1376601~S1*cat}, author = {Marcotegui, B. and Salembier, P.} } @inbook {bMarcotegui10a, title = {Compression}, booktitle = {Morphologie Math{\'e}matique II: estimation choix et mise en oeuvre}, year = {2010}, publisher = {Hermes, Lavoisier}, organization = {Hermes, Lavoisier}, edition = {L. Najman and H. Talbot (Eds.)}, isbn = {978-2-7462-2593-0}, url = {http://cataleg.upc.edu/search~S1*cat/X?estimation\%2C+choix\&SORT=D\&searchscope=1}, author = {Marcotegui, B. and Salembier, P.} } @inbook {bWaibel10, title = {Computers in the Human Interaction Loop}, booktitle = {Handbook on Ambient Intelligence and Smart Environments (AISE)}, year = {2010}, pages = {1071{\textendash}1116}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA}, abstract = {

It is a common experience in our modern world, for us humans to be overwhelmed by the complexities of technological artifacts around us, and by the attention they demand. While technology provides wonderful support and helpful assistance, it also causes an increased preoccupation with technology itself and a related fragmentation of attention. But as humans, we would rather attend to a meaningful dialog and interaction with other humans, than to control the operations of machines that serve us. The cause for such complexity and distraction, however, is a natural consequence of the flexibility and choice of functions and features that technology has to offer. Thus flexibility of choice and the availability of desirable functions are in conflict with ease of use and our very ability to enjoy their benefits.\ 

}, isbn = {978-0-387-93807-3}, doi = {10.1007/978-0-387-93808-0_40}, author = {Waibel, A. and Stiefelhagen, R. and Carlson, R. and Casas, J. and Kleindienst, J. and Lamel, L. and Lanz, O. and Mostefa, D. and Omologo, M. and Pianesi, F. and Polymenakos, L. and Potamianos, G. and Soldatos, J. and Sutschet, G. and Terken, J.} } @inbook {bSalembier10, title = {Connected operators based on tree pruning strategies}, booktitle = {Mathematical morphology from theories to applications}, year = {2010}, publisher = {Wiley}, organization = {Wiley}, edition = {L. Najman and H. Talbot (Eds.)}, chapter = {7}, isbn = {978-1848212152}, url = {http://cataleg.upc.edu/record=b1376601~S1*cat}, author = {Salembier, P.} } @conference {cPont-Tuset10, title = {Contour detection using binary partition trees}, booktitle = {IEEE International Conference on Image Processing}, year = {2010}, pages = {1609{\textendash}1612}, isbn = {?}, doi = {10.1109/ICIP.2010.5652339}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=\&arnumber=5652339\&queryText\%3DContour+detection+using+binary+partition+trees\%26openedRefinements\%3D*\%26searchField\%3DSearch+All}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @conference {cLeon09, title = {Caption text extraction for indexing purposes using a hierarchical region-based image model}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {1869{\textendash}1872}, isbn = {978-1-4244-5655-0}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5413607}, url = {http://hdl.handle.net/2117/7940}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @conference {cMarcello09, title = {Cloud motion estimation in seviri image sequences}, booktitle = {2009 IEEE International Geoscience and Remote Sensing Symposium}, year = {2009}, pages = {642{\textendash}645}, isbn = {978-1-4244-3394-0}, doi = {10.1109/IGARSS.2009.5417842}, url = {http://hdl.handle.net/2117/9492}, author = {Marcello, J. and F. Eugenio and Marqu{\'e}s, F.} } @conference {cRuiz-Hidalgo09, title = {Comparison of MPEG-7 descriptors for long term selection of reference frames}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009}, year = {2009}, pages = {941{\textendash}944}, address = {Taipei, Taiwan}, isbn = {0-8194-2103-0}, doi = {10.1109/ICASSP.2009.4959740}, url = {http://hdl.handle.net/2117/8816}, author = {Ruiz-Hidalgo, J. and Salembier, P.} } @inbook {bVilaplana09, title = {Compressi{\'o} d{\textquoteright}imatges}, booktitle = {Codificaci{\'o} del so i de la imatge}, year = {2009}, pages = {1{\textendash}65}, isbn = {9788469148365}, url = {http://ccuc.cbuc.cat:2082/record=b4149618~S23*cat}, author = {Ver{\'o}nica Vilaplana} } @article {aSalembier09, title = {Connected operators: A review of region-based morphological image processing techniques}, journal = {IEEE Signal Processing Magazine}, volume = {6}, year = {2009}, pages = {136{\textendash}157}, issn = {1053-5888}, doi = {10.1109/MSP.2009.934154}, author = {Salembier, P. and Wilkinson, M.H.F.} } @conference {cSalembier08, title = {Connected operators based on region-trees}, booktitle = {IEEE International Conference on Image Processing, ICIP 2008}, year = {2008}, address = {San Diego, USA}, author = {Salembier, P.} } @article {aMostefa07, title = {The CHIL Audiovisual Corpus for Lecture and Meeting Analysis inside Smart Rooms}, journal = {Language resources and evaluation}, volume = {41}, number = {3}, year = {2007}, month = {01/2008}, pages = {389{\textendash}407}, abstract = {

