@phdthesis {dPina25, title = {From pixels to graphs: cell-based digital pathology image analysis}, volume = {PhD}, year = {2025}, month = {06/2025}, type = {PhD Thesis}, abstract = {

Histopathology is the gold standard for cancer diagnosis, where pathologists examine tissue samples to assess cellular morphology, tissue architecture, and biomarker expression. The digitization of histological slides has enabled the application of artificial intelligence to assist in this process. However, deploying deep learning models in pathology remains challenging due to the gigapixel-scale nature of whole slide images, variability introduced by staining protocols and tissue morphology, and the scarcity of high-quality annotations. This thesis focuses on the development of deep learning methodologies for the identification and characterization of cells in histopathological slides, addressing these core challenges. Inspired by the way pathologists assess slides, the proposed methods target both individual cells and their spatial organization within the tissue. A transformer-based model, CellNuc-DETR, is introduced for efficient cell nuclei detection and classification, achieving state-of-the-art performance while significantly reducing inference time. To address annotation scarcity in immunohistochemistry, the model is adapted using unsupervised domain adaptation techniques, enabling knowledge transfer from hematoxylin and eosin-stained slides without requiring annotated immunohistochemistry data. To incorporate broader spatial context into cell classification, the thesis explores graph-based representations of histological tissue. It proposes a novel graph construction strategy that integrates both cell-level and patch-level nodes, enabling the fusion of cellular features with visual and contextual information in a scalable manner. To support annotation-efficient learning, the thesis introduces Regularized Graph Infomax, a self-supervised learning algorithm designed for representation learning on graphs without manual annotations. This algorithm is then applied to the proposed cell{\textendash}patch graphs to enable efficient, large-context cell classification with minimal supervision. Finally, the thesis demonstrates the clinical relevance of these methods in a study on endometrial carcinoma. A weakly supervised whole slide classifier is trained to predict molecular subtypes, and the developed cell detection and classification tools are used to extract cell-level biomarkers from predicted regions of interest. These biomarkers enable the quantification of subtype-specific cell spatial organization differences, linking computational predictions to interpretable biological features. By integrating transformers, graph-based representations, and self-supervised learning, this thesis introduces scalable and annotation-efficient methodologies for computational pathology. These approaches enhance the efficiency, robustness, and generalization of artificial intelligence models, contributing to the development of clinically relevant tools for cancer diagnosis and characterization.

}, doi = {10.5821/dissertation-2117-439958}, url = {http://hdl.handle.net/10803/695086}, author = {Oscar Pina}, editor = {Ver{\'o}nica Vilaplana} } @conference {cReixach, title = {Fast unsupervised tensor restoration via low-rank deconvolution}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Abu Dhabi}, abstract = {

Low-rank Deconvolution (LRD) has appeared as a new multi-dimensional representation model that enjoys important efficiency and flexibility properties. In this work we ask ourselves if this analytical model can compete against Deep Learning (DL) frameworks like Deep Image Prior (DIP) or Blind-Spot Networks (BSN) and other classical methods in the task of signal restoration. More specifically, we propose to extend LRD with differential regularization. This approach allows us to easily incorporate Total Variation (TV) and integral priors to the formulation leading to considerable performance tested on signal restoration tasks such image denoising and video enhancement, and at the same time benefiting from its small computational cost.

}, keywords = {De-noising, Enhancement, Restoration, Tensors, Total Variation}, doi = {https://doi.org/10.1109/ICIP51287.2024.10647407}, url = {https://arxiv.org/abs/2406.10679}, author = {Reixach, David and Morros, J.R.} } @article {aPina, title = {Feature propagation as self-supervision signals on graphs}, journal = {Knowledge-Based Systems}, volume = {289}, year = {2024}, month = {04/2024}, doi = {https://doi.org/10.1016/j.knosys.2024.111512}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @phdthesis {dRamon22, title = {Few-shot 3D Reconstruction of Body Parts with Deep Neural Networks}, volume = {Excellent}, year = {2022}, month = {09/2022}, type = {Industrial}, abstract = {

In this thesis, we study the problem of reconstructing objects from a concrete category\ in 3D when few images are available as input, i.e. less than 10. We apply our findings to digitalizing human body parts such as heads and torsos for medical applications. The first part of the thesis explores systems that rely on 3D Morphable Models. When approaching a concrete task, training such systems requires expensive manual hyper-parameter tuning of both the architecture and the loss, which is time consuming. We focus on designing novel losses without hyperparameters and modular architectures that allow to train models without tuning efforts. We also aim at providing a fine alignment between the 3D space and the image space by estimating camera poses with a low re-projection error that further improve the texturing process in 3D modelling applications or the rendering process in augmented reality applications. Our findings lead to systems that are very stable and that naturally scale to different scenes.

While 3D Morphable Models are fast and robust, they are still very limited in terms\ of accuracy and expressiveness, which might be prohibitive for applications that require high fidelity. A promising alternative to 3D Morphable Models are implicit functions,which in combination with differentiable rendering techniques have shown impressive results at reconstructing 3D surfaces. However, the later require large sets of images at test time to obtain satisfactory results. In the second part of the thesis, we propose to use a probabilistic model that represents a distribution of implicit surfaces in combination with a differentiable renderer to reduce the number of images required at test time. The resulting 3D reconstruction system is highly accurate and allows to reconstruct a wide variety of human head shapes when only 3 images are available.

