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  • Grup de processament d'imatge i video

     Gasull Llampallas, Antoni; Giro Nieto, Xavier; Marques Acosta, Fernando; Morros Rubió, Josep Ramon; Oliveras Verges, Albert; Pardas Feliu, Montserrat; Ruiz Hidalgo, Javier; Salembier Clairon, Philippe Jean; Sayrol Clols, Elisa; Vilaplana Besler, Veronica; Casas Pla, Josep Ramon
    Competitive project

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  • Procesado de información heterogénea y señales en grafos para Big Data:aplicación en cribado de alto rendimiento,teledetección,multimedia y HCI

     Gasull Llampallas, Antoni; Ruiz Hidalgo, Javier; Giro Nieto, Xavier; Marques Acosta, Fernando; Morros Rubió, Josep Ramon; Oliveras Verges, Albert; Salembier Clairon, Philippe Jean; Sayrol Clols, Elisa; Vilaplana Besler, Veronica; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Competitive project

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  • Region based foreground segmentation combining color and depth sensors via logarithmic opinion pool decision

     Gallego Vila, Jaime; Pardas Feliu, Montserrat
    Journal of visual communication and image representation
    Vol. 25, num. 1, p. 184-194
    DOI: 10.1016/j.jvcir.2013.03.019
    Date of publication: 2013-04-01
    Journal article

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    In this paper we present a novel foreground segmentation system that combines color and depth sensors information to perform a more complete Bayesian segmentation between foreground and background classes. The system shows a combination of spatial-color and spatial-depth region-based models for the foreground as well as color and depth pixel-wise models for the background in a Logarithmic Opinion Pool decision framework used to correctly combine the likelihoods of each model. A posterior enhancement step based on a trimap analysis is also proposed in order to correct the precision errors that the depth sensor introduces. The results presented in this paper show that our system is robust in front of color and depth camouflage problems between the foreground object and the background, and also improves the segmentation in the area of the objects¿ contours by reducing the false positive detections that appear due to the lack of precision of the depth sensors.

  • Foreground objects segmentation for moving camera scenarios based on SCGMM

     Gallego Vila, Jaime; Pardas Feliu, Montserrat; Solano Pallarol, Montse
    DOI: 10.1007/978-3-642-32436-9
    Date of publication: 2013-01-01
    Book chapter

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    In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor sce narios 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

  • Parametric Region-Based Foreround Segmentation in Planar and Multi-View Sequences  Open access

     Gallego Vila, Jaime
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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    La segmentació d'objectes de primer pla en seqüències de vídeo és una important àrea del processat d'imatge que acull gran interès per part de la comunitat científica, ja que possibilita la detecció d'objectes que apareixen en les diferents seqüències en anàlisi, i permet el bon funcionament d'aplicacions d'alt nivell que utilitzen aquesta segmentació obtinguda com a paràmetre d'entrada.Aquesta tesi doctoral titulada Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences detalla, en les pàgines que segueixen, el treball de recerca desenvolupat en aquest camp. % Aquesta àrea del processament d'imatge dedicada a la segmentation d'objectes de primer pla en seqüències vídeo.En aquesta investigació es proposa utilitzar models probabilístics paramètrics a nivell de píxel i a nivell de regió per modelar les diferents classes que participen en la classificació de les regions de la imatge: primer pla, fons i depenent de les seqüències, les regions d'ombra. El desenvolupament es presenta als capítols que segueixen com una generalització de tècniques proposades per a la segmentació d'objectes en seqüències 2D mono-càmera, a l'entorn 3D multicàmera, on s'estableix la cooperació dels diferents sensors que participen en l'enregistrament de l'escena .D'aquesta manera, s'han estudiat diferents escenaris amb l'objectiu de millorar les tècniques de segmentació per a cadascun d'ells:A la primera part de la investigació, es presenten mètodes de segmentació per escenaris mono-càmera. Concretament, es comença tractant la segmentació de primer pla per a càmera estàtica, on es proposa un sistema basat en la classificació Bayesiana entre el model a nivell de píxel per modelar el fons, i els models a nivell de regió creats per modelar els objectes de primer pla i l'ombra que cada un d'ells projecta. La investigació continua amb l'aplicació d'aquest mètode a seqüències gravades mitjançant càmera en moviment, on la classificació Bayesiana es planteja entre les classes de fons i primer pla, ambdues caracteritzades amb models a nivell de regió, amb l'objectiu d'obtenir una segmentació robusta per aquest tipus de seqüències.La segona part de la investigació, es focalitza en l'aplicació d'aquestes tècniques mono-càmera a entorns multi-vista, on diverses càmeres graven conjuntament la mateixa escena. A l'inici d'aquest apartat, es proposa una segmentació de primer pla en seqüències on es combina una càmera de color amb una càmera de profunditat en una classificació que combina els diferents models probabilístics creats per al fons i el primer pla a cada càmera, a partir de la fiabilitat que presenta cada sensor. La investigació continua proposant mètodes de segmentació de primer pla per a entorns multi-vista en sales intel·ligents. En aquests apartats es dissenyen dos sistemes on la segmentació de primer pla i la reconstrucció 3D es combinen per millorar els resultats de cada un d'aquests processos. Les propostes finalitzen amb la presentació d'un sistema de segmentació multicàmera on es centralitza la informació de l'objecte a segmentar mitjançant el disseny d'un model probabilístic 3D.Els resultats presentats en cada un dels sistemes, demostren que la segmentació de primer pla i la reconstrucció 3D es poden veure millorats en aquests escenaris mitjançant l'ús de models probabilístics paramètrics per modelar els objectes a segmentar, introduint així la informació disponible de l'objecte en un marc de classificació Bayesià.