The analysis of lectures and meetings inside smart rooms has recently attracted much interest in the literature, being the focus of international projects and technology evaluations. A key enabler for progress in this area is the availability of appropriate multimodal and multi-sensory corpora, annotated with rich human activity information during lectures and meetings. This paper is devoted to exactly such a corpus, developed in the framework of the European project CHIL, {\textquotedblleft}Computers in the Human Interaction Loop{\textquotedblright}. The resulting data set has the potential to drastically advance the state-of-the-art, by providing numerous synchronized audio and video streams of real lectures and meetings, captured in multiple recording sites over the past 4 years. It particularly overcomes typical shortcomings of other existing databases that may contain limited sensory or monomodal data, exhibit constrained human behavior and interaction patterns, or lack data variability. The CHIL corpus is accompanied by rich manual annotations of both its audio and visual modalities. These provide a detailed multi-channel verbatim orthographic transcription that includes speaker turns and identities, acoustic condition information, and named entities, as well as video labels in multiple camera views that provide multi-person 3D head and 2D facial feature location information. Over the past 3 years, the corpus has been crucial to the evaluation of a multitude of audiovisual perception technologies for human activity analysis in lecture and meeting scenarios, demonstrating its utility during internal evaluations of the CHIL consortium, as well as at the recent international CLEAR and Rich Transcription evaluations. The CHIL corpus is publicly available to the research community

}, issn = {1574-020X}, doi = {10.1007/s10579-007-9054-4}, author = {Mostefa, D. and Moreau, N. and Choukri, K. and Potamianos, G. and Chu, S. and Tyagi, A. and Casas, J. and Turmo, J. and Cristoforetti, L. and Tobia, F. and Pnevmatikakis, A. and Mylonakis, V. and Talantzis, F. and Burger, S. and Stiefelhagen, R. and Bernardin, K. and Rochet, C.} } @conference {cGiro-i-Nieto07, title = {Composite object detection in video sequences: Applications to controlled environments}, booktitle = {8th International Workshop on Image Analysis for Multimedia Interactive Services}, year = {2007}, month = {06/2007}, publisher = {IEEE}, organization = {IEEE}, address = {Santorini, Greece}, abstract = {

This paper presents a set of techniques for the detection of composite objects in video recordings of a controlled environ- ment. Firstly, a selective region-based analysis is performed by tuning the algorithm to the perceptual characteristics of the object in the environment. Secondly, the controlled per- ceptual and semantic variabilities of the object are addressed by the detection analysis thanks to a frame by frame update of the object models, and by allowing multiple models for a single object. The proposed techniques are illustrated in the detection of laptops from a zenithal view in a smart room.\ 

}, keywords = {image region analysis, image sequence analysis, Object detection, object recognition, pattern recognition}, isbn = {0-7695-2818-X}, doi = {10.1109/WIAMIS.2007.31}, url = {http://dx.doi.org/10.1109/WIAMIS.2007.31}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @inbook {bCasas07, title = {Context Awareness triggered by Multiple Perceptual Analyzers}, booktitle = {Emerging Artificial Intelligence Applications in Computer Engineering}, volume = {160}, year = {2007}, pages = {371{\textendash}383}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, abstract = {

A multitude of technologies from computer vision, acoustic signal analysis and natural language processing are used to implement multi-modal perceptual components. The output of this analysis is used to gain context awareness {\textendash} a necessity when designing a computer-based service that interacts reactively and proactively with humans. This article describes the integration process and our experience in implementing one such information service, the {\textquotedblleft}Memory Jog{\textquotedblright}, in a particular scenario where the computer system supports a group of journalists in their daily work.