}, author = {Ramon, Eduard}, editor = {Moreno, Francesc and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto} } @article {aGene-Molac, title = {Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry}, journal = {Computers and Electronics in Agriculture}, volume = {169}, year = {2020}, month = {02/2020}, abstract = {

The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the efficiency of agriculture management. Most of the current fruit detection systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location consisting of: (1) 2D fruit detection and segmentation using Mask R-CNN instance segmentation neural network; (2) 3D point cloud generation of detected apples using structure-from-motion (SfM) photogrammetry; (3) projection of 2D image detections onto 3D space; (4) false positives removal using a trained support vector machine. This methodology was tested on 11 Fuji apple trees containing a total of 1455 apples. Results showed that, by combining instance segmentation with SfM the system performance increased from an F1-score of 0.816 (2D fruit detection) to 0.881 (3D fruit detection and location) with respect to the total amount of fruits. The main advantages of this methodology are the reduced number of false positives and the higher detection rate, while the main disadvantage is the high processing time required for SfM, which makes it presently unsuitable for real-time work. From these results, it can be concluded that the combination of instance segmentation and SfM provides high performance fruit detection with high 3D data precision. The dataset has been made publicly available and an interactive visualization of fruit detection results is accessible at http://www.grap.udl.cat/documents/photogrammetry_fruit_detection.html

}, keywords = {Fruit detection, Fruit location, Mask R-CNN, Structure-from-motion, Terrestrial remote sensing}, issn = {ISSN: 0168-1699}, doi = {https://doi.org/10.1016/j.compag.2019.105165}, url = {https://doi.org/10.1016/j.compag.2019.105165}, author = {Gen{\'e}-Mola, Jordi and Sanz, Ricardo and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Gregorio, Eduard} } @article {aRey-Arena20, title = {FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations}, journal = {Information Fusion}, volume = {63}, year = {2020}, month = {10/2020}, chapter = {188}, abstract = {

It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This issue is often alleviated using transfer learning, regularization techniques and/or data augmentation. This work presents a new approach, independent but complementary to the previous mentioned techniques, for improving the generalization of DNNs on very small datasets in which the involved classes share many visual features. The proposed model, called FuCiTNet (Fusion Class inherent Transformations Network), inspired by GANs, creates as many generators as classes in the problem. Each generator, k, learns the transformations that bring the input image into the k-class domain. We introduce a classification loss in the generators to drive the leaning of specific k-class transformations. Our experiments demonstrate that the proposed transformations improve the generalization of the classification model in three diverse datasets.

}, doi = {10.1016/j.inffus.2020.06.015}, author = {Rey-Arena, M. and Guirado, E. and Tabik, S. and Ruiz-Hidalgo, J.} } @article {aGene-Mola20, title = {Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry}, volume = {Data in Brief}, year = {2020}, month = {06/2020}, keywords = {Fruit detection, Mask R-CNN, Photogrammetry, Structure-from-motion, Terrestrial remote sensing, Yield mapping, Yield prediction}, doi = {https://doi.org/10.1016/j.dib.2020.105591}, author = {Gen{\'e}-Mola, Jordi and Sanz, Ricardo and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Gregorio, Eduard} } @article {aGene-Molab, title = {Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner}, journal = {Biosystems Engineering}, volume = {187}, year = {2019}, month = {09/2019}, chapter = {171}, abstract = {

The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimized harvesting practices. Most proposed systems for fruit detection are based on RGB cameras and thus are affected by intrinsic constraints, such as variable lighting conditions. This work presents a new technique that uses a mobile terrestrial laser scanner (MTLS) to detect and localise Fuji apples. An experimental test focused on Fuji apple trees (Malus domestica Borkh. cv. Fuji) was carried out. A 3D point cloud of the scene was generated using an MTLS composed of a Velodyne VLP-16 LiDAR sensor synchronized with an RTK-GNSS satellite navigation receiver. A reflectance analysis of tree elements was performed, obtaining mean apparent reflectance values of 28.9\%, 29.1\%, and 44.3\% for leaves, branches and trunks, and apples, respectively. These results suggest that the apparent reflectance parameter (at 905 nm wavelength) can be useful to detect apples. For that purpose, a four-step fruit detection algorithm was developed. By applying this algorithm, a localization success of 87.5\%, an identification success of 82.4\%, and an F1-score of 0.858 were obtained in relation to the total amount of fruits. These detection rates are similar to those obtained by RGB-based systems, but with the additional advantages of providing direct 3D fruit location information, which is not affected by sunlight variations. From the experimental results, it can be concluded that LiDAR-based technology and, particularly, its reflectance information, has potential for remote apple detection and 3D location.