    Foreground segmentation in video sequences is an important area of the image processing that attracts great interest among the scientist community, since it makes possible the detection of the objects that appear in the sequences under analysis, and allows us to achieve a correct performance of high level applications which use foreground segmentation as an initial step. The current Ph.D. thesis entitled Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences details, in the following pages, the research work carried out within this eld. In this investigation, we propose to use parametric probabilistic models at pixel-wise and region level in order to model the di erent classes that are involved in the classi cation process of the di erent regions of the image: foreground, background and, in some sequences, shadow. The development is presented in the following chapters as a generalization of the techniques proposed for objects segmentation in 2D planar sequences to 3D multi-view environment, where we establish a cooperative relationship between all the sensors that are recording the scene. Hence, di erent scenarios have been analyzed in this thesis in order to improve the foreground segmentation techniques: In the first part of this research, we present segmentation methods appropriate for 2D planar scenarios. We start dealing with foreground segmentation in static camera sequences, where a system that combines pixel-wise background model with region-based foreground and shadow models is proposed in a Bayesian classi cation framework. The research continues with the application of this method to moving camera scenarios, where the Bayesian framework is developed between foreground and background classes, both characterized with region-based models, in order to obtain a robust foreground segmentation for this kind of sequences. The second stage of the research is devoted to apply these 2D techniques to multi-view acquisition setups, where several cameras are recording the scene at the same time. At the beginning of this section, we propose a foreground segmentation system for sequences recorded by means of color and depth sensors, which combines di erent probabilistic models created for the background and foreground classes in each one of the views, by taking into account the reliability that each sensor presents. The investigation goes ahead by proposing foreground segregation methods for multi-view smart room scenarios. In these sections, we design two systems where foreground segmentation and 3D reconstruction are combined in order to improve the results of each process. The proposals end with the presentation of a multi-view segmentation system where a foreground probabilistic model is proposed in the 3D space to gather all the object information that appears in the views. The results presented in each one of the proposals show that the foreground segmentation and also the 3D reconstruction can be improved, in these scenarios, by using parametric probabilistic models for modeling the objects to segment, thus introducing the information of the object in a Bayesian classi cation framework.

