@article {aGene-Molad, title = {AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation}, journal = {Data in Brief}, volume = {52}, year = {2024}, month = {02/2024}, abstract = {

The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub- set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research papers titled "Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation" [1] and {\textquotedblleft}Simultaneous fruit detection and size estimation using multitask deep neural networks{\textquotedblright} [2].

}, keywords = {Agricultural robotics, amodal segmentation, depth image, Fruit measurement, Fruit visibility, Instance Segmentation, modal segmentation, Yield prediction}, doi = {https://doi.org/10.1016/j.dib.2023.110000}, author = {Gen{\'e}-Mola, Jordi and Ferrer-Ferrer, M. and Hemming, J. and Dalfsen, P. and Hoog, D. and Sanz-Cortiella, R. and Rosell-Polo, Joan R. and Morros, J.R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @conference {cBarrabes, title = {Adversarial Learning for Feature Shift Detection and Correction}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2023}, month = {12/2023}, address = {New Orleans, USA}, abstract = {

TL;DR: We introduce a framework inspired in adversarial learning to detect and correct features originating a distribution shift between datasets.

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods trying to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts outperforming current statistical and neural network-based techniques.

}, author = {Barrab{\'e}s, M{\'\i}riam and Mas-Montserrat, Daniel and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xPerera22, title = {Ancestry-conditioned Generative Models for Genotyping}, year = {2022}, abstract = {

Local ancestry inference (LAI) identifies the ancestry of each segment of an individual{\textquoteright}s genome and it is a critical step in the analysis of human genomes with applications from pharmacogenomics and personalized medicine to increase detection of genetic associations.\ 

New LAI techniques are appearing at a fast pace in both industry and academic research and large data-sets of human genomic sequences from the ancestries of interest are required to train those methods. Usually, those data-sets are protected by privacy regulations, are proprietary or accessible only when they come with restrictions due to its nature. An interesting way to overcome those difficulties is through the generation of data samples that could be similar enough to real sequences from ancestries of interest. A generalized model can be openly shared because there is no real individual information in there.\ 

Thus, we present a class-conditional Generative adversarial Model and a Conditional Generative Moment-Matching Network intended to generate new realistic genotypes of a desired ancestry. In addition, we present a privacy mechanism that extracts features from the real data to generate new realistic genotypes by using features.

}, author = {Perera, Maria}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cCombaliae, title = {Artificial intelligence to predict positivity of sentinel lymph node biopsy in melanoma patients}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Marc Combalia and Sebastian Podlipnik and Carlos Hernandez and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Ver{\'o}nica Vilaplana and Josep Malvehy} } @mastersthesis {xEscur, title = {Attention-based multi-view 3D reconstruction models}, year = {2020}, abstract = {

Attention mechanism has been successfully used in multiple tasks in the fields of Computer Vision and Natural Language Processing, but has not ever been applied to 3D reconstruction problems. In this work, we explore the potential of attention in a multi-view 3D face recon- struction pipeline. On one hand, we use spatial attention when extracting the features of the input images, taking advantage of the interpretability it provides us. This allows us to validate the proper behaviour of the model. On the other hand, we want to make this multi-view setup invariant to the order of the input image?s views. To do so, instead of concatenating the fea- tures of the different views, we use part of the Transformer architecture as a symmetric merging function, which is based on a multi-head self-attention mechanism, showing an improvement in the performance.

}, author = {Escur, Janna}, editor = {Ramon, Eduard and Xavier Gir{\'o}-i-Nieto} } @conference {xCaros19, title = {Automatic Reminiscence Therapy for Dementia}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)}, year = {2020}, month = {06/2020}, publisher = {ACM}, organization = {ACM}, address = {Dublin, Ireland}, abstract = {

With people living longer than ever, the number of cases with dementia such as Alzheimer{\textquoteright}s disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. (demo paper)

Automatic Reminiscence Therapy for Dementia from Universitat Polit{\`e}cnica de Catalunya



}, doi = {https://doi.org/10.1145/3372278.3391927}, url = {https://arxiv.org/abs/1910.11949}, author = {Caros, Mariona and Garolera, Maite and Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @conference {cGorrizc, title = {Assessing Knee OA Severity with CNN attention-based end-to-end architectures}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL) 2019}, year = {2019}, month = {02/2019}, publisher = {JMLR}, organization = {JMLR}, address = {London, United Kingdom}, abstract = {

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to\ automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which\ incorporates trainable attention modules acting as unsupervised fine-grained detectors of\ the region of interest (ROI). The proposed attention modules can be applied at different\ levels and scales across any CNN pipeline helping the network to learn relevant attention\ patterns over the most informative parts of the image at different resolutions. We test\ the proposed attention mechanism on existing state-of-the-art CNN architectures as our\ base models, achieving promising results on the benchmark knee OA datasets from the\ osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All the codes\ from our experiments will be publicly available on the github repository: https://github.com/marc-gorriz/KneeOA-CNNAttention

}, url = {http://proceedings.mlr.press/v102/gorriz19a.html}, author = {G{\'o}rriz, Marc and Antony, Joseph and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cLopez-Palmab, title = {Audience measurement using a top-view camera and oriented trajectories}, booktitle = {IEEE IECON 2019}, year = {2019}, month = {10/2019}, address = {Lisbon, Portugal}, abstract = {

One of the points of maximum interest in the valuation of the areas of commercial advertising is the probability with which that publicity will be seen. This paper presents a method based on top-view camera measurement, where the probability of viewing is estimated based on the trajectories and movements of the heads of the passerby individuals in the area of interest. Using a camera with a depth sensor, the heads of the people in the range of view can be detected and modeled. This allows to\  determine the orientation of the head which is used to estimate the direction of vision. A tracking by detection algorithm\  allows to compute the trajectory of each user. The attention at each advertising point is estimated based on the trajectories and head orientations of the individuals in the area of interest.