}, issn = {1537-5110}, doi = {10.1016/j.biosystemseng.2019.08.017}, url = {https://authors.elsevier.com/c/1Zmc45Tbkk9EHW}, author = {Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat Cheein, Fernando and Sanz, Ricardo and Escol{\`a}, Alexandre and Llorens Calveras, Jordi and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R.} } @conference {cGene-Mola18, title = {Fruit Detection Using Mobile Terrestrial Laser Scanning}, booktitle = {AgEng 2018,}, year = {2018}, month = {07/2018}, address = {Wageningen (Netherlands)}, abstract = {

The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. Up to date, most proposed systems on fruit detection and characterization are based on RGB cameras and thus affected by intrinsic constraints, such as variable lighting conditions and camera calibration. This work presents a new technique that uses a mobile terrestrial laser scanner to detect and localize fruits regardless of the prevailing lighting conditions and without the need of a previous calibration. An experimental test focused on two Fuji apple trees (containing 139 and 145 apples each) was carried out. A 3D point cloud of this scene was generated using a Velodyne VLP-16 LiDAR sensor synchronized with a RTK-GNSS receiver. A reflectivity analysis of tree elements was performed, obtaining mean reflectivity values of 28.9\%, 29.1\%, and 44.3\% for leaves, trunks, and fruits, respectively. These results suggest that the reflectivity parameter can be useful to localize fruits in the tree. From this knowledge, a three-step fruit detection algorithm has been developed: 1) reflectivity thresholding to remove most of the leaves and trunks from the original point cloud; 2) statistical outlier removal to reduce noise; 3) connected components clustering using a density-based algorithm. By applying this algorithm to our dataset, a localization success of 85\%, a detachment success of 78.8\%, and a false detection rate of 15.2\% were obtained. These detection rates are similar to those obtained by current RGB-based system, but with the additional advantage of providing direct 3D fruit location information (global coordinates) which is not affected by sunlight variations. It can be concluded that LiDAR technology and, particularly, its reflectivity information, might have potential use in fruit detection. Future work should include the application of this fruit detection technique on a wider range of crop types

}, author = {Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat, Fernando and Escol{\`a}, Alexandre and Morros, J.R. and Rosell-Polo, Joan R.} } @unpublished {xMohedanoa, title = {Fine-tuning of CNN models for Instance Search with Pseudo-Relevance Feedback}, year = {2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

CNN classification models trained on millions of labeled images have been proven to encode {\textquotedblleft}general purpose{\textquotedblright} descriptors in their intermediate layers. These descriptors are useful for a diverse range of computer vision problems~\cite{1}. However, the target task of these models is substantially different to the instance search task. While classification is concerned with distinguishing between different classes, instance search is concerned with identifying concrete instances of a particular class.\ 

In this work we propose an unsupervised approach to finetune a model for similarity learning~\cite{2}. For that, we combine two different search engines: one based on off-the-shelf CNN features, and another one on the popular SIFT features. As shown in the figure below, we observe that the information of pre-trained CNN representations and SIFT is in most of the cases complementary, which allows the generation of high quality rank lists. The fusion of the two rankings is used to generate training data for a particular dataset. A pseudo-relevance feedback strategy~\cite{3} is used for sampling images from rankings, considering the top images as positive examples of a particular instance and middle-low ranked images as negative examples.

}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @article {xCampos, title = {From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction}, journal = {Image and Vision Computing}, year = {2017}, abstract = {

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.

}, doi = {http://dx.doi.org/10.1016/j.imavis.2017.01.011}, url = {http://arxiv.org/abs/1604.03489}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto} } @conference {cSalvadorb, title = {Faster R-CNN Features for Instance Search}, booktitle = {CVPR Workshop Deep Vision}, year = {2016}, month = {06/2016}, abstract = {

Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline composed of a first filtering stage followed by a spatial reranking. We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve. We assess the performance of our proposed system with the Oxford Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013, achieving competitive results.

2016-05-Seminar-AmaiaSalvador-DeepVision from Image Processing Group on Vimeo.

Convolutional Features for Instance Search from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w12/papers/Salvador_Faster_R-CNN_Features_CVPR_2016_paper.pdf}, author = {Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Satoh, Shin{\textquoteright}ichi} } @mastersthesis {xCata16, title = {Feature Selection Methods for Predicting Pre-Clinical Stage in Alzheimer{\textquoteright}s Disease}, year = {2016}, abstract = {

Advisors: Ver{\'o}nica Vilaplana, Adri{\`a} Casamitjana

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Alzheimer{\textquoteright}s disease is still an incurable disease. Nevertheless, some of its biomarkers suffer changes in the early stages of the disease, long before clinical symptoms appear. In order to determine how biomarkers obtained from magnetic resonance (MRI) techniques affect the disease{\textquoteright}s evolution, machine learning techniques have been used to design and implement a classification system so as to predict the stages in which several patients belong. One of the main objectives of this project is reducing the number of data to manage, since MRI provide a large volume of data for each patient. As a result, we will focus on the stage of reduction and extraction of characteristics of the classifier which may be relevant for the mentioned problem. We will carry out an exhaustive analysis of different methods of selection of features to apply to biomedical data related to Alzheimer{\textquoteright}s disease. Results obtained will also be applicable to other fields. Finally, we will assess these methods with a multimodal data base provided by the collaboration agreement with Pasqual Maragall Foundation (FPM).

