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Author Ivet Rafegas; Maria Vanrell edit   pdf
url  doi
openurl 
Title Color encoding in biologically-inspired convolutional neural networks Type Journal Article
Year 2018 Publication Vision Research Abbreviated Journal VR  
Volume (down) 151 Issue Pages 7-17  
Keywords Color coding; Computer vision; Deep learning; Convolutional neural networks  
Abstract Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations.  
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Area Expedition Conference  
Notes CIC; 600.051; 600.087 Approved no  
Call Number Admin @ si @RaV2018 Serial 3114  
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Author Jose Carlos Rubio edit  openurl
Title Graph matching based on graphical models with application to vehicle tracking and classification at night Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 144 Issue Pages  
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Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Rub2009 Serial 2398  
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Author Shida Beigpour edit  openurl
Title Physics-based Reflectance Estimation Applied to Recoloring Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 137 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Bei2009 Serial 2396  
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Author Xavier Boix edit  openurl
Title Learning Conditional Random Fields for Stereo Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 136 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Boi2009 Serial 2395  
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Author Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias edit   pdf
url  openurl
Title Understanding trained CNNs by indexing neuron selectivity Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
Volume (down) 136 Issue Pages 318-325  
Keywords  
Abstract The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.  
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Publisher Place of Publication Editor  
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Series Editor Series Title Abbreviated Series Title  
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ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC; 600.087; 600.140; 600.118 Approved no  
Call Number Admin @ si @ RVL2019 Serial 3310  
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Author Olivier Penacchio edit  openurl
Title Relative Density of L, M, S photoreceptors in the Human Retina Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 135 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Pen2009 Serial 2394  
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Author David Augusto Rojas edit  openurl
Title Colouring Local Feature Detection for Matching Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 133 Issue Pages  
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Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Roj2009 Serial 2392  
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Author Naila Murray edit  openurl
Title Perceptual Feature Detection Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 131 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ Mur2009 Serial 2390  
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Author Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio edit  url
doi  openurl
Title A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding Type Journal
Year 2021 Publication SMPTE Motion Imaging Journal Abbreviated Journal SMPTE  
Volume (down) 130 Issue 4 Pages 53 - 65  
Keywords  
Abstract In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components.  
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Area Expedition Conference  
Notes CIC Approved no  
Call Number SCV2021 Serial 3671  
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Author Albert Gordo edit  openurl
Title A Cyclic Page Layout Descriptor for Document Classification & Retrieval Type Report
Year 2009 Publication CVC Technical Report Abbreviated Journal  
Volume (down) 128 Issue Pages  
Keywords  
Abstract  
Address  
Corporate Author Computer Vision Center Thesis Master's thesis  
Publisher Place of Publication Bellaterra, Barcelona Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference  
Notes CIC;DAG Approved no  
Call Number Admin @ si @ Gor2009 Serial 2387  
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Author Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu edit   pdf
url  doi
openurl 
Title Low-dimensional and Comprehensive Color Texture Description Type Journal Article
Year 2012 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
Volume (down) 116 Issue I Pages 54-67  
Keywords  
Abstract Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap		  
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Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 1077-3142 ISBN Medium  
Area Expedition Conference  
Notes CAT;CIC Approved no  
Call Number Admin @ si @ ASV2012 Serial 1827  
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg edit   pdf
doi  openurl
Title Coloring Action Recognition in Still Images Type Journal Article
Year 2013 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
Volume (down) 105 Issue 3 Pages 205-221  
Keywords  
Abstract In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.  
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Corporate Author Thesis  
Publisher Springer US Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 0920-5691 ISBN Medium  
Area Expedition Conference  
Notes CIC; ADAS; 600.057; 600.048 Approved no  
Call Number Admin @ si @ KRW2013 Serial 2285  
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell edit   pdf
url  doi
openurl 
Title Modulating Shape Features by Color Attention for Object Recognition Type Journal Article
Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
Volume (down) 98 Issue 1 Pages 49-64  
Keywords  
Abstract Bag-of-words based image representation is a successful approach for object recognition. Generally, the subsequent stages of the process: feature detection,feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information.  
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Corporate Author Thesis  
Publisher Springer Netherlands Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 0920-5691 ISBN Medium  
Area Expedition Conference  
Notes CIC Approved no  
Call Number Admin @ si @ KWV2012 Serial 1864  
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Author Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez edit   pdf
url  doi
openurl 
Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
Volume (down) 96 Issue 1 Pages 83-102  
Keywords  
Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.		  
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Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 0920-5691 ISBN Medium  
Area Expedition Conference  
Notes ISE;CIC;ADAS Approved no  
Call Number Admin @ si @ BGW2012 Serial 1718  
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Author David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich edit   pdf
doi  openurl
Title Traffic sign recognition for computer vision project-based learning Type Journal Article
Year 2013 Publication IEEE Transactions on Education Abbreviated Journal T-EDUC  
Volume (down) 56 Issue 3 Pages 364-371  
Keywords traffic signs  
Abstract This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback.  
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Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 0018-9359 ISBN Medium  
Area Expedition Conference  
Notes ADAS; CIC Approved no  
Call Number Admin @ si @ GSL2013; ADAS @ adas @ Serial 2160  
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Author C. Alejandro Parraga; Robert Benavente; Maria Vanrell; Ramon Baldrich edit  url
openurl 
Title Psychophysical measurements to model inter-colour regions of colour-naming space Type Journal Article
Year 2009 Publication Journal of Imaging Science and Technology Abbreviated Journal  
Volume (down) 53 Issue 3 Pages 031106 (8 pages)  
Keywords image processing; Analysis  
Abstract JCR Impact Factor 2009: 0.391
In this paper, we present a fuzzy-set of parametric functions which segment the CIE lab space into eleven regions which correspond to the group of common universal categories present in all evolved languages as identified by anthropologists and linguists. The set of functions is intended to model a color-name assignment task by humans and differs from other models in its emphasis on the inter-color boundary regions, which were explicitly measured by means of a psychophysics experiment. In our particular implementation, the CIE lab space was segmented into eleven color categories using a Triple Sigmoid as the fuzzy sets basis, whose parameters are included in this paper. The model’s parameters were adjusted according to the psychophysical results of a yes/no discrimination paradigm where observers had to choose (English) names for isoluminant colors belonging to regions in-between neighboring categories. These colors were presented on a calibrated CRT monitor (14-bit x 3 precision). The experimental results show that inter- color boundary regions are much less defined than expected and color samples other than those near the most representatives are needed to define the position and shape of boundaries between categories. The extended set of model parameters is given as a table.		  
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Publisher Place of Publication Editor  
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Series Editor Series Title Abbreviated Series Title  
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ISSN ISBN Medium  
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Notes CIC Approved no  
Call Number CAT @ cat @ PBV2009 Serial 1157  
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Author Javier Vazquez; C. Alejandro Parraga; Maria Vanrell; Ramon Baldrich edit  doi
openurl 
Title Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset Type Journal Article
Year 2009 Publication Journal of Imaging Science and Technology Abbreviated Journal  
Volume (down) 53 Issue 3 Pages 031105–9  
Keywords  
Abstract The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems. In this paper we propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than a half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral grey sphere, which better copes with the illuminant variations of the scene.  
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Notes CIC Approved no  
Call Number CAT @ cat @ VPV2009a Serial 1171  
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Author Xavier Otazu; Maria Vanrell; C. Alejandro Parraga edit  openurl
Title Multiresolution Wavelet Framework Models Brightness Induction Effects Type Journal
Year 2008 Publication Vision Research Abbreviated Journal VR  
Volume (down) 48 Issue 5 Pages 733–751  
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Publisher Place of Publication Editor  
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Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
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Notes CIC Approved no  
Call Number CAT @ cat @ OVP2008a Serial 927  
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