    La segmentaci on de objetos de primer plano en secuencias de v deo es una importante area del procesado de imagen que despierta gran inter es por parte de la comunidad cient ca, ya que posibilita la detecci on de objetos que aparecen en las diferentes secuencias en an alisis, y permite el buen funcionamiento de aplicaciones de alto nivel que utilizan esta segmentaci on obtenida como par ametro de entrada. La presente tesis doctoral titulada Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences detalla, en las p aginas que siguen, el trabajo de investigaci on desarrollado en este campo. En esta investigaci on se propone utilizar modelos probabil sticos param etricos a nivel de p xel y a nivel de regi on para modelar las diferentes clases que participan en la clasi caci on de las regiones de la imagen: primer plano, fondo y en seg un que secuencias, las regiones de sombra. El desarrollo se presenta en los cap tulos que siguen como una generalizaci on de t ecnicas propuestas para la segmentaci on de objetos en secuencias 2D mono-c amara, al entorno 3D multi-c amara, donde se establece la cooperaci on de los diferentes sensores que participan en la grabaci on de la escena. De esta manera, diferentes escenarios han sido estudiados con el objetivo de mejorar las t ecnicas de segmentaci on para cada uno de ellos: En la primera parte de la investigaci on, se presentan m etodos de segmentaci on para escenarios monoc amara. Concretamente, se comienza tratando la segmentaci on de primer plano para c amara est atica, donde se propone un sistema completo basado en la clasi caci on Bayesiana entre el modelo a nivel de p xel de nido para modelar el fondo, y los modelos a nivel de regi on creados para modelar los objetos de primer plano y la sombra que cada uno de ellos proyecta. La investigaci on prosigue con la aplicaci on de este m etodo a secuencias grabadas mediante c amara en movimiento, donde la clasi caci on Bayesiana se plantea entre las clases de fondo y primer plano, ambas caracterizadas con modelos a nivel de regi on, con el objetivo de obtener una segmentaci on robusta para este tipo de secuencias. La segunda parte de la investigaci on, se centra en la aplicaci on de estas t ecnicas mono-c amara a entornos multi-vista, donde varias c amaras graban conjuntamente la misma escena. Al inicio de dicho apartado, se propone una segmentaci on de primer plano en secuencias donde se combina una c amara de color con una c amara de profundidad en una clasi caci on que combina los diferentes modelos probabil sticos creados para el fondo y el primer plano en cada c amara, a partir de la fi abilidad que presenta cada sensor. La investigaci on prosigue proponiendo m etodos de segmentaci on de primer plano para entornos multi-vista en salas inteligentes. En estos apartados se diseñan dos sistemas donde la segmentaci on de primer plano y la reconstrucci on 3D se combinan para mejorar los resultados de cada uno de estos procesos. Las propuestas fi nalizan con la presentaci on de un sistema de segmentaci on multi-c amara donde se centraliza la informaci on del objeto a segmentar mediante el diseño de un modelo probabil stico 3D. Los resultados presentados en cada uno de los sistemas, demuestran que la segmentacion de primer plano y la reconstrucci on 3D pueden verse mejorados en estos escenarios mediante el uso de modelos probabilisticos param etricos para modelar los objetos a segmentar, introduciendo as la informaci on disponible del objeto en un marco de clasi caci on Bayesiano.

  • Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling

     Gallego Vila, Jaime; Pardas Feliu, Montserrat; Haro Ortega, Gloria
    Pattern recognition letters
    Vol. 33, num. 12, p. 1558-1568
    DOI: 10.1016/j.patrec.2012.05.004
    Date of publication: 2012-09-01
    Journal article

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  • ISRN Machine Vision

     Pardas Feliu, Montserrat
    Collaboration in journals

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  • Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models

     Molina, Javier; Escudero-Viñolo, Marcos; Bescós Cano, Jesús; Signorelo, Alessandro; Pardas Feliu, Montserrat; Ferran, Christian; Marques Acosta, Fernando; Martínez, José Maria
    Machine vision and applications
    Vol. 24, num. 1, p. 187-204
    DOI: 10.1007/s00138-011-0364-6
    Date of publication: 2011-08-06
    Journal article

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    The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.

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    Multi-camera multi-object voxel-based Monte Carlo 3D tracking strategies  Open access

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat; Monte Moreno, Enrique
    Eurasip journal on advances in signal processing
    Vol. 2011, num. 114, p. 1-15
    DOI: 10.1186/1687-6180-2011-114
    Date of publication: 2011-11-23
    Journal article

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    This article presents a new approach to the problem of simultaneous tracking of several people in low-resolution sequences from multiple calibrated cameras. Redundancy among cameras is exploited to generate a discrete 3D colored representation of the scene, being the starting point of the processing chain. We review how the initiation and termination of tracks influences the overall tracker performance, and present a Bayesian approach to efficiently create and destroy tracks. Two Monte Carlo-based schemes adapted to the incoming 3D discrete data are introduced. First, a particle filtering technique is proposed relying on a volume likelihood function taking into account both occupancy and color information. Sparse sampling is presented as an alternative based on a sampling of the surface voxels in order to estimate the centroid of the tracked people. In this case, the likelihood function is based on local neighborhoods computations thus dramatically decreasing the computational load of the algorithm. A discrete 3D re-sampling procedure is introduced to drive these samples along time. Multiple targets are tracked by means of multiple filters, and interaction among them is modeled through a 3D blocking scheme. Tests over CLEAR-annotated database yield quantitative results showing the effectiveness of the proposed algorithms in indoor scenarios, and a fair comparison with other state-of-the-art algorithms is presented. We also consider the real-time performance of the proposed algorithm.

