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Author Robert Benavente; C. Alejandro Parraga; Maria Vanrell edit  url
isbn  openurl
Title La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas Type Conference Article
Year 2010 Publication 9th Congreso Nacional del Color Abbreviated Journal  
Volume (down) Issue Pages 92–95  
Keywords Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos  
Abstract En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto.  
Address Alicante (Spain)  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN 978-84-9717-144-1 Medium  
Area Expedition Conference CNC  
Notes CIC Approved no  
Call Number CAT @ cat @ BPV2010 Serial 1327  
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Author Javier Vazquez; Maria Vanrell; Robert Benavente edit  openurl
Title Color names as a constraint for Computer Vision problems Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal  
Volume (down) Issue Pages 324–328  
Keywords  
Abstract Computer Vision Problems are usually ill-posed. Constraining de gamut of possible solutions is then a necessary step. Many constrains for different problems have been developed during years. In this paper, we present a different way of constraining some of these problems: the use of color names. In particular, we will focus on segmentation, representation ans constancy.  
Address Gjovik (Norway)  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference CREATE  
Notes CIC Approved no  
Call Number CAT @ cat @ VVB2010 Serial 1328  
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell edit  openurl
Title Who Painted this Painting? Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal  
Volume (down) Issue Pages 329–333  
Keywords  
Abstract  
Address Gjovik (Norway)  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference CREATE  
Notes CIC Approved no  
Call Number CAT @ cat @ KWV2010 Serial 1329  
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Author Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu edit  doi
isbn  openurl
Title Perceptual color texture codebooks for retrieving in highly diverse texture datasets Type Conference Article
Year 2010 Publication 20th International Conference on Pattern Recognition Abbreviated Journal  
Volume (down) Issue Pages 866–869  
Keywords  
Abstract Color and texture are visual cues of different nature, their integration in a useful visual descriptor is not an obvious step. One way to combine both features is to compute texture descriptors independently on each color channel. A second way is integrate the features at a descriptor level, in this case arises the problem of normalizing both cues. A significant progress in the last years in object recognition has provided the bag-of-words framework that again deals with the problem of feature combination through the definition of vocabularies of visual words. Inspired in this framework, here we present perceptual textons that will allow to fuse color and texture at the level of p-blobs, which is our feature detection step. Feature representation is based on two uniform spaces representing the attributes of the p-blobs. The low-dimensionality of these text on spaces will allow to bypass the usual problems of previous approaches. Firstly, no need for normalization between cues; and secondly, vocabularies are directly obtained from the perceptual properties of text on spaces without any learning step. Our proposal improve current state-of-art of color-texture descriptors in an image retrieval experiment over a highly diverse texture dataset from Corel.  
Address Istanbul (Turkey)  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 1051-4651 ISBN 978-1-4244-7542-1 Medium  
Area Expedition Conference ICPR  
Notes CIC Approved no  
Call Number CAT @ cat @ ASV2010b Serial 1426  
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Author Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga edit   pdf
url  doi
isbn  openurl
Title Saliency Estimation Using a Non-Parametric Low-Level Vision Model Type Conference Article
Year 2011 Publication IEEE conference on Computer Vision and Pattern Recognition Abbreviated Journal  
Volume (down) Issue Pages 433-440  
Keywords Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms  
Abstract Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks.  
Address Colorado Springs  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 1063-6919 ISBN 978-1-4577-0394-2 Medium  
Area Expedition Conference CVPR  
Notes CIC Approved no  
Call Number Admin @ si @ MVO2011 Serial 1757  
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez edit   pdf
url  doi
isbn  openurl
Title Color Attributes for Object Detection Type Conference Article
Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
Volume (down) Issue Pages 3306-3313  
Keywords pedestrian detection  
Abstract State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.
In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-
art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods.		  
Address Providence; Rhode Island; USA;  
Corporate Author Thesis  
Publisher IEEE Xplore Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
Area Expedition Conference CVPR  
Notes ADAS; CIC; Approved no  
Call Number Admin @ si @ KRW2012 Serial 1935  
Permanent link to this record
 