}, author = {Marcel Cat{\`a}}, editor = {Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana} } @conference {cBazazian15, title = {Fast and Robust Edge Extraction in Unorganized Point Clouds}, booktitle = {International Conference on Digital Image Computing: Techniques and Applications}, year = {2015}, month = {11/2015}, publisher = {DICTA/IEEE}, organization = {DICTA/IEEE}, address = {Adelaide, Australia}, abstract = {

Edges provide important visual information in scene surfaces. The need for fast and robust feature extraction from 3D data is nowadays fostered by the widespread availability of cheap commercial depth sensors and multi-camera setups. This article investigates the challenge of detecting edges in surfaces represented by unorganized point clouds. Generally, edge recognition requires the extraction of geometric features such as normal vectors and curvatures. Since the normals alone do not provide enough information about the geometry of the cloud, further analysis of extracted normals is needed for edge extraction, such as a clustering method. Edge extraction through these techniques consists of several steps with parameters which depend on the density and the scale of the point cloud. In this paper we propose a fast and precise method to detect sharp edge features by analysing the eigenvalues of the covariance matrix that are defined by each point{\textquoteright}s k-nearest neighbors. Moreover, we evaluate quantitatively, and qualitatively the proposed methods for sharp edge extraction using several dihedral angles and well known examples of unorganized point clouds. Furthermore, we demonstrate the robustness of our approach in the noisier real-world datasets.

}, doi = {10.1109/DICTA.2015.7371262}, url = {http://dictaconference.org/dicta2015/}, author = {D. Bazazian and Casas, J. and Ruiz-Hidalgo, J.} } @mastersthesis {xCalafell-Oros, title = {Fine-tuning a Convolutional Network for Cultural Event Recognition}, year = {2015}, abstract = {

Advisors: Amaia Salvador (UPC), Matthias Zeppelzauer (FH St P{\"o}lten), Xavier Gir{\'o}-i-Nieto (UPC)

Studies: Bachelor Degree in Audiovisual Systems Engineering at Telecom BCN-ETSETB from the Technical University of Catalonia (UPC)

Grade: A with honors (10/10)

This thesis explores good practices for improving the performance of an existing convnet trained with a dataset of clean data when an additional dataset of noisy data is available. We develop techniques to clean the noisy data with the help of the clean one, a family of solutions that we will refer to as denoising, and then we explore the best sorting of the clean and noisy datasets during the fine-tuning of a convnet. Then we study strategies to select the subset of images of the clean data that will improve the classification performance, a practice we will efer to as fracking. Next, we determine how many layers are actually better to fine-tune in our convnet, given our amount of data. And finally, we compare the classic convnet architecture where a single network is fine-tuned to solve a multi-class problem with the case of fine-tuning a convnet for binary classification for each considered class.

Fine tuning a convolutional network for cultural event recognition from Xavier Giro

2015-TFG-AndreaCalafell-FineTuningConvolutionalNetworkForCulturalEventRecognition from Image Processing Group on Vimeo.

See https://imatge.upc.edu/web/publications/cultural-event-recognition-visual-convnets-and-temporal-models

}, url = {http://hdl.handle.net/2117/78391}, author = {Calafell, Andrea}, editor = {Amaia Salvador and Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} } @article {xCanton-Ferrer15, title = {From Catalonia to America: notes on how to achieve a successful post-PhD career}, year = {2015}, abstract = {

Presented at the 2nd Annual Catalan Meeting on Computer Vision - ACMCV (September 18, 2015)

Talk summary

Reaching the green prairies where the big fat tech companies graze has been historically regarded as a hard and painful journey for young freshly graduated PhDs. The amount of contenders is large and the competition may be overwhelming but, despite all, you often see researchers related to Catalan universities that have found a prominent role in companies like Microsoft, Google, Facebook, etc. By talking to them you start to detect some common factors that contributed to the success of their careers and some common views on the pros/cons related to their experience in big corporations. In this talk, I will present some hints/advice/directions on how to bridge the gap between the end of a PhD and the start of a professional career in the software industry. What is needed? What is the posture of big companies w.r.t. research? Which are our strong assets coming out from a Catalan university (and which are not!)?

Short bio about the speaker

Cristian Canton (Barcelona, 1980) got his MSc in Telecommunications Engineering by the Technical University of Catalonia (UPC) in 2003, carrying out his MSc Thesis at the Swiss Federal Institute of Technology of Lausanne (EPFL), covering topics in image processing and coding. After that, he pursued his PhD at UPC on computer vision and machine learning topics applied to human motion capture in multi-view scenarios. During this period, he was visiting researcher at Technion (Haifa, Israel) and Koc University (Istanbul, Turkey) and took active roles in several EU founded projects. In 2008, he was a Postdoc researcher at NXP in Paris where he developed gesture-based HCIs for mobile devices. In 2009, he joined VICON Ltd., a leading company in the field of vision-based motion capture systems for entertainment and biomedical industries. During 2010-2012, he was EU Project reviewer. He joined Microsoft in 2012 as a senior researcher and developer where transfers technology into real life products such as Surface Hub, Hololens and Skype.

}, keywords = {america, catalonia, computer Vision, job, microsoft}, author = {Cristian Canton-Ferrer} } @conference {cPerez-Pellitero14, title = {Fast Super-Resolution via Dense Local Training and Inverse Regressor Search}, booktitle = {Asian Conference in Computer Vision (ACCV)}, year = {2014}, month = {11/2014}, address = {Singapore}, abstract = {

Regression-based Super-Resolution (SR) addresses the up- scaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors{\textquoteright} dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the state-of-the-art.