  • Human motion capture using scalable body models

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Computer vision and image understanding
    Vol. 115, num. 10, p. 1363-1374
    DOI: 10.1016/j.cviu.2011.06.001
    Date of publication: 2011-10
    Journal article

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    This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. Scalable human body models are introduced as an ordered set of articulated models fulfilling an inclusive hierarchy. The concept of annealing is applied to derive a generic particle filtering scheme able to perform a sequential filtering over the set of models contained in the scalable human body model. Two annealing loops are employed, the standard likelihood annealing and the newly introduced structural annealing, leading to a robust, progressive and efficient analysis of the input data. The validity of this scheme is tested by performing markerless human motion capture in a multi-camera environment employing the standard HumanEva annotated datasets. Finally, quantitative results are presented and compared with other existing HMC techniques.

  • Procesado de vídeo multicámara empleando información de la escena: aplicación a eventos deportivos, interacción visual y 3DTV

     Gasull Llampallas, Antoni; Oliveras Verges, Albert; Giro Nieto, Xavier; Vilaplana Besler, Veronica; Ruiz Hidalgo, Javier; Morros Rubió, Josep Ramon; Pardas Feliu, Montserrat; Sayrol Clols, Elisa; Marques Acosta, Fernando; Salembier Clairon, Philippe Jean; Casas Pla, Josep Ramon
    Competitive project

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  • Real-time upper body tracking with online initialization using a range sensor

     Lopez Mendez, Adolfo; Alcoverro Vidal, Marcel; Pardas Feliu, Montserrat; Casas Pla, Josep Ramon
    International Conference on Computer Vision
    p. 391-398
    DOI: 10.1109/ICCVW.2011.6130268
    Presentation's date: 2011-11-07
    Presentation of work at congresses

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    We present a novel method for upper body pose estimation with online initialization of pose and the anthropometric profile. Our method is based on a Hierarchical Particle Filter that defines its likelihood function with a single view depth map provided by a range sensor. We use Connected Operators on range data to detect hand and head candidates that are used to enrich the Particle filter’s proposal distribution, but also to perform an automated initialization of the pose and the anthropometric profile estimation. A GPU based implementation of the likelihood evaluation yields real-time performance. Experimental validation of the proposed algorithm and the real-time implementation are provided, as well as a comparison with the recently released OpenNI tracker for the Kinect sensor.

  • A real-time body tracking system for smart rooms

     Alcoverro Vidal, Marcel; Lopez Mendez, Adolfo; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    IEEE International Conference on Multimedia and Expo
    p. 1-6
    DOI: 10.1109/ICME.2011.6011847
    Presentation's date: 2011-07-12
    Presentation of work at congresses

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    We present a real-time human body tracking system for a single user in a Smart Room scenario. In this paper we propose a novel system that involves a silhouette-based cost function using variable windows, a hierarchical optimization method, parallel implementations of pixel-based algorithms and efficient usage of a low-cost hardware structure. Results in a Smart Room setup are presented.

  • Work in progress - Cooperative and competitive projects for engaging students in advanced ICT subjects

     Pardas Feliu, Montserrat; Bonafonte Cavez, Antonio Jesus
    Annual Frontiers in Education Conference
    p. 1-3
    DOI: 10.1109/FIE.2011.6143032
    Presentation's date: 2011
    Presentation of work at congresses

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    In this paper we present a specific kind of projects that can be used for project-based learning in engineering subjects. The subjects must combine lectures with projects, in order to provide the technical competences together with additional skills such as teamwork learning, oral and written communication skills and application of theory to practice. The projects proposed consist on improving an elemental baseline system. The system is decomposed in modules that correspond to the topics that have been learnt during the lectures. For improving the system, the class is divided in groups and each group has to propose, implement, assess and report a better system. In order to be able to improve the system with a limited amount of time and effort the students need to make a coherent proposal and split the project in several tasks that are usually developed by one or two students. The students within a group cooperate to achieve a better system, but groups compete for the best results. We have already implemented this kind of project in a Speech Processing course and we plan to apply it in a Video Coding course.

