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| Author | Javier Vazquez; Robert Benavente; Maria Vanrell |
 
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| Title | Naming constraints constancy | Type | Conference Article | |||
| Year | 2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | ||
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| 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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| Area | Expedition | Conference | AV A | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ VBV2012 | Serial | 2131 | |||
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| Author | Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco |
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| Title | An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction | Type | Conference Article | |||
| Year | 2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | ||
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| 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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| Area | Expedition | Conference | AV A | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ OPD2012a | Serial | 2132 | |||
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| Author | Jaime Moreno; Xavier Otazu |
 
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| Title | Image compression algorithm based on Hilbert scanning of embedded quadTrees: an introduction of the Hi-SET coder | Type | Conference Article | |||
| Year | 2011 | Publication | IEEE International Conference on Multimedia and Expo | Abbreviated Journal | ||
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Issue | Pages | 1-6 | |||
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| Abstract | In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. The implementation of the proposed coder is developed for gray-scale and color image compression. Hi-SET compressed images are, on average, 6.20dB better than the ones obtained by other compression techniques based on the Hilbert scanning. Moreover, Hi-SET improves the image quality in 1.39dB and 1.00dB in gray-scale and color compression, respectively, when compared with JPEG2000 coder. | |||||
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| ISSN | 1945-7871 | ISBN | 978-1-61284-348-3 | Medium | ||
| Area | Expedition | Conference | ICME | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MoO2011a | Serial | 2176 | |||
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| Author | Jaime Moreno; Xavier Otazu |
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| Title | Image coder based on Hilbert scanning of embedded quadTrees | Type | Conference Article | |||
| Year | 2011 | Publication | Data Compression Conference | Abbreviated Journal | ||
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Issue | Pages | 470-470 | |||
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| Abstract | In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. | |||||
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| Area | Expedition | Conference | DCC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ MoO2011b | Serial | 2177 | |||
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| Author | Jordi