}, author = {E. Perez-Pellitero and Salvador, J. and Torres-Xirau, I. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @article {aGiro-i-Nieto13, title = {From Global Image Annotation to Interactive Object Segmentation}, journal = {Multimedia Tools and Applications}, volume = {70}, year = {2014}, month = {05/2014}, chapter = {475}, abstract = {

This paper presents a graphical environment for the annotation of still images that works both at the global and local scales. At the global scale, each image can be tagged with positive, negative and neutral labels referred to a semantic class from an ontology. These annotations can be used to train and evaluate an image classifier. A finer annotation at a local scale is also available for interactive segmentation of objects. This process is formulated as a selection of regions from a precomputed hierarchical partition called Binary Partition Tree. Three different semi-supervised methods have been presented and evaluated: bounding boxes, scribbles and hierarchical navigation. The implemented Java source code is published under a free software license.

}, keywords = {annotation, Hierarchical, Interaction, Multiscale, segmentation}, doi = {10.1007/s11042-013-1374-3}, author = {Xavier Gir{\'o}-i-Nieto and Martos, Manel and Mohedano, Eva and Jordi Pont-Tuset} } @conference {cMaceira13, title = {Fusion of colour and depth partitions for depth map coding}, booktitle = {Digital Signal Processing}, year = {2013}, month = {07/2013}, address = {Santorini, Greece}, abstract = {

3D video coding includes the use of multiple color views and depth maps associated to each view. An adequate coding of depth maps should be adapted to the characteristics of depth maps: smooth regions and sharp edges. In this paper a segmentation-based technique is proposed for improving the depth map compression while preserving the main discontinuities that exploits the color-depth similarity of 3D video. An initial coarse depth map segmentation is used to locate the main discontinuities in depth. The resulting partition is improved by fusing a color partition. We assume that the color image is first encoded and available when the associated depth map is encoded, therefore the color partition can be segmented in the decoder without introducing any extra cost. A new segmentation criterion inspired by super-pixels techniques is proposed to obtain the color partition. Initial experimental results show similar compression efficiency to hevc with a big potential for further improvements.

}, keywords = {3DTV, Depth map coding}, doi = {10.1109/ICDSP.2013.6622781}, author = {Maceira, M. and Morros, J.R. and Ruiz-Hidalgo, J.} } @article {aAlonso-Gonzalez12, title = {Filtering and Segmentation of Polarimetric SAR Data Based on Binary Partition Trees}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {50}, year = {2012}, pages = {593{\textendash}605}, abstract = {

In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed. When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system.

}, issn = {0196-2892}, doi = {10.1109/TGRS.2011.2160647}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @inbook {bGallego12, title = {Foreground objects segmentation for moving camera scenarios based on SCGMM}, booktitle = {Computational Intelligence for Multimedia Understanding}, volume = {7252}, number = {Lecture Notes in Computer Science}, year = {2012}, month = {09/2011}, pages = {195-206}, publisher = {Springer}, organization = {Springer}, address = {Berlin Heidelberg}, abstract = {

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.

}, keywords = {moving camera sequences, Object segmentation, SCGMM, video indexation}, issn = {978-3-642-32435-2}, doi = {10.1007/978-3-642-32436-9_17}, url = {http://www.springerlink.com/content/r6u266562h586476/}, author = {Gallego, J. and M. Pard{\`a}s and Solano, M.} } @conference {cPalou12a, title = {From local occlusion cues to global depth estimation}, booktitle = {IEEE Int. Conf. on Acoustics Speech and Signal Processing, ICASSP 2012}, year = {2012}, month = {03/2012}, address = {Kyoto, Japan}, author = {Palou, G. and Salembier, P.} } @conference {cBosio11, title = {Feature set enhancement via hierarchical clustering for microarray classification}, booktitle = {IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2011}, year = {2011}, month = {dec.}, pages = {226 -229}, abstract = {

A new method for gene expression classification is proposed in this paper. In a first step, the original feature set is enriched by including new features, called metagenes, produced via hierarchical clustering. In a second step, a reliable classifier is built from a wrapper feature selection process. The selection relies on two criteria: the classical classification error rate and a new reliability measure. As a result, a classifier with good predictive ability using as few features as possible to reduce the risk of overfitting is obtained. This method has been tested on three public cancer datasets: leukemia, lymphoma and colon. The proposed method has obtained interesting classification results and the experiments have confirmed the utility of both metagenes and feature ranking criterion to improve the final classifier.