  • Foreground objects segmentation for moving camera scenarios based on SCGMM

     Gallego Vila, Jaime; Pardas Feliu, Montserrat; Solano, Montse
    International Workshop on Computational Intelligence for Multimedia Understanding
    p. 195-206
    DOI: 10.1007/978-3-642-32436-9_17
    Presentation's date: 2011
    Presentation of work at congresses

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  • Joint multi-view foreground segmentation and 3D reconstruction with tolerance loop

     Gallego, Jaime; Salvador, Jordi; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    IEEE International Conference on Image Processing
    p. 997-1000
    DOI: 10.1109/ICIP.2011.6116731
    Presentation's date: 2011-09-12
    Presentation of work at congresses

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  • Connected Operators on 3D data for human body analysis

     Alcoverro Vidal, Marcel; Lopez Mendez, Adolfo; Pardas Feliu, Montserrat; Casas Pla, Josep Ramon
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 9-14
    DOI: 10.1109/CVPRW.2011.5981772
    Presentation's date: 2011
    Presentation of work at congresses

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

  • Approximate partitioning of observations in hierarchical particle filter body tracking

     Lopez Mendez, Adolfo; Alcoverro Vidal, Marcel; Pardas Feliu, Montserrat; Casas Pla, Josep Ramon
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 19-24
    DOI: 10.1109/CVPRW.2011.5981712
    Presentation's date: 2011-06-20
    Presentation of work at congresses

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    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.

  • Skeleton and shape adjustment and tracking in multicamera environments

     Alcoverro Vidal, Marcel; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Lecture notes in computer science
    Vol. 6169/2010, p. 88-97
    DOI: 10.1007/978-3-642-14061-7_9
    Date of publication: 2010-07
    Journal article

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    In this paper we present a method for automatic body model adjustment and motion tracking in multicamera environments.We introduce a set of shape deformation parameters based on linear blend skinning, that allow a deformation related to the scaling of the distinct bones of the body model skeleton, and a deformation in the radial direction of a bone. The adjustment of a generic body model to a specific subject is achieved by the estimation of those shape deformation parameters. This estimation combines a local optimization method and hierarchical particle filtering, and uses an efficient cost function based on foreground silhouettes using GPU. This estimation takes into account anthropometric constraints by using a rejection sampling method of propagation of particles. We propose a hierarchical particle filtering method for motion tracking using the adjusted model. We show accurate model adjustment and tracking for distinct subjects in a 5 cameras set up.

  • Shape from incomplete silhouettes based on the reprojection error

     Haro Ortega, Gloria; Pardas Feliu, Montserrat
    Image and vision computing
    Vol. 28, num. 9, p. 1354-1368
    DOI: 10.1016/j.imavis.2010.01.016
    Date of publication: 2010-09
    Journal article

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  • Improved 3D reconstruction in smart-room environments using ToF imaging

     Gudmundsson, Sigurjon Arni; Pardas Feliu, Montserrat; Casas Pla, Josep Ramon; Sveinsson, Johannes; Aanaes, Henrik; Larsen, Rasmus
    Computer vision and image understanding
    Vol. 114, num. 12, p. 1376-1384
    DOI: 10.1016/j.cviu.2010.07.011
    Date of publication: 2010-12
    Journal article

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  • Model-based hand gesture tracking in ToF image sequences

     Gudmundsson, Sigurjon; Sveinsson, Johannes; Pardas Feliu, Montserrat; Aanaes, Henrik; Larsen, Ramus
    Lecture notes in computer science
    Vol. 6169/2010, p. 118-127
    DOI: 10.1007/978-3-642-14061-7_12
    Date of publication: 2010-07
    Journal article

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    This paper presents a Time-of-Flight (ToF) camera based system for hand motion and gesture tracking. A 27 degree of freedom (DOF) hand model is constructed and fleshed out by ellipsoids. This allows the synthesis of range images of the model through projective geometry. The hand pose is then tracked with a particle filter by statistically measuring the hypothetical pose against the ToF input image; where the inside/outside alignment of the hand pixels and the depth differences serve as classifying metrics. The high DOF tracking problem for the particle filter is addressed by reducing the high dimensionality of the joint angle space to a low dimensional space via Principal Component Analysis (PCA). The basis vectors are learned from a few basic model configurations and the transformations between these poses. This results in a system capable of practical hand tracking in a restricted gesture configuration space.