}, isbn = {978-1-58603-780-2}, url = {http://www.booksonline.iospress.nl/Content/View.aspx?piid=6976}, author = {Casas, J. and Neumann, J.} } @conference {cSole06, title = {A Common Formulation for Interpolation, Prediction, and Update Lifting Design,}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2006}, year = {2006}, pages = {1{\textendash}4}, address = {Toulouse, France}, isbn = {978-83-921340-2-2}, author = {Sol{\'e}, J. and Salembier, P.} } @conference {cLandabaso06, title = {Cooperative background modelling using multiple cameras towards human detection in smart-room}, booktitle = {14th European Signal Processing Conference}, year = {2006}, pages = {1{\textendash}5}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {cAnton05, title = {Collaborative Network Space: Infrastructure and Learning Application}, booktitle = {IEEE Region 8 EUROCON 2005 Conference: Computer as a tool.}, year = {2005}, pages = {803{\textendash}806}, isbn = {1-4244-0050-3}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01630054}, author = {Anton, M. and Vall-Llosera, M. and Jordi Torres and Romeu, J. and Jofre, L. and Sole, F. and Marqu{\'e}s, F. and Sabate, F. and Berenguer, J.} } @conference {cVidal02, title = {Color Image Watermarking Using Channel-State Knowledge}, booktitle = {SPIE Electronic Imaging 2002}, year = {2002}, pages = {214{\textendash}221}, isbn = {0-8194-4415-4}, author = {Vidal, J. and Maribel, M. and Elisa Sayrol} } @conference {cSalembier02a, title = {Connected Operators Based on Reconstruction Process for Size and Motion Simplification}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2002}, year = {2002}, month = {05/2002}, address = {Orlando, USA}, author = {Salembier, P. and Ruiz-Hidalgo, J.} } @conference {cEugenio02, title = {A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery}, booktitle = {International Geoscience and Remote Sensing Symposium, 2002. IGARSS {\textquoteright}02. 2002 IEEE}, year = {2002}, pages = {1{\textendash}3}, isbn = {0-7803-7536-0}, author = {F. Eugenio and Marqu{\'e}s, F. and Marcello, J.} } @conference {cSayrol01, title = {Color Inititialization for Lip Tracking}, booktitle = {International Conference on Augmented, Virtual Environments and 3D Imaging}, year = {2001}, pages = {351{\textendash}354}, author = {Elisa Sayrol and Fischi, O. and M. Pard{\`a}s} } @inbook {bMarques00, title = {CODING-ORIENTED SEGMENTATION OF VIDEO SEQUENCES}, booktitle = {Video coding: the second generation approach}, year = {2000}, pages = {79{\textendash}124}, isbn = {079239680}, author = {Marqu{\'e}s, F.} } @conference {cSalembier00a, title = {Connected operators based on region-tree pruning}, booktitle = {Mathematical Morphology and its application to signal processing, ISMM 200}, year = {2000}, pages = {169{\textendash}184}, address = {Palo Alto, USA}, isbn = {0-7803-8555-1}, author = {Salembier, P. and Garrido, L.} } @conference {cSalembier00c, title = {Connected operators based on region-tree pruning strategies}, booktitle = {15th IAPR International Conference on Pattern Recognition, ICPR 2000}, year = {2000}, pages = {371{\textendash}374}, address = {Barcelona, Spain}, isbn = {?}, author = {Salembier, P. and Garrido, L.