}, keywords = {attention time, depth sensor, top-view camera, tracking}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Montserrat Corbal{\'a}n and Javier Gago} } @conference {cXu, title = {Action Tube Extraction based 3D -CNN for RGB-D Action Recognition}, booktitle = {International Conference on Content-Based Multimedia Indexing CBMI 2018}, year = {2018}, month = {09/2018}, abstract = {

In this paper we propose a novel action tube extractor for RGB-D action recognition in trimmed videos. The action tube extractor takes as input a video and outputs an action

tube. The method consists of two parts: spatial tube extraction and temporal sampling. The first part is built upon MobileNet-SSD and its role is to define the spatial region where the action takes place. The second part is based on the structural similarity index (SSIM) and is designed to remove frames without obvious motion from the primary action tube. The final extracted action tube has two benefits: 1) a higher ratio of ROI (subjects of action) to background; 2) most frames contain obvious motion change. We propose to use a two-stream (RGB and Depth) I3D architecture as our 3D-CNN model. Our approach outperforms the state-of-the-art methods on the OA and NTU RGB-D datasets.

}, author = {Zineng Xu and Ver{\'o}nica Vilaplana and Morros, J.R.} } @inbook {bTochon18, title = {Advances in utilization of hierarchical representations in remote sensing data analysis}, booktitle = {Reference Module in Earth Systems and Environmental Sciences}, volume = {2}, year = {2018}, pages = {77-107}, publisher = {Elsevier}, organization = {Elsevier}, issn = {978-0-12-409548-9}, author = {G. Tochon and Dalla Mura, M. and Veganzones, M.A. and Valero, S. and Salembier, P. and Chanussot, J.} } @mastersthesis {xArtigot18, title = {Automatic fruit classification using deep learning}, year = {2018}, abstract = {

To achieve greater efficiency at harvesting tasks, the mechanization of such task is unavoidable. Apart from the mechanical aspects, the harvesting systems needs software that can locate the fruit to be harvested.

The use of machine learning and deep learning techniques to achieve such software was studied in this thesis. The results showed that an accuracy similar to other studies is feasible with a limited number of training samples using deep learning techniques.

From this thesis we conclude that the mechanization of the harvesting labour is possible, at least from the software point of view, while the crop estimation application may need some more work before being feasible.

}, author = {Joaquim Mart{\'\i}nez Artigot}, editor = {Josep Ramon Morros and Ver{\'o}nica Vilaplana} } @mastersthesis {xGorriz, title = {Active Deep Learning for Medical Imaging Segmentation}, year = {2017}, abstract = {

Grade: A (9.7/10)

This thesis proposes a novel active learning framework capable to train e ectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our approach tries to apply in segmentation existing active learning techniques, which is becoming an important topic today because of the many problems caused by the lack of large amounts of data. We explore di erent strategies to study the image information and introduce a previously used cost-e ective active learning method based on the selection of high con dence predictions to assign automatically pseudo-labels with the aim of reducing the manual annotations. First, we made a simple application for handwritten digit classi cation to get started to the methodology and then we test the system with a medical image database for the treatment of melanoma skin cancer. Finally, we compared the traditional training methods with our active learning proposals, specifying the conditions and parameters required for it to be optimal.

Active Deep Learning for Medical Imaging from Xavier Giro-i-Nieto
}, url = {http://hdl.handle.net/2117/109304}, author = {G{\'o}rriz, Marc}, editor = {Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} } @conference {cGorriz, title = {Active Deep Learning for Medical Imaging Segmentation}, booktitle = {Medical Image meets NIPS 2017 Workshop}, year = {2017}, month = {11/2017}, 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
}, author = {G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} } @article {xSalvador17, title = {Artificial intelligence suggests recipes based on food photos}, volume = {2017}, year = {2017}, publisher = {MIT News}, address = {Boston}, abstract = {

Given a still image of a dish filled with food, CSAIL team{\textquoteright}s deep-learning algorithm recommends ingredients and recipes.