}, keywords = {cancer, classical classification error rate, classifier, colon, feature set enhancement, gene expression classification, hierarchical clustering, leukemia, lymphoma, medical computing, metagenes, microarray classification, overfitting risk reduction, pattern classification, pattern clustering, public cancer datasets, reliability measure, wrapper feature selection process}, doi = {10.1109/GENSiPS.2011.6169486}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cSchreer11, title = {Format-agnostic approach for production, delivery and rendering of immersive media}, booktitle = {Networked and Electronic Media Summit}, year = {2011}, pages = {{\textendash}}, isbn = {SN}, url = {http://nem-summit.eu/program/}, author = {Schreer, O. and Thomas, G. and Niamut, O. and Macq, J. and Kochale, A. and Batke, J. and Ruiz-Hidalgo, J. and Oldfield, R. and Shirley, B. and Thallinger, G.} } @conference {cAlonso-Gonzalez10, title = {Filtering and segmentation of polarimetric SAR images with binary partition trees}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010}, year = {2010}, pages = {4043{\textendash}4046}, address = {Honolulu, USA}, isbn = {978-1-4244-9564-1}, doi = {10.1109/IGARSS.2010.5653466}, url = {http://cataleg.upc.edu/record=b1167223~S1*cat}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @conference {cSalvador10b, title = {From silhouettes to 3D points to mesh: towards free viewpoint video}, booktitle = {ACM Workshop on 3D Video Processing (3DVP)}, year = {2010}, pages = {19{\textendash}24}, isbn = {1522-4880}, doi = {10.1145/1877791.1877797}, url = {http://delivery.acm.org/10.1145/1880000/1877797/p19-salvador.pdf?ip=147.83.95.37\&CFID=24773176\&CFTOKEN=26981598\&__acm__=1306838718_5a07df5f539bac704e3bc98a25b9a68c}, author = {Salvador, J. and Suau, X. and Casas, J.} } @conference {cVilaplana09, title = {Face tracking using a region-based mean-shift algorithm with adaptive object and background models}, booktitle = {Workshop on Image Analysis for Multimedia Interactive Services}, year = {2009}, pages = {9{\textendash}12}, doi = {http://dx.doi.org/10.1109/WIAMIS.2009.5031419}, url = {http://hdl.handle.net/2117/7946}, author = {Ver{\'o}nica Vilaplana and David Varas} } @conference {cLopez-Mendez09, title = {Feature-based annealing particle filter for robust body pose estimation}, booktitle = {Fourth International Conference on Computer Vision Theory and Applications}, year = {2009}, pages = {438{\textendash}443}, abstract = {

This paper presents a new annealing method for particle filtering in the context of body pose estimation. The feature-based annealing is inferred from the weighting functions obtained with common image features used for the likelihood approximation. We introduce a complementary weighting function based on the foreground extraction and we balance the different measures through the annealing layers in order to improve the posterior estimate. This technique is applied to estimate the upper body pose of a subject in a realistic multi-view environment. Comparative results between the proposed method and the common annealing strategy are presented to assess the robustness of the algorithm.

}, doi = {10.5220/0001783404380443}, url = {http://visapp.visigrapp.org/Abstracts/2009/VISAPP_2009_Abstracts.htm}, author = {L{\'o}pez-M{\'e}ndez, A. and Casas, J.} } @article {Caselles200916, title = {Flux-gradient and source-term balancing for certain high resolution shock-capturing schemes}, journal = {Computers \& Fluids}, volume = {38}, year = {2009}, month = {0/2009}, pages = {16 - 36}, abstract = {

We present an extension of Marquina{\textquoteright}s flux formula, as introduced in Fedkiw et al. [Fedkiw RP, Merriman B, Donat R, Osher S. The penultimate scheme for systems of conservation laws: finite difference \{ENO\} with Marquina{\textquoteright}s flux splitting. In: Hafez M, editor. Progress in numerical solutions of partial differential equations, Arcachon, France; July 1998], for the shallow water system. We show that the use of two different Jacobians at cell interfaces prevents the scheme from satisfying the exact C-property [Berm{\'u}dez A, V{\'a}zquez ME. Upwind methods for hyperbolic conservation laws with source terms. Comput Fluids 1994;23(8):1049{\textendash}71] while the approximate C-property is satisfied for higher order versions of the scheme. The use of a single Jacobian in Marquina{\textquoteright}s flux splitting formula leads to a numerical scheme satisfying the exact C-property, hence we propose a combined technique that uses Marquina{\textquoteright}s two sided decomposition when the two adjacent states are not close and a single decomposition otherwise. Finally, we propose a special treatment at wet/dry fronts and situations of dry bed generation.