} } @conference {cEugenio00, title = {A contour matching approach for accurate NOAA-AVHRR image navigation}, booktitle = {10th European Signal Processing Conference (EUSIPCO 2000)}, year = {2000}, isbn = {952-15-0447-1}, author = {F. Eugenio and Marqu{\'e}s, F. and Suarez, E. and Rovaris, E.} } @article {aNunes00, title = {A contour-based approach to binary shape coding using a multiple grid chain code}, journal = {Signal processing: image communication}, volume = {15}, number = {7-8}, year = {2000}, pages = {585{\textendash}599}, issn = {0923-5965}, author = {Nunes, P. and Marqu{\'e}s, F. and Pereira, F. and Gasull, A.} } @conference {cSalembier00, title = {Creation of visual segment trees for MPEG-7 Description Schemes}, booktitle = {International Symposium on Image / video Communications over Fixed and Mobile Networks, ISIVC{\textquoteright}2000}, year = {2000}, pages = {127{\textendash}134}, address = {Rabat, Morocco}, isbn = {?}, author = {Salembier, P. and Llach, J. and Garrido, L.} } @inbook {bTorres98, title = {Codificaci{\'o}n de imagen}, booktitle = {Reconocimiento de formas y an{\'a}lisis de im{\'a}genes}, year = {1998}, pages = {101{\textendash}135}, publisher = {AERFAI}, organization = {AERFAI}, edition = {A. Sanfeliu (Ed.)}, isbn = {84-922529-4-4}, author = {Torres, L. and Salembier, P.} } @conference {cSalembier98a, title = {Connected operators for sprite creation and layered representation of image sequences}, booktitle = {9th European Signal Processing Conference, EUSIPCO 1998}, year = {1998}, pages = {2105{\textendash}2108}, address = {Rhodes, Greece}, isbn = {978-1-4503-0159-6}, author = {Salembier, P. and Pujol, O. and Garrido, L.} } @phdthesis {dOliveras97, title = {Contribuci{\'o} a l{\textquoteright}an{\`a}lisi morfol{\`o}gica d{\textquoteright}imatges amb operadors connexes}, year = {1997}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Albert Oliveras}, editor = {Salembier, P.} } @inbook {bSalembier96, title = {Coding of partition sequences}, booktitle = {Video coding: the second generation approach}, year = {1996}, pages = {125{\textendash}170}, publisher = {Kluwer}, organization = {Kluwer}, edition = {L. Torres and M. Kunt (Eds.)}, isbn = {0 7923 9680 4}, author = {Salembier, P. and Marqu{\'e}s, F. and Gasull, A.} } @inbook {bMarques96, title = {Coding-oriented segmentation of video sequences}, booktitle = {Video coding: the second generation approach}, year = {1996}, pages = {79{\textendash}124}, publisher = {Kluwer}, organization = {Kluwer}, edition = {L. Torres and M. Kunt (Eds.)}, isbn = {0 7923 9680 4}, author = {Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P.} } @conference {cMarques95, title = {Coding of image partitions by morphological skeleton using overlapping structuring elements}, booktitle = {IEEE WORKSHOP ON NONLINEAR SIGNAL AND IMAGE PROCESSING}, year = {1995}, pages = {250{\textendash}253}, author = {Marqu{\'e}s, F. and Fioravanti, S. and Brigger, P.} } @article {aCasas94, title = {Coding of details in very low bit-rate video systems}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {4}, number = {3}, year = {1994}, month = {06/1994}, pages = {317{\textendash}327}, abstract = {