}, url = {Artificial intelligence suggests recipes based on food photos}, author = {Amaia Salvador} } @conference {cCasamitjanaa, title = {Augmented V-Net for infant brain segmentation}, booktitle = {MICCAI Grand Challenge on 6-month Infant Brain MRI Segmentation, MICCAI 2017}, year = {2017}, month = {09/2017}, author = {Adri{\`a} Casamitjana and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cCasamitjana, title = {Augmented V-Net for White Matter Hyperintensities segmentation}, booktitle = {WMH Segmentation Challenge, Brain-lesion Workshop, MICCAI 2017 }, year = {2017}, month = {09/2017}, author = {Adri{\`a} Casamitjana and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @mastersthesis {xAduriz16, title = {Analysis of the dynamics of gray matter reduction in Alzheimer{\textquoteright}s Disease}, year = {2016}, abstract = {

Advisor: Ver{\'o}nica Vilaplana

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

This project attempts to study the cerebral atrophy patterns in gray matter across the
di erent stages of the Alzheimer{\textquoteright}s Disease (AD), or more speci cally, along the entire AD
continuum, in a voxelwise approach. To this end, we propose and implement an extensible
toolbox that allows to t di erent models to the data, hence de ning a curve for each voxel
that shows the evolution of the gray matter volume in the respective region as compared
to the progression of the disease. The toolbox also includes several evaluation methods
to estimate how closely the proposed model ts the data for each particular voxel. The
resulting values, namely tting-scores, serve as a base to achieve two di erent goals: a)
to identify the regions within the brain that are (most) likely to follow the curve-shape
speci ed in a given model, and b) to depict the model that best describes the behavior of
the gray matter volume in each voxel from a xed set of models.

}, author = {Asier Aduriz}, editor = {Ver{\'o}nica Vilaplana} } @article {aPerez-Pellitero16, title = {Antipodally Invariant Metrics For Fast Regression-Based Super-Resolution}, journal = {IEEE Transactions on Image Processing}, volume = {25}, year = {2016}, month = {06/2016}, pages = {2468}, chapter = {2456}, abstract = {

Dictionary-based Super-Resolution algorithms usually select dictionary atoms based on distance or similarity metrics. Although the optimal selection of nearest neighbors is of central importance for such methods, the impact of using proper metrics for Super-Resolution (SR) has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper we present a very fast regression-based algorithm which builds on densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e. its antipode, with same module and angle, but opposite direction. Even though we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both worlds, we propose a simple yet effective Antipodally Invariant Transform (AIT) that can be easily included in the Euclidean distance calculation. We modify the original Spherical Hashing algorithm with this metric in our Antipodally Invariant Spherical Hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to Iterative Back Projection. The performance of our method, which we named Antipodally Invariant Super-Resolution (AIS), improves quality (PSNR) and it is faster than any other state-of-the-art method.

}, doi = {10.1109/TIP.2016.2549362}, url = {http://perezpellitero.github.io/project_websites/ais_sr.html}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @article {a, title = {Assessment of Crowdsourcing and Gamification Loss in User-Assisted Object Segmentation}, journal = {Multimedia Tools and Applications}, volume = {23}, year = {2016}, month = {11/2016}, chapter = {15901-15928}, abstract = {

There has been a growing interest in applying human computation -- \ particularly crowdsourcing techniques -- to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans.

In this paper we focus on a specific problem -- object segmentation within color images -- and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game.\ We use Click{\textquoteright}n{\textquoteright}Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask{\textquoteright}nSeek, a game with a purpose designed for object detection and segmentation.

The two main contributions of this paper are: (i) We use the results of Click{\textquoteright}n{\textquoteright}Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we\ compare the quality \ of the results obtained with Click{\textquoteright}n{\textquoteright}Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task.\ 

We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that -- on the other hand -- the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.\ 

}, keywords = {Crowdsourcing, GWAP, Object detection, Object segmentation, Serious games}, issn = {1573-7721}, doi = {10.1007/s11042-015-2897-6}, url = {http://dx.doi.org/10.1007/s11042-015-2897-6}, author = {Carlier, Axel and Amaia Salvador and Cabezas, Ferran and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent and Marques, Oge} } @conference {cPerez-Pellitero15, title = {Accelerating Super-Resolution for 4K Upscaling}, booktitle = {IEEE International Conference on Consumer Electronics}, year = {2015}, month = {01/2015}, address = {Las Vegas, NV, USA}, abstract = {

This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques and tackles the problem using binary boosted tree classifiers. The applied upscaler is chosen based on the obtained probabilities: (1) A fast upscaler (e.g. bicubic interpolation) for those regions which are smooth or (2) a linear regression SR algorithm for those which are ill-posed. The proposed strategy accelerates SR by only processing the regions which benefit from it, thus not compromising quality. Furthermore all the algorithms composing the pipeline are naturally parallelizable and further speed-ups could be obtained.