  • Marker-based human motion capture in multi-view sequences

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Eurasip journal on advances in signal processing
    Vol. 2010, num. Article ID 105476, p. 1-11
    DOI: 10.1155/2010/105476
    Date of publication: 2010-12
    Journal article

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  • FascinatE: Format-Agnostic SCript-based INterAcTive Experience

     Casas Pla, Josep Ramon; Ruiz Hidalgo, Javier; Suau Cuadros, Xavier; Morros Rubió, Josep Ramon; Pardas Feliu, Montserrat; Marques Acosta, Fernando
    Competitive project

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  • Adquisición multicámara para Free Viewpoint Video (MC4FVV)

     Pardas Feliu, Montserrat; Giro Nieto, Xavier; Vilaplana Besler, Veronica; Ruiz Hidalgo, Javier; Morros Rubió, Josep Ramon; Salembier Clairon, Philippe Jean; Marques Acosta, Fernando; Gasull Llampallas, Antoni; Oliveras Verges, Albert; Sayrol Clols, Elisa; Casas Pla, Josep Ramon
    Competitive project

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  • Format-Agnostic SCript-based INterAcTive Experience

     Casas Pla, Josep Ramon; Morros Rubió, Josep Ramon; Marques Acosta, Fernando; Pardas Feliu, Montserrat; Ruiz Hidalgo, Javier
    Competitive project

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  • Skeleton and shape adjustment and tracking in multicamera environments

     Alcoverro Vidal, Marcel; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Conference on Articulated Motion and Deformable Objects
    p. 88-97
    DOI: 10.1007/978-3-642-14061-7_9
    Presentation's date: 2010-07-07
    Presentation of work at congresses

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    Spatio-temporal alignment and hyperspherical radon transform for 3D gait recognition in multi-view environments  Open access

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 116-121
    DOI: 10.1109/CVPRW.2010.5544615
    Presentation's date: 2010-06
    Presentation of work at congresses

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    This paper presents a view-invariant approach to gait recognition in multi-camera scenarios exploiting a joint spatio-temporal data representation and analysis. First, multi-view information is employed to generate a 3D voxel reconstruction of the scene under study. The analyzed subject is tracked and its centroid and orientation allow recentering and aligning the volume associated to it, thus obtaining a representation invariant to translation, rotation and scaling. Temporal periodicity of the walking cycle is extracted to align the input data in the time domain. Finally, Hyperspherical Radon Transform is presented as an efficient tool to obtain features from spatio-temporal gait templates for classification purposes. Experimental results prove the validity and robustness of the proposed method for gait recognition tasks with several covariates.

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    Real-time 3D multi-person tracking using Monte Carlo surface sampling  Open access

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    IEEE Conference on Computer Vision and Pattern Recognition
    p. 40-46
    DOI: 10.1109/CVPRW.2010.5543734
    Presentation's date: 2010-06
    Presentation of work at congresses

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    The current paper presents a low-complexity approach to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Redundancy among cameras is exploited to generate a discrete 3D colored representation of the scene. The proposed filtering technique estimates the centroid of a target using only a sparse set of points placed on its surface and making this set evolve along time based on the seminal particle filtering principle. In this case, the likelihood function is based on local neighborhoods computations thus drastically decreasing the computational load of the algorithm. In order to handle multiple interacting targets, a separate filter is assigned to each subject in the scenario while a blocking scheme is employed to model their interactions. Tests over a standard annotated dataset yield quantitative results showing the effectiveness of the proposed technique in both accuracy and real-time performance.

  • Enhanced bayesian foreground segmentation using brightness and color distortion region-based model for shadow removal

     Gallego Vila, Jaime; Pardas Feliu, Montserrat
    IEEE International Conference on Image Processing
    p. 3449-3452
    DOI: 10.1109/ICIP.2010.5653897
    Presentation's date: 2010-09
    Presentation of work at congresses

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  • Multi-Person Tracking Strategies Based on Voxel Analysis

     Casas Pla, Josep Ramon; Canton Ferrer, Cristian; Salvador, J; Pardas Feliu, Montserrat
    Date of publication: 2009-01
    Book chapter

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  • Head Orientation Estimation using Particle Filtering in Multiview Scenarios

     Casas Pla, Josep Ramon; Canton Ferrer, Cristian; Pardas Feliu, Montserrat
    Date of publication: 2009-01
    Book chapter

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  • Trajectory tree as an object-oriented hierarchical representation for video

     Chang Dorea, Camilo; Pardas Feliu, Montserrat; Marques Acosta, Fernando
    IEEE transactions on circuits and systems for video technology
    Vol. 19, num. 4, p. 547-560
    Date of publication: 2009-04
    Journal article

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  • Human motion capture with scalable body models.