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept of {\textquotedblleft}partition{\textquotedblright} in the segmentation scheme. As long as details are not considered as adjacent regions but isolated features spread over the image, {\textquotedblleft}detail coding{\textquotedblright} can be seen as one step towards the so called feature-based video coding techniques

}, keywords = {CHIL}, issn = {1051-8215}, doi = {10.1109/76.305876}, url = {http://hdl.handle.net/2117/97643}, author = {Casas, J. and Torres, L.} } @conference {cCasas94a, title = {Coding of significant features in very low bit-rate video systems}, booktitle = {SPIE{\textquoteright}S Visual Communications{\textquoteright}94}, year = {1994}, pages = {73{\textendash}85}, author = {Casas, J. and Torres, L.} } @conference {cGasull93, title = {Character recognition and document analysis by morphological techniq}, booktitle = {MATHEMATICAL MORPHOLOGY}, year = {1993}, pages = {198{\textendash}203}, author = {Gasull, A. and Corbera, L. and Marqu{\'e}s, F.} } @conference {cMontolio93, title = {Character recognition and document analysis by morphological techniques}, booktitle = {WORKSHOP ON MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO SIGNAL PROCESSING}, year = {1993}, pages = {198{\textendash}203}, author = {Montolio, P. and Gasull, A. and Corbera, L. and Marqu{\'e}s, F.} } @conference {cCasas93, title = {Codificacion run-length de imagenes de detalle}, booktitle = {VIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1993}, pages = {349{\textendash}399}, author = {Casas, J.} } @conference {cSerra93, title = {Connected operators and pyramids}, booktitle = {SPIE Image Algebra and Mathematical Morphology}, year = {1993}, pages = {65{\textendash}76}, address = {San Diego (CA), USA}, isbn = {1522-4880}, author = {Serra, J. and Salembier, P.} } @conference {cMarques93d, title = {Cuantificacion de lesiones de columna vertebral a partir de imagenes}, booktitle = {VIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1993}, pages = {433{\textendash}437}, author = {Marqu{\'e}s, F.} } @conference {cMarques93e, title = {Cuantificador vectorial con clasificador difuso para la codificacion}, booktitle = {VIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1993}, pages = {369{\textendash}373}, author = {Marqu{\'e}s, F.} } @conference {cMarques92, title = {Classified vector quantization with fuzzy theory}, booktitle = {INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS}, year = {1992}, pages = {237{\textendash}244}, isbn = {0-7803-3192-3}, author = {Marqu{\'e}s, F.} } @conference {cGasull92c, title = {Coagulation time detection by means of a real-time image processing}, booktitle = {14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {1992}, pages = {1948{\textendash}1949}, author = {Gasull, A. and Vallverdu, F. and Marqu{\'e}s, F.} } @conference {cMarques92b, title = {Codificacion de imagenes: un metodo de segunda generacion}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {144{\textendash}148}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cMarques92d, title = {Codificacion de imagenes:un metodo de segunda generacion}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {144{\textendash}148}, author = {Marqu{\'e}s, F. and Gasull, A.} } @article {aTorres92, title = {Compresi{\'o}n de audio e imagen para sistemas multimedia}, journal = {Bit numerical mathematics}, number = {76}, year = {1992}, pages = {50{\textendash}58}, issn = {0006-3835}, author = {Torres, L. and Moreno, A. and Masgrau, E. and Gasull, A.} } @conference {cPardas92, title = {Connectivity filters for image sequences}, booktitle = {IMAGE ALGEBRA AND MORPHOLOGICAL IMAGE PROCESSING. SPIE}, year = {1992}, pages = {318{\textendash}329}, isbn = {0 7923 5133 9}, author = {M. Pard{\`a}s and Torres, L.} } @conference {cMarques91, title = {Codig-oriented segmentation based on g-m random}, booktitle = {International Conference on Acoustics, Speech and Signal Processing 1991}, year = {1991}, pages = {2749{\textendash}2752}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cGasull90, title = {Contour extraction and image preprocessing of echocardiographic images using r}, booktitle = {Latvian Signal Processing International Conference}, year = {1990}, pages = {26{\textendash}30}, author = {Gasull, A. and Torres, L.} } @conference {cVazquez89, title = {Constant variance transversal filtering for adaptive channel equalization}, booktitle = {INTERNATIONAL CONFERENCE ON SONAR SIGNAL PROCESSING}, year = {1989}, pages = {212{\textendash}215}, isbn = {0-7923-9733-9}, author = {Vazquez, G. and Gasull, A. and Sanchez, J. and Lagunas, M.} } @conference {cSallent88, title = {Codificaci{\'o}n piramidal generalizada}, booktitle = {III Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1988}, pages = {1{\textendash}2}, isbn = {142440469X/1520-6149}, author = {Sallent, S. and Torres, L. and Gasull, A.} }