}, doi = {10.1109/ICCE.2015.7066429}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @mastersthesis {xQueralt14, title = {Automatic Human Detection and Tracking for Robust Video Sequence Annotation}, year = {2014}, author = {Ramon Llorca Queralt}, editor = {Morros, J.R. and David Varas} } @mastersthesis {xRamon13, title = {Algorithms for B wave detection}, year = {2013}, abstract = {

The objective of this Master Thesis was to develop algorithms for B waves detection in ICP. This goal was approached by two different methods that depend basically in the resolution of the acquired ICP. Then, both methods were adapted to work in an ultra-low power microcontroller. The first method works using ICP recorded at 1 Hz and it is based on the Lundberg{\textquoteright}s definition of B wave. A plus of this algorithm is that reduces to the minimum the number of samples per block to classify. The results obtained after testing it using long records of ICP from 27 patients were an accuracy of 89,59\%, a specificity 89,71\% and a sensitivity of 89,16\%. These results did not change when the code was adapted to the microcontroller. The second method requires ICP obtained with a sampling rate of 100 Hz. It is based on the morphology of the pulse waves present in the ICP and caused by the change of blood volume inside the skull with every heartbeat. A total of 1430 blocks of ICP (864 for lack of B wave and 566 for presence of B wave), everyone with duration of 41 seconds, were used to extract 21 features from each one. Then a MLP classifier and a SVM classifier were tested and compared. The best results were obtained by the SVM classifier, reaching an accuracy of 86,37\%, a specificity of 88,09\% and a sensitivity of 83,74\% when all features were used. After adapting the algorithm to the microcontroller the results were nearly the same.

}, url = {http://hdl.handle.net/2099.1/19034}, author = {Ramon, Eduard} } @conference {cVentura13, title = {Automatic Keyframe Selection based on Mutual Reinforcement Algorithm}, booktitle = {CBMI (Content-Based Multimedia Indexing)}, year = {2013}, month = {09/2013}, address = {Veszprem}, abstract = {

This paper addresses the problem of video summarization through an automatic selection of a single representative keyframe. The proposed solution is based on the mutual reinforcement paradigm, where a keyframe is selected thanks to its highest and most frequent similarity to the rest of considered frames. Two variations of the algorithm are explored: a first one where only frames within the same video are used (intraclip mode) and a second one where the decision also depends on the previously selected keyframes of related videos (interclip mode). These two algorithms were evaluated by a set of
professional documentalists from a broadcaster{\textquoteright}s archive, and results concluded that the proposed techniques outperform the semi-manual solution adopted so far in the company.

http://cbmi2013.mik.uni-pannon.hu/

Automatic Keyframe Selection based on Mutual Reinforcement Algorithm from Xavi Gir{\'o}
}, keywords = {mutual reinforcement algorithm, video summarization}, isbn = {978-1-4799-0955-1}, doi = {10.1109/CBMI.2013.6576548}, url = {http://dx.doi.org/10.1109/CBMI.2013.6576548}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Daniel Giribet and Eusebio Carasusan} } @phdthesis {dLopez-Mendez12, title = {Articulated Models for Human Motion Analysis}, year = {2012}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Human motion analysis is as a broad area of computer vision that has strongly attracted the interest of researchers in the last decades. Motion analysis covers topics such as human motion tracking and estimation, action and behavior recognition or segmentation of human motion. All these fields are challenging due to different reasons, but mostly because of viewing perspectives, clutter and the imprecise semantics of actions and human motion. The computer vision community has addressed human motion analysis from several perspectives. Earlier approaches often relied on articulated human body models represented in the three-dimensional world. However, due to the traditionally high difficulty and cost of estimating such an articulated structure from video, research has focus on the development of human motion analysis approaches relying on low-level features. Although obtaining impressive results in several tasks, low-level features are typically conditioned by appearance and viewpoint, thus making difficult their application on different scenarios. Nonetheless, the increase in computational power, the massive availability of data and the irruption of consumer-depth cameras is changing the scenario, and with that change human motion analysis through articulated models can be reconsidered. Analyzing and understanding of human motion through 3-dimensional information is still a crucial issue in order to obtain richer models of dynamics and behavior. In that sense, articulated models of the human body offer a compact and view-invariant representation of motion that can be used to leverage motion analysis. In this dissertation, we present several approaches for motion analysis. In particular, we address the problem of pose inference, action recognition and temporal clustering of human motion. Articulated models are the leitmotiv in all the presented approaches. Firstly, we address pose inference by formulating a layered analysis-by-synthesis framework where models are used to generate hypothesis that are matched against video. Based on the same articulated representation upon which models are built, we propose an action recognition framework. Actions are seen as time-series observed through the articulated model and generated by underlying dynamical systems that we hypothesize that are generating the time-series. Such an hypothesis is used in order to develop recognition methods based on time-delay embeddings, which are analysis tools that do not make assumptions on the form of the form of the underlying dynamical system. Finally, we propose a method to cluster human motion sequences into distinct behaviors, without a priori knowledge of the number of actions in the sequence. Our approach relies on the articulated model representation in order to learn a distance metric from pose data. This metric aims at capturing semantics from labeled data in order to cluster unseen motion sequences into meaningful behaviors. The proposed approaches are evaluated using publicly available datasets in order to objectively measure our contributions.