 
Author Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell edit   pdf
url  openurl
Title Portmanteau Vocabularies for Multi-Cue Image Representation Type Conference Article
Year 2011 Publication 25th Annual Conference on Neural Information Processing Systems Abbreviated Journal  
Volume (down) Issue Pages  
Keywords  
Abstract We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference NIPS  
Notes CIC Approved no  
Call Number Admin @ si @ KWB2011 Serial 1865  
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Author Jordi Roca; C. Alejandro Parraga; Maria Vanrell edit  url
openurl 
Title Categorical Focal Colours are Structurally Invariant Under Illuminant Changes Type Conference Article
Year 2011 Publication European Conference on Visual Perception Abbreviated Journal  
Volume (down) Issue Pages 196  
Keywords  
Abstract The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes.  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Perception 40 Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference ECVP  
Notes CIC Approved no  
Call Number Admin @ si @ RPV2011 Serial 1867  
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Author Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell edit   pdf
url  doi
isbn  openurl
Title Names and Shades of Color for Intrinsic Image Estimation Type Conference Article
Year 2012 Publication 25th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
Volume (down) Issue Pages 278-285  
Keywords  
Abstract In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance.  
Address Providence, Rhode Island  
Corporate Author Thesis  
Publisher IEEE Xplore Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN 1063-6919 ISBN 978-1-4673-1226-4 Medium  
Area Expedition Conference CVPR  
Notes CIC Approved no  
Call Number Admin @ si @ SPB2012 Serial 2026  
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Author Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus edit   pdf
openurl 
Title Color Naming Type Book Chapter
Year 2012 Publication Color in Computer Vision: Fundamentals and Applications Abbreviated Journal  
Volume (down) Issue 17 Pages 287-317  
Keywords  
Abstract  
Address  
Corporate Author Thesis  
Publisher John Wiley & Sons, Ltd. Place of Publication Editor Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek  
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 @ WBV2012 Serial 2063  
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Author Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras edit   pdf
doi  openurl
Title Intrinsic Image Evaluation On Synthetic Complex Scenes Type Conference Article
Year 2013 Publication 20th IEEE International Conference on Image Processing Abbreviated Journal  
Volume (down) Issue Pages 285 - 289  
Keywords  
Abstract Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes.		  
Address Melbourne; Australia; September 2013  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference ICIP  
Notes CIC; 600.048; 600.052; 600.051 Approved no  
Call Number Admin @ si @ BSW2013 Serial 2264  
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Author Javier Vazquez; Robert Benavente; Maria Vanrell edit   pdf
url  openurl
Title Naming constraints constancy Type Conference Article
Year 2012 Publication 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision Abbreviated Journal  
Volume (down) Issue Pages  
Keywords  
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].		  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference AV A  
Notes CIC Approved no  
Call Number Admin @ si @ VBV2012 Serial 2131  
Permanent link to this record
 
 
Author Jordi Roca; Maria Vanrell; C. Alejandro Parraga edit  url
isbn  openurl
Title What is constant in colour constancy? Type Conference Article
Year 2012 Publication 6th European Conference on Colour in Graphics, Imaging and Vision Abbreviated Journal  
Volume (down) Issue Pages 337-343  
Keywords  
Abstract Color constancy refers to the ability of the human visual system to stabilize
the color appearance of surfaces under an illuminant change. In this work we studied how the interrelations among nine colors are perceived under illuminant changes, particularly whether they remain stable across 10 different conditions (5 illuminants and 2 backgrounds). To do so we have used a paradigm that measures several colors under an immersive state of adaptation. From our measures we defined a perceptual structure descriptor that is up to 87% stable over all conditions, suggesting that color category features could be used to predict color constancy. This is in agreement with previous results on the stability of border categories [1,2] and with computational color constancy
algorithms [3] for estimating the scene illuminant.		  
Address  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN 9781622767014 Medium  
Area Expedition Conference CGIV  
Notes CIC Approved no  
Call Number RVP2012 Serial 2189  
Permanent link to this record
 
 
Author Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras edit   pdf
doi  openurl
Title The Photometry of Intrinsic Images Type Conference Article
Year 2014 Publication 27th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
Volume (down) Issue Pages 1494-1501  
Keywords  
Abstract Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images.  
Address Columbus; Ohio; USA; June 2014  
Corporate Author Thesis  
Publisher Place of Publication Editor  
Language Summary Language Original Title  
Series Editor Series Title Abbreviated Series Title  
Series Volume Series Issue Edition  
ISSN ISBN Medium  
Area Expedition Conference CVPR  
Notes CIC; 600.052; 600.051; 600.074 Approved no  
Call Number Admin @ si @ SPB2014 Serial 2506  
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