}, issn = {0045-7930}, doi = {10.1016/j.compfluid.2007.07.023}, author = {Caselles, Vicent and Donat, Rosa and Haro, G.} } @inbook {bVilaplana08, title = {Face and speech interaction}, booktitle = {Multimodal user interfaces: from signals to interaction}, year = {2008}, pages = {85{\textendash}118}, isbn = {978-3-540-78344-2}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Gurban, M. and Thiran, J.} } @conference {cButko08, title = {Fusion of Audio and Video Modalities for Detection of Acoustic Events}, booktitle = {Interspeech 2008, 9th Annual Conference of the International Speech Communication Association}, year = {2008}, month = {09/2008}, pages = {123{\textendash}126}, publisher = {ISCA}, organization = {ISCA}, address = {Brisbane, Australia}, isbn = {978-1-61567-378-0}, url = {http://www.isca-speech.org/archive/interspeech_2008/i08_0123.html}, author = {Butko, T. and Temko, A. and Nadeu, C. and Cristian Canton-Ferrer} } @conference {cVilaplana07a, title = {Face detection and segmentation on a hierarchical image representation}, booktitle = {15th European Signal Processing Conference}, year = {2007}, pages = {1955{\textendash}1959}, isbn = {0-7803-7403-7}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cPetras07, title = {Flexible test-bed for unusual behavior detection}, booktitle = {6th ACM International Conference on Image and Video Retrieval}, year = {2007}, pages = {105{\textendash}108}, isbn = {0 646 34170 7}, author = {Petras, I. and Beleznai, C. and Dedeoglu, Y. and M. Pard{\`a}s and Kov{\'a}cs, L. and Szl{\'a}vik, Z. and Havasi, L. and Szir{\'a}nyi, T. and T{\"o}reyin, B. and G{\"u}d{\"u}kbay, U. and Cetin, E. and Cristian Canton-Ferrer} } @conference {cVilaplana06, title = {Face Recognition Using Groups of Images in Smart Room Scenarios}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {2071{\textendash}2074}, author = {Ver{\'o}nica Vilaplana and Mart{\'\i}nez, C. and Javier, C. and Marqu{\'e}s, F.} } @article {aLandabaso06, title = {Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking}, journal = {Lecture notes in computer science}, volume = {3869}, year = {2006}, pages = {241{\textendash}249}, issn = {0302-9743}, url = {http://www.springerlink.com}, author = {Landabaso, J. and M. Pard{\`a}s} } @inbook {bGiro-i-Nieto06, title = {From partition trees to semantic trees}, booktitle = {Multimedia Content Representation, Classification and Security}, volume = {4105/2006}, number = {4105}, year = {2006}, pages = {306{\textendash}313}, abstract = {

This paper proposes a solution to bridge the gap between semantic and visual information formulated as a structural pattern recognition problem. Instances of semantic classes expressed by Description Graphs are detected on a region-based representation of visual data expressed with a Binary Partition Tree. The detection process builds instances of Semantic Trees on the top of the Binary Partition Tree using an encyclopedia of models organised as a hierarchy. At the leaves of the Semantic Tree, classes are defined by perceptual models containing a list of low-level descriptors. The proposed solution is assessed in different environments to show its flexibility.

}, issn = {0302-9743}, doi = {10.1007/11848035_41}, url = {http://www.springerlink.com/content/j148713624k48u3r/}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto06b, title = {From Partition Trees to Semantic Trees}, booktitle = {2ndas Jornades UPC de Investigaci{\'o}n en Autom{\'a}tica, Visi{\'o}n y Rob{\'o}tica}, year = {2006}, pages = {187{\textendash}194}, isbn = {84-7653-885-5}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto06a, title = {From Partition Trees to Semantic Trees}, booktitle = {International Workshop on Multimedia Content Representation, Classification and Security}, year = {2006}, pages = {306{\textendash}313}, isbn = {3-540-39392-7}, doi = {10.1007/11848035}, url = {http://dx.doi.org/10.1007/11848035_41}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cLandabaso05a, title = {Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, pages = {241{\textendash}249}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {cGarcia05, title = {Functionalities for mapping 2D images and 3D world objects in a Multicamera Environment}, booktitle = {6th International Workshop on Image Analysis for Multimedia Interactive Services}, year = {2005}, abstract = {

We present four functionalities intended to improve the ability of image detection and tracking algorithms to understand a scene in a multicamera system. The redundancy of several available projections of any 3D object onto different cameras might ease video analysis tasks. When some prior information about the 3D object or any of its projections is known, geometric constraints can help to restrict search areas in the images under analysis. The functionalities presented also tackle the problem of selecting the best camera at any time, or computing projected areas of 3D objects in images.

}, author = {Garcia, O. and Casas, J.} } @conference {cCanton-Ferrer05a, title = {Fusion of multiple viewpoint information towards 3d face robust orientation detection}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, abstract = {

This paper presents a novel approach to the problem of determining head pose estimation and face 3D orientation of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited and the heads of people in the scene are detected and geometrically approximated by an ellipsoid using a voxel reconstruction and a moment analysis method. Skin patches from each detected head are located in each camera view. Data fusion is performed by back-projecting skin patches from single images onto the estimated 3D head model, thus providing a synthetic reconstruction of the head appearance. Finally, these data are processed in a pattern analysis framework thus giving a reliable and robust estimation of face orientation. Tracking over time is performed by Kalman filtering. Results are provided showing the effectiveness of the proposed algorithm in a SmartRoom scenario.