}, url = {http://hdl.handle.net/10803/112124}, author = {L{\'o}pez-M{\'e}ndez, A.}, editor = {Casas, J.} } @article {aButko11, title = {Acoustic event detection based on feature-level fusion of audio and video modalities}, journal = {Eurasip journal on advances in signal processing}, volume = {2011}, year = {2011}, pages = {1{\textendash}11}, abstract = {

Acoustic event detection (AED) aims at determining the identity of sounds and their temporal position in audio signals. When applied to spontaneously generated acoustic events, AED based only on audio information shows a large amount of errors, which are mostly due to temporal overlaps. Actually, temporal overlaps accounted for more than 70\% of errors in the real-world interactive seminar recordings used in CLEAR 2007 evaluations. In this paper, we improve the recognition rate of acoustic events using information from both audio and video modalities. First, the acoustic data are processed to obtain both a set of spectrotemporal features and the 3D localization coordinates of the sound source. Second, a number of features are extracted from video recordings by means of object detection, motion analysis, and multicamera person tracking to represent the visual counterpart of several acoustic events. A feature-level fusion strategy is used, and a parallel structure of binary HMM-based detectors is employed in our work. The experimental results show that information from both the microphone array and video cameras is useful to improve the detection rate of isolated as well as spontaneously generated acoustic events.

}, issn = {1687-6172}, doi = {10.1155/2011/485738}, url = {http://www.hindawi.com/journals/asp/2011/485738/}, author = {Butko, T. and Cristian Canton-Ferrer and Segura, C. and Xavier Gir{\'o}-i-Nieto and Nadeu, C. and Hernando, J. and Casas, J.} } @conference {cNiamut11, title = {Advanced visual rendering, gesture-based interaction and distributed delivery for immersive and interactive media services}, booktitle = {International Broadcasting Convention 2011}, year = {2011}, pages = {1{\textendash}8}, isbn = {0780388747}, url = {http://www.ibc.org/page.cfm/Action=Visitor/VisitorID=2851/PageOption=Seminar_1/libEntryID=15}, author = {Niamut, O. and Kochale, A. and Ruiz-Hidalgo, J. and Macq, J. and Kienast, G.} } @conference {cLopez-Mendez11, title = {Approximate partitioning of observations in hierarchical particle filter body tracking}, booktitle = {2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2011}, pages = {19{\textendash}24}, abstract = {

This paper presents a model-based hierarchical particle filtering algorithm to estimate the pose and anthropometric parameters of humans in multi-view environments. Our method incorporates a novel likelihood measurement approach consisting of an approximate partitioning of observations. Provided that a partitioning of the human body model has been defined and associates body parts to state space variables, the proposed method estimates image regions that are relevant to that body part and thus to the state space variables of interest. The proposed regions are bounding boxes and consequently can be efficiently processed in a GPU. The algorithm is tested in a challenging dataset involving people playing tennis (TennisSense) and also in the well-known HumanEva dataset. The obtained results show the effectiveness of the proposed method.

}, isbn = {978-1-4577-0529-8}, doi = {10.1109/CVPRW.2011.5981712}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5981712}, author = {L{\'o}pez-M{\'e}ndez, A. and Alcoverro, M. and M. Pard{\`a}s and Casas, J.} } @conference {cValero11a, title = {Arbre de partition binaire: un nouvel outil pour la repr{\'e}sentation hi{\'e}rarchique et l{\textquoteright}analyse des images hyperspectrales}, booktitle = {XXIII{\`e} Colloque sur le Traitement du Signal et des Images, GRETSI 2011}, year = {2011}, pages = {120{\textendash}125}, address = {Bordeaux, France}, url = {http://hdl.handle.net/2117/14621}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @inbook {bNickel09, title = {Activity Classification}, booktitle = {Computers in the Human Interaction Loop}, year = {2009}, pages = {107{\textendash}119}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

When a person enters a room, he or she immediately develops a mental concept about {\textquotedblleft}what is going on{\textquotedblright} in the room; for example, people may be working in the room, people may be engaged in a conversation, or the room may be empty. The CHIL services depend on just the same kind of semantic description, which is termed activity in the following. The {\textquotedblleft}Connector{\textquotedblright} or the {\textquotedblleft}Memory Jog{\textquotedblright}, for example, could provide support that is appropriate for the given context if it knew about the current activity at the user{\textquoteright}s place. This kind of higher-level understanding of human interaction processes could then be used, e.g., for rating the user{\textquoteright}s current availability in a certain situation.

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Nickel, K. and M. Pard{\`a}s and Stiefelhagen, R. and Cristian Canton-Ferrer and Landabaso, J. and Casas, J.} } @mastersthesis {xRovira09, title = {Aplicaci{\'o} rica d{\textquoteright}internet per a la consulta amb text i imatge a la Corporaci{\'o} Catalana de Mitjans Audiovisuals}, year = {2009}, abstract = {

Premi millor projecte final de carrera d{\textquoteright}Enginyeria de Telecomunicaci{\'o} en Serveis Telem{\`a}tics. Atorgat per Accenture (Curs 2009-2010)

}, url = {http://hdl.handle.net/2099.1/8766}, author = {Salla-Rovira, Ramon}, editor = {Xavier Gir{\'o}-i-Nieto and Vives, X.} } @book {eDutoit09, title = {Applied signal processing: A Matlab-based proof of concept}, year = {2009}, publisher = {Springer}, organization = {Springer}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Dutoit, T. and Marqu{\'e}s, F.} } @conference {cCanton-Ferrer09, title = {Audiovisual Event Detection Towards Scene Understanding}, booktitle = {2009 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2009}, pages = {840{\textendash}847}, abstract = {

Acoustic events produced in meeting environments may contain useful information for perceptually aware interfaces and multimodal behavior analysis. In this paper, a system to detect and recognize these events from a multimodal perspective is presented combining information from multiple cameras and microphones. First, spectral and temporal features are extracted from a single audio channel and spatial localization is achieved by exploiting cross-correlation among microphone arrays. Second, several video cues obtained from multi-person tracking, motion analysis, face recognition, and object detection provide the visual counterpart of the acoustic events to be detected. A multimodal data fusion at score level is carried out using two approaches: weighted mean average and fuzzy integral. Finally, a multimodal database containing a rich variety of acoustic events has been recorded including manual annotations of the data. A set of metrics allow assessing the performance of the presented algorithms. This dataset is made publicly available for research purposes.

}, doi = {10.1109/CVPRW.2009.5204264}, author = {Cristian Canton-Ferrer and Butko, T. and Segura, C. and Xavier Gir{\'o}-i-Nieto and Nadeu, C. and Hernando, J. and Casas, J.} } @article {aCanton-Ferrer08, title = {Audiovisual head orientation estimation with particle filtering in multisensor scenarios}, journal = {EURASIP Journal on Advances in Signal Processing}, volume = {2008}, year = {2008}, month = {06/2007}, pages = {1{\textendash}13}, chapter = {276846}, abstract = {

This article presents a multimodal approach to head pose estimation of individuals in environments equipped with multiple cameras and microphones, such as SmartRooms or automatic video conferencing. Determining the individuals head orientation is the basis for many forms of more sophisticated interactions between humans and technical devices and can also be used for automatic sensor selection (camera, microphone) in communications or video surveillance systems. The use of particle filters as a unified framework for the estimation of the head orientation for both monomodal and multimodal cases is proposed. In video, we estimate head orientation from color information by exploiting spatial redundancy among cameras. Audio information is processed to estimate the direction of the voice produced by a speaker making use of the directivity characteristics of the head radiation pattern. Furthermore, two different particle filter multimodal information fusion schemes for combining the audio and video streams are analyzed in terms of accuracy and robustness. In the first one, fusion is performed at a decision level by combining each monomodal head pose estimation, while the second one uses a joint estimation system combining information at data level. Experimental results conducted over the CLEAR 2006 evaluation database are reported and the comparison of the proposed multimodal head pose estimation algorithms with the reference monomodal approaches proves the effectiveness of the proposed approach.

}, issn = {1687-6172}, doi = {10.1155/2008/276846}, url = {http://asp.eurasipjournals.com/content/2008/1/276846}, author = {Cristian Canton-Ferrer and Segura, C. and Casas, J. and M. Pard{\`a}s and Hernando, J.} } @conference {cLuque06a, title = {Audio, Video and Multimodal Person Identification in a Smart Room}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2007}, pages = {258{\textendash}269}, isbn = {978-3-540-69567-7}, author = {Luque, J. and Morros, J.R. and Garde, A. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @conference {cSole06a, title = {Adaptive Quadratic Interpolation Methods for Lifting Steps Construction}, booktitle = {IEEE International Symposium on Signal Processing and Information Technology, , ISSPIT 2006}, year = {2006}, pages = {1{\textendash}8}, address = {Vancouver, Canada}, isbn = {1-4244-1437-7}, author = {Sol{\'e}, J. and Salembier, P.} } @inbook {bLuque06, title = {Audio, Video and Multimodal Person Identification in a Smart Room}, booktitle = {Lecture notes in computer science - Multimodal Technologies for Perception of Humans}, volume = {4122}, year = {2006}, pages = {258{\textendash}269}, issn = {0302-9743}, doi = {10.1007/978-3-540-69568-4_23}, author = {Luque, J. and Morros, J.R. and Garde, A. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @conference {cSole05, title = {Adaptive Generalized Prediction for Lifting Schemes}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2005}, year = {2005}, pages = {205{\textendash}208}, address = {Philadelphia, USA}, isbn = {1-4244-1011-8}, author = {Sol{\'e}, J. and Salembier, P.} } @inbook {bGiro-i-Nieto05, title = {Automatic extraction and analysis of visual objects information}, booktitle = {Multimedia content and the semantic web}, year = {2005}, pages = {203{\textendash}221}, publisher = {Wiley}, organization = {Wiley}, chapter = {7}, isbn = {978-0-470-85753-3}, doi = {10.1002/0470012617.ch7}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470857536.html}, author = {Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Salembier, P.} } @article {aMarcello05, title = {Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {43}, number = {7}, year = {2005}, pages = {1605{\textendash}1616}, issn = {0196-2892}, author = {Marcello, J. and Marqu{\'e}s, F. and F. Eugenio} } @conference {cSole04, title = {Adaptive Discrete Generalized Lifting for Lossless Compression}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2004}, year = {2004}, pages = {1{\textendash}5}, address = {Montreal, Canada}, isbn = {1-4244-1764-3}, author = {Sol{\'e}, J. and Salembier, P.} } @conference {cEugenio04, title = {An automated multisensor satellite imagery registration technique based on the optimization of contour features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium 2004}, year = {2004}, pages = {1410{\textendash}1413}, author = {F. Eugenio and Marcello, J. and Marqu{\'e}s, F.} } @conference {cCasas03, title = {Advanced content-based semantic scene analysis and information retrieval: the Schema Project}, booktitle = {4th European Workshop on Image Analysis for Multimedia Interactive Services_x000D_}, year = {2003}, pages = {43{\textendash}45}, author = {Casas, J. and Kompatsiaris, I. and Strintzis, M.} } @phdthesis {dLlach03, title = {Analysis of Video Sequences for Content Description. Table of Contents \& Index Creation and Scene Classification}, year = {2003}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Llach, J.}, editor = {Salembier, P.} } @article {aEugenio03, title = {Automatic satellite image georeferencing using a contour matching approach}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {41}, number = {12}, year = {2003}, pages = {2869{\textendash}2880}, issn = {0196-2892}, author = {F. Eugenio and Marqu{\'e}s, F.} } @conference {cEugenio03a, title = {Automatic structures detection and spatial registration using multisensor satellite imagery}, booktitle = {Proceedings of the International Geoscience and Remote Sensing Symposium,}, year = {2003}, pages = {1038{\textendash}1040}, author = {F. Eugenio and Rovaris, E. and Marcello, J. and Marqu{\'e}s, F.} } @conference {cMarcelo02, title = {Automatic feature extraction from multisensorial oceanographic imagery}, booktitle = {International Geoscience and Remote Sensing Symposium, 2002. IGARSS {\textquoteright}02. 2002 IEEE}, year = {2002}, pages = {4{\textendash}8}, isbn = {0-7803-7536-0}, author = {Marcello, J. and Marqu{\'e}s, F. and F. Eugenio} } @conference {cEugenio00a, title = {Accurate and automatic NOAA-AVHRR image navigation using a global contour matching approach}, booktitle = {International Geoscience and remote Sensing Symposium}, year = {2000}, pages = {639{\textendash}642}, isbn = {0-7803-6362-0}, author = {F. Eugenio and Marqu{\'e}s, F. and G{\'o}mez, L. and Suarez, E. and Rovaris, E.} } @article {pSalembier99a, title = {Analysis of Video Sequence. Method for Defining the Structure of a Video Sequence. Part II}, number = {994026615-}, year = {1999}, type = {Invention patent}, author = {Llach, J. and Salembier, P.} } @article {pSalembier99, title = {Analysis of Video Sequence. Method for Defining the Structure of a Video Sequence. Part I}, number = {994025948-}, year = {1999}, type = {Invention patent}, author = {Llach, J. and Salembier, P.} } @conference {cLlach99, title = {Analysis of video sequences : table of content and index creation}, booktitle = {International Workshop on Very Low Bitrate Video, VLBV{\textquoteright}99}, year = {1999}, pages = {51{\textendash}54}, address = {Kobe, Japan}, author = {Llach, J. and Salembier, P.} } @conference {cPardas99, title = {Automatic Face Analysis for Model Calibration}, booktitle = {International Workshop on Synthetic and natural hybrid coding and three dimensional imaging}, year = {1999}, pages = {12{\textendash}15}, isbn = {0-7695-0750-6}, author = {M. Pard{\`a}s} } @article {aSalembier98, title = {Antiextensive connected operators for image and sequence processing}, journal = {IEEE transactions on image processing}, volume = {7}, number = {4}, year = {1998}, pages = {555{\textendash}570}, abstract = {

This paper deals with a class of morphological operators called connected operators. These operators filter the signal by merging its flat zones. As a result, they do not create any new contours and are very attractive for filtering tasks where the contour information has to be preserved. This paper shows that connected operators work implicitly on a structured representation of the image made of flat zones. The max-tree is proposed as a suitable and efficient structure to deal with the processing steps involved in antiextensive connected operators. A formal definition of the various processing steps involved in the operator is proposed and, as a result, several lines of generalization are developed. First, the notion of connectivity and its definition are analyzed. Several modifications of the traditional approach are presented. They lead to connected operators that are able to deal with texture. They also allow the definition of connected operators with less leakage than the classical ones. Second, a set of simplification criteria are proposed and discussed. They lead to simplicity-, entropy-, and motion-oriented operators. The problem of using a nonincreasing criterion is analyzed. Its solution is formulated as an optimization problem that can be very efficiently solved by a Viterbi (1979) algorithm. Finally, several implementation issues are discussed showing that these operators can be very efficiently implemented.

}, issn = {1057-7149}, doi = {10.1109/83.663500}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=663500}, author = {Salembier, P. and Albert Oliveras and Garrido, L.} } @conference {cVidal98, title = {Autentificacion digital de imagen, marcado y detecci{\'o}n {\'o}ptimos}, booktitle = {XIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1998}, pages = {105{\textendash}106}, isbn = {84-89654-12-3}, author = {Vidal, J. and Elisa Sayrol and Lagunas, M.} } @conference {cSalembier98, title = {Auto-dual connected operators based on iterative merging algorithms}, booktitle = {International Symposium on Mathematical Morphology and its applications to image and signal processing, ISMM 1998}, year = {1998}, pages = {183{\textendash}190}, address = {Amsterdam, The Netherlands}, isbn = {1522-4880}, author = {Salembier, P. and Garrido, L. and Garc{\'\i}a, D.} } @article {aMarcotegui97a, title = {Allowing content-based functionalities in segmentation-based coding schemes}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {398{\textendash}407}, issn = {0003-4347}, author = {Marcotegui, B. and Marqu{\'e}s, F. and Meyer, F.} } @conference {cGarrido97a, title = {Anti-extensive Connected Operators with Application to Image Sequences}, booktitle = {VII Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1997}, pages = {151{\textendash}156}, address = {Barcelona, Spain}, isbn = {84-922529-0-1}, author = {Garrido, L. and Salembier, P. and Albert Oliveras} } @conference {cSalembier96d, title = {Active mesh coding and rate distortion theory}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, address = {Lausanne, Switzerland}, isbn = {0-7803-7043-0}, author = {Salembier, P. and Mart{\'\i}, E. and M. Pard{\`a}s} } @inbook {bMarques94a, title = {AUTOMATIC QUANTIFICATION OF SPINE PARAMETERS FROM X-RAY IMAGES BY MEANS OF MORPHOLOGICAL TOOLS.}, booktitle = {Mathematical morphology and its applications to image processing}, year = {1994}, pages = {330{\textendash}340}, isbn = {0-7923-3093-5}, author = {Marqu{\'e}s, F.} } @conference {cSalembier93a, title = {Application of mathematical morphology to picture coding}, booktitle = {General Assembly of URSI, 1993}, year = {1993}, address = {Kyoto, Japan}, author = {Salembier, P.} } @conference {cSalembier92, title = {Adaptation of grey level structuring elements for morphological filters with application to shape detection}, booktitle = {VI European Signal Processing Conference, EUSIPCO{\textquoteright}92}, year = {1992}, pages = {1137{\textendash}1140}, address = {Brussels, Belgium}, isbn = {0-7803-8485-7}, author = {Salembier, P.} } @article {aSalembier92b, title = {Adaptive rank order based filters}, journal = {Signal processing}, volume = {27}, number = {1}, year = {1992}, pages = {1{\textendash}25}, issn = {0165-1684}, author = {Salembier, P.} } @inbook {bGasull92, title = {Analysis and optimization of the K-Means algorithm for remote sensing applications}, booktitle = {Pattern recognition and image analysis}, year = {1992}, pages = {0{\textendash}0}, isbn = {9810208812}, url = {http://cataleg.upc.edu/search~S1*cat/?searchtype=i\&searcharg=+9810208812\&searchscope=1\&SORT=D\&extended=0\&SUBMIT=Cerca\&searchlimits=\&searchorigarg=t+Proceedings+of++Workshop+on+Network+Robot+Systems+2009}, author = {Gasull, A. and Monte, E. and Torres, L. and Montolio, P. and Marqu{\'e}s, F.} } @conference {cSalembier91, title = {Adaptive morphological multiresolution decomposition}, booktitle = {SPIE Image Algebra and Mathematical Morphology}, year = {1991}, pages = {620{\textendash}631}, address = {San Diego, USA}, author = {Salembier, P. and Jaquenoud, L} } @conference {cGasull91, title = {Analisis de no estacionariedades en la interpolacion de imagenes}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {985{\textendash}989}, isbn = {84-600-7766-7}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cGasull90a, title = {Analisis y optimizacion del algoritmo k-means aplicado a teledeteccion en ima}, booktitle = {IV Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1990}, pages = {17{\textendash}23}, author = {Gasull, A. and Torres, L.} } @article {aSanz90, title = {Aspectos Cl{\'\i}nicos-toxicol{\'o}gicos en los trabajadores de una industria productora de cromatos}, journal = {Annual review of pharmacology and toxicology}, volume = {7}, year = {1990}, pages = {1:13{\textendash}1:20}, issn = {0362-1642}, author = {Sanz, P. and Ribas, B. and Cobo, E. and Gadea, E. and Marqu{\'e}s, F. and Sol{\'e}, E. and Corbella, J.} } @article {pSalembier89, title = {Appareil de D{\'e}codage de Signaux Modul{\'e}s en Fr{\'e}quence}, number = {8900812-}, year = {1989}, type = {Invention patent}, author = {Salembier, P. and Lamnabhi, M.} } @conference {cGasull89a, title = {Automated left ventricular contour extraction and volume calculation form echocardiographic images}, booktitle = {Ultrasonics International 89}, year = {1989}, pages = {1{\textendash}4}, isbn = {SN.}, author = {Gasull, A. and Alquezar, R. and Torres, L. and Sallent, S. and Marqu{\'e}s, F. and Vidal, J. and Sangr{\'a}, E.} } @conference {cGasull89, title = {Automatic left ventricular contour for volume calculation}, booktitle = {Ultrasonics International 89}, year = {1989}, pages = {123{\textendash}126}, isbn = {SN.}, author = {Gasull, A. and Vazquez, G.} } @article {pSalembier88a, title = {Appareil Muni d{\textquoteright}un Dispositif de R{\'e}stitution de la Composante Continue Am{\'e}lior{\'e}}, number = {8814685-}, year = {1988}, type = {Invention patent}, author = {Salembier, P. and Hayet, P.} }