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Jose Carlos Rubio, Joan Serrat, & Antonio Lopez. (2012). Multiple target tracking and identity linking under split, merge and occlusion of targets and observations. In 1st International Conference on Pattern Recognition Applications and Methods.
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Ekaterina Zaytseva, & Jordi Vitria. (2012). A search based approach to non maximum suppression in face detection. In 19th IEEE International Conference on Image Processing.
Abstract: Poster
paper TA.P5.12
Face detectors typically produce a large number of false positives and this leads to the need to have a further non maximum suppression stage to eliminate multiple and spurious responses. This stage is based on considering spatial heuristics: true positive responses are selected by implicitly considering several restrictions on the spatial distribution of detector responses in natural images. In this paper we analyze the limitations of this approach and propose an efficient search method to overcome them. Results show how the application of this new non-maximum suppression approach to a simple face detector boosts its performance to state of the art results.
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Sergio Escalera, Josep Moya, Laura Igual, Veronica Violant, & Maria Teresa Anguera. (2012). Análisis Comportamental Automatizado de TDAH: la Influencia de la Variable Motivación. In IPSI – Cosmocaixa, Jornadas "Empremtes del present, efectes en la psicoanàlisi, la cultura i la societat.
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Laura Igual, Joan Carles Soliva, Antonio Hernandez, Sergio Escalera, Oscar Vilarroya, & Petia Radeva. (2012). A Supervised Graph-cut Deformable Model for Brain MRI Segmentation. Deformation models: tracking, animation and applications. In Computational Vision and Biomechanics. LNCS. Springer Netherlands.
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Angel Sappa, & George A. Triantafyllid. (2012). Computer Graphics and Imaging.
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Theo Gevers, Arjan Gijsenij, Joost Van de Weijer, & J.M. Geusebroek. (2012). Color in Computer Vision: Fundamentals and Applications. The Wiley-IS&T Series in Imaging Science and Technology.
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Michal Drozdzal, Petia Radeva, Santiago Segui, Laura Igual, Carolina Malagelada, Fernando Azpiroz, et al. (2012). System and method for automatic detection of in vivo contraction video sequences.
Abstract: Publication date: 2012/3/8
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Marçal Rusiñol, Lluis Pere de las Heras, Joan Mas, Oriol Ramos Terrades, Dimosthenis Karatzas, Anjan Dutta, et al. (2012). CVC-UAB's participation in the Flowchart Recognition Task of CLEF-IP 2012. In Conference and Labs of the Evaluation Forum.
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Antonio Hernandez, Miguel Angel Bautista, Xavier Perez Sala, Victor Ponce, Xavier Baro, Oriol Pujol, et al. (2012). BoVDW: Bag-of-Visual-and-Depth-Words for Gesture Recognition. In 21st International Conference on Pattern Recognition.
Abstract: We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model.
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Javier Vazquez, Robert Benavente, & Maria Vanrell. (2012). Naming constraints constancy. In 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision.
Abstract: Different studies have shown that languages from industrialized cultures
share a set of 11 basic colour terms: red, green, blue, yellow, pink, purple, brown, orange, black, white, and grey (Berlin & Kay, 1969, Basic Color Terms, University of California Press)( Kay & Regier, 2003, PNAS, 100, 9085-9089). Some of these studies have also reported the best representatives or focal values of each colour (Boynton and Olson, 1990, Vision Res. 30,1311–1317), (Sturges and Whitfield, 1995, CRA, 20:6, 364–376). Some further studies have provided us with fuzzy datasets for color naming by asking human observers to rate colours in terms of membership values (Benavente -et al-, 2006, CRA. 31:1, 48–56,). Recently, a computational model based on these human ratings has been developed (Benavente -et al-, 2008, JOSA-A, 25:10, 2582-2593). This computational model follows a fuzzy approach to assign a colour name to a particular RGB value. For example, a pixel with a value (255,0,0) will be named 'red' with membership 1, while a cyan pixel with a RGB value of (0, 200, 200) will be considered to be 0.5 green and 0.5 blue. In this work, we show how this colour naming paradigm can be applied to different computer vision tasks. In particular, we report results in colour constancy (Vazquez-Corral -et al-, 2012, IEEE TIP, in press) showing that the classical constraints on either illumination or surface reflectance can be substituted by
the statistical properties encoded in the colour names. [Supported by projects TIN2010-21771-C02-1, CSD2007-00018].
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Xavier Otazu, Olivier Penacchio, & Laura Dempere-Marco. (2012). An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction. In 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision.
Abstract: Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here.
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2012). Text/graphic separation using a sparse representation with multi-learned dictionaries. In 21st International Conference on Pattern Recognition.
Abstract: In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds.
Keywords: Graphics Recognition; Layout Analysis; Document Understandin
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Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2012). Noise suppression over bi-level graphical documents using a sparse representation. In Colloque International Francophone sur l'Écrit et le Document.
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Adriana Romero, Simeon Petkov, Carlo Gatta, M.Sabate, & Petia Radeva. (2012). Efficient automatic segmentation of vessels. In 16th Conference on Medical Image Understanding and Analysis.
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Francesco Ciompi. (2012). Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound (Petia Radeva, & Oriol Pujol, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: In this thesis we tackle the problem of automatic characterization of human coronary vessel in Intravascular Ultrasound (IVUS) image modality. The basis for the whole characterization process is machine learning applied to multi-class problems. In all the presented approaches, the Error-Correcting Output Codes (ECOC) framework is used as central element for the design of multi-class classifiers.
Two main topics are tackled in this thesis. First, the automatic detection of the vessel borders is presented. For this purpose, a novel context-aware classifier for multi-class classification of the vessel morphology is presented, namely ECOC-DRF. Based on ECOC-DRF, the lumen border and the media-adventitia border in IVUS are robustly detected by means of a novel holistic approach, achieving an error comparable with inter-observer variability and with state of the art methods.
The two vessel borders define the atheroma area of the vessel. In this area, tissue characterization is required. For this purpose, we present a framework for automatic plaque characterization by processing both texture in IVUS images and spectral information in raw Radio Frequency data. Furthermore, a novel method for fusing in-vivo and in-vitro IVUS data for plaque characterization is presented, namely pSFFS. The method demonstrates to effectively fuse data generating a classifier that improves the tissue characterization in both in-vitro and in-vivo datasets.
A novel method for automatic video summarization in IVUS sequences is also presented. The method aims to detect the key frames of the sequence, i.e., the frames representative of morphological changes. This novel method represents the basis for video summarization in IVUS as well as the markers for the partition of the vessel into morphological and clinically interesting events.
Finally, multi-class learning based on ECOC is applied to lung tissue characterization in Computed Tomography. The novel proposed approach, based on supervised and unsupervised learning, achieves accurate tissue classification on a large and heterogeneous dataset.
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