     Canton Ferrer, Cristian
    Department of Signal Theory and Communications, Universitat Politècnica de Catalunya
    Theses

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  • Image and video processing tools for HCI

     Canton Ferrer, Cristian; Pardas Feliu, Montserrat; Vilaplana Besler, Veronica
    Date of publication: 2009
    Book chapter

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  • Activity classification

     Nickel, Kai; Pardas Feliu, Montserrat; Stiefelhagen, Rainer; Canton Ferrer, Cristian; Landabaso Diaz, Jose Luis; Casas Pla, Josep Ramon
    DOI: 10.1007/978-1-84882-054-8_3
    Date of publication: 2009-05-31
    Book chapter

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    One of the most basic building blocks for the understanding of human actions and interactions is the accurate detection and tracking of persons in a scene. In constrained scenarios involving at most one subject, or in situations where persons can be confined to a controlled monitoring space or required to wear markers, sensors, or microphones, these tasks can be solved with relative ease. However, when accurate localization and tracking have to be performed in an unobtrusive or discreet fashion, using only distantly placed microphones and cameras, in a variety of natural and uncontrolled scenarios, the challenges posed are much greater. The problems faced by video analysis are those of poor or uneven illumination, low resolution, clutter or occlusion, unclean backgrounds, and multiple moving and uncooperative users that are not always easily distinguishable.

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    Towards a low cost multi-camera marker based human motion capture system  Open access

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    IEEE International Conference on Image Processing
    p. 2581-2584
    DOI: 10.1109/ICIP.2009.5413915
    Presentation's date: 2009-11-09
    Presentation of work at congresses

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    This paper presents a low cost real-time alternative to available commercial human motion capture systems. First, a set of distinguishable markers are placed on several human body landmarks and the scene is captured by a number of calibrated and synchronized cameras. In order to establish a physical relation among markers, a human body model (HBM) is defined. Markers are detected on all camera views and delivered as the input of an annealed particle filter scheme where every particle encodes an instance of the pose of the HBM to be estimated. Likelihood between particles and input data is performed through the generalized symmetric epipolar distance and kinematic constrains are enforced in the propagation step towards avoiding impossible poses. Tests over the HumanEva annotated dataset yield quantitative results showing the effectiveness of the proposed algorithm. Results over sequences involving fast and complex motions are also presented.

  • Visual hull reconstruction algorithms comparison: towards robustness to silhouette errors

     Alcoverro Vidal, Marcel; Pardas Feliu, Montserrat
    International Conference on Computer Vision Theory and Applications
    p. 464-469
    Presentation's date: 2009-02
    Presentation of work at congresses

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    Voxel based annealed particle filtering for markerless 3D articulated motion capture  Open access

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    3DTV Conference
    p. 1-4
    DOI: 10.1109/3DTV.2009.5069645
    Presentation's date: 2009-05-06
    Presentation of work at congresses

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    This paper presents a view-independent approach to markerless human motion capture in low resolution sequences from multiple calibrated and synchronized cameras. Redundancy among cameras is exploited to generate a 3D voxelized representation of the scene and a human body model (HBM) is introduced towards analyzing these data. An annealed particle filtering scheme where every particle encodes an instance of the pose of the HBM is employed. Likelihood between particles and input data is performed using occupancy and surface information and kinematic constrains are imposed in the propagation step towards avoiding impossible poses. Test over the HumanEva annotated dataset yield quantitative results showing the effectiveness of the proposed algorithm.

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    Bayesian foreground segmentation and tracking using pixel-wise background model and region-based foreground model  Open access

     Gallego Vila, Jaime; Pardas Feliu, Montserrat; Haro Ortega, Gloria
    IEEE International Conference on Image Processing
    p. 3205-3208
    DOI: 10.1109/ICIP.2009.5414380
    Presentation's date: 2009-11
    Presentation of work at congresses

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    In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/ background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground is modeled using a Gaussian Mixture Model with feature vectors consisting of the spatial (x, y) and colour (r, g, b) components. The spatial components of this model are updated using the Expectation Maximization algorithm after the classification of each frame. The background model is formulated in the 5 dimensional feature space in order to be able to apply a Maximum A Posteriori framework for the classification. The classification is done using a graph cut algorithm that allows taking into account neighborhood information. The results presented in the paper show the improvement of the system in situations where the foreground objects have similar colors to those of the background.