}, isbn = {0-7803-9135-7}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {aMarques02, title = {Face segmentation and tracking based on connected operators and partition projection}, journal = {Pattern recognition}, volume = {35}, number = {3}, year = {2002}, pages = {601{\textendash}614}, issn = {0031-3203}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana} } @article {aPardas02, title = {Facial animation parameters extraction and expression recognition using Hidden Markov Models}, journal = {Signal processing: image communication}, number = {17}, year = {2002}, pages = {675{\textendash}688}, issn = {0923-5965}, author = {M. Pard{\`a}s and Bonafonte, A.} } @conference {cMarques02a, title = {Facial Feature Segmentation from Frontal View Images}, booktitle = {11th European Signal Processing Conference (EUSIPCO 2002)}, year = {2002}, pages = {33{\textendash}36}, author = {Marqu{\'e}s, F. and Sobrevals, C.} } @conference {cSalembier02, title = {On Filters by Reconstruction for Size and Motion Simplification,}, booktitle = {Int. Symposium on Mathematical Morphology, ISMM 2002}, year = {2002}, month = {04/2002}, pages = {425{\textendash}434}, address = {Sydney, Australia}, author = {Salembier, P. and Ruiz-Hidalgo, J.} } @conference {cGarrido02, title = {A framework for the retrieval of multiple regions using Binary Partition Trees and low level descriptors}, booktitle = {11th European Signal Processing Conference, EUSIPCO 2002}, year = {2002}, pages = {512{\textendash}516}, address = {Toulouse, France}, author = {Garrido, L. and Salembier, P.} } @conference {cPardas01, title = {Facial Animation Parameters extraction and Expression detection using HMM}, booktitle = {International Conference on Augmented, Virtual Environments and 3D Imaging}, year = {2001}, pages = {120{\textendash}123}, isbn = {1-4244-1764-3}, author = {M. Pard{\`a}s and Bonafonte, A.} } @conference {cPardas01a, title = {Facial Parameter Extraction System based on Active Contours}, booktitle = {IEEE International Conference on Image Processing}, year = {2001}, pages = {1058{\textendash}1061}, author = {M. Pard{\`a}s and Marcos, L.} } @conference {cTorres99, title = {Face location and recognition for video indexing in the Hypermedia project}, booktitle = {European Conference on Multimedia Applications, Services and Techniques}, year = {1999}, pages = {364{\textendash}377}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @inbook {aTorres99, title = {Face Location and Recognition for Video Indexing in the Hypermedia Project}, booktitle = {Lecture Notes in Computer Science}, volume = {1629}, year = {1999}, issn = {364-377}, doi = {DOI: 10.1007/3-540-48757-3_25}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @conference {cVilaplana98, title = {Face segmentation using connected operators}, booktitle = {Mathematical Morphology and its applications to image and signal processing}, year = {1998}, pages = {207{\textendash}214}, isbn = {0 7923 5133 9}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cCasas96, title = {A feature-based subband coding schene}, booktitle = {THE 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH \& SIGNAL PROCESSING}, year = {1996}, pages = {2357{\textendash}2360}, abstract = {

In this paper, a feature-based subband coding scheme is presented. It is intended for the coding of the texture component of a two-component image model. The przmary component contains the strong edges, individually described by their amplitudes and positions. A morphological interpolation technique, presented in a recent paper [4], reconstructs the smooth areas of the image from the coded transitions. The residual texture component is coded by subband decomposition. Information about the edge structure, available from the primary component, is used for texture coding by employing adaptive quantizers and bit allocations over arbitrarily shaped regions. This feature-based subband coding technique produces subjective improvements over conventional subband schemes. The enhanced rendition of the strong edges is especially noticeable at very-low bit-rates. The coding of the primary component, based on morphological tools, is also more efficient and of lower complexity than in previously reported schemes for two-component image coding models.

}, isbn = {0-7803-3192-3}, author = {Casas, J. and Torres, L.} } @article {pOliveras96, title = {Filtering Method and Corresponding Filtering System}, number = {96402925.0-}, year = {1996}, type = {Invention patent}, author = {Albert Oliveras and Salembier, P. and Garrido, L.} } @article {aSalembier95a, title = {Flat zones filtering, connected operators and filters by reconstruction}, journal = {IEEE transactions on image processing}, volume = {3}, number = {8}, year = {1995}, pages = {1153{\textendash}1160}, issn = {1057-7149}, author = {Salembier, P. and Serra, J.} } @conference {cSayrol95, title = {Fourth-order statistics cost functions: applications to time delay estimation and image motion estimation}, booktitle = {VI SPANISH SYMPOSIUM ON PATTERN RECOGNITION AND IMAGE ANALYSIS}, year = {1995}, pages = {543{\textendash}548}, isbn = {978-1-4244-9564-1}, author = {Elisa Sayrol and Gasull, A. and R. Fonollosa, Javier} } @conference {cCasas94c, title = {Feature-based video coding using Mathematical Morphology}, booktitle = {VII European Signal Processing Conference (EUSIPCO)}, year = {1994}, month = {09/1994}, publisher = {EURASIP}, organization = {EURASIP}, address = {Edinburgh}, abstract = {

This paper puts forward a new approach to second generation image coding. The concept of "image feature" is introduced in order to deal with those "objects" that cannot be properly described as regions within a segmentation framework. Visual features such as open contours or texture details are extracted from the original images using morphological operators. As malhematical morphology deals with the shapes and structures of the images, the resulting features are very close to what would be obtained from a visual perception point of view. If these features are efficiently coded with suitable techniques, such coding model is able to reach higher compression ratios than purely segmentationbased techniques. Numerical results are given at the end of the paper to prove the goodness of the feature-based coding scheme in a very-low bit-rate video coding application.

}, author = {Casas, J. and Torres, L.} } @conference {cCasas92a, title = {Fuzzy classification of Remote Sensing images: a pseudocolor representation of fuzzy partitions}, booktitle = {SPIE Neural and Stochastic Methods in Image and Signal Processing}, year = {1992}, month = {07/1992}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, CA}, doi = {10.1117/12.130844}, author = {Casas, J. and Hillion, A. and Roux, C. and Torres, L. and Gasull, A.} }