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    3D shape from multi-camera views by error projection minimization  Open access

     Haro Ortega, Gloria; Pardas Feliu, Montserrat
    Workshop on Image Analysis for Multimedia Interactive Services
    p. 250-253
    DOI: 10.1109/WIAMIS.2009.5031480
    Presentation's date: 2009-05-08
    Presentation of work at congresses

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    Traditional shape from silhouette methods compute the 3D shape as the intersection of the back-projected silhouettes in the 3D space, the so called visual hull. However, silhouettes that have been obtained with background subtraction techniques often present miss-detection errors (produced by false negatives or occlusions) which produce incomplete 3D shapes. Our approach deals with miss-detections and noise in the silhouettes. We recover the voxel occupancy which describes the 3D shape by minimizing an energy based on an approximation of the error between the shape 2D projections and the silhouettes. The energy also includes regularization and takes into account the visibility of the voxels in each view in order to handle self-occlusions.

  • Revisión del estado del arte 2007

     Ruiz Hidalgo, Javier; Félix, Sainz; Antonio, Albiol; Alberto, Albiol; Marques Acosta, Fernando; Casas Pla, Josep Ramon; Morros Rubió, Josep Ramon; Canton Ferrer, Cristian; Pardas Feliu, Montserrat; Batalle, Dafnis Demian Bola
    Date: 2008-01
    Report

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  • Desarrollo de los primeros algoritmos de indexación

     Calderero, Felipe; Casas Pla, Josep Ramon; Gasull Llampallas, Antoni; Giro Nieto, Xavier; Miriam, Leon; Marques Acosta, Fernando; Pardas Feliu, Montserrat; Jordi, Pont; Salembier Clairon, Philippe Jean; Vilaplana Besler, Veronica; Morros Rubió, Josep Ramon
    Date: 2008-12
    Report

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  • Edge Projections for Eye Localization

     Turkan, M; Pardas Feliu, Montserrat; Cetin, E
    Optical engineering
    Vol. 47, num. 4, p. 1-6
    DOI: 10.1117/1.2902437
    Date of publication: 2008-04
    Journal article

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  • A Unified Framework for Consistent 2D/3D Foreground Object Detection

     Landabaso, J L; Pardas Feliu, Montserrat
    IEEE transactions on circuits and systems for video technology
    Vol. 18, num. 8, p. 1040-1051
    DOI: 10.1109/TCSVT.2008.928219
    Date of publication: 2008-08
    Journal article

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    Audiovisual head orientation estimation with particle filtering in multisensor scenarios  Open access

     Canton Ferrer, Cristian; Segura, C; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat; Hernando Pericas, Francisco Javier
    Eurasip journal on advances in signal processing
    Vol. 2008, p. 1-12
    DOI: 10.1155/2008/276846
    Date of publication: 2008-01
    Journal article

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    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.

  • Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Lecture notes in computer science
    Vol. 5098, p. 82-91
    Date of publication: 2008-07
    Journal article

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  • Shape from inconsistent silhouette

     Landabaso, J L; Pardas Feliu, Montserrat; Casas Pla, Josep Ramon
    Computer vision and image understanding
    Vol. 112, num. 2, p. 210-224
    Date of publication: 2008-11
    Journal article

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  • Multi-person tracking strategies based on voxel analysis

     Canton Ferrer, Cristian; Salvador, J; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Lecture notes in computer science
    Vol. 4625, p. 91-103
    DOI: 10.1007/978-3-540-68585-2_7
    Date of publication: 2008-06
    Journal article

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    This paper presents two approaches to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited to generate a discrete 3D binary representation of the foreground objects in the scene. Color information obtained from a zenithal camera view is added to this 3D information. The first tracking approach implements heuristic association rules between blobs labelled according to spatiotemporal connectivity criteria. Association rules are based on a cost function which considers their placement and color histogram. In the second approach, a particle filtering scheme adapted to the incoming 3D discrete data is proposed. A volume likelihood function and a discrete 3D re-sampling procedure are introduced to evaluate and drive particles. Multiple targets are tracked by means of multiple particle filters and interaction among them is modeled through a 3D blocking scheme. Evaluation over the CLEAR 2007 database yields quantitative results assessing the performance of the proposed algorithm for indoor scenarios.

  • Head Orientation Estimation Using Particle Filtering in Multiview Scenarios

     Canton Ferrer, Cristian; Casas Pla, Josep Ramon; Pardas Feliu, Montserrat
    Lecture notes in computer science
    Vol. 4625, p. 317-327
    Date of publication: 2008-06
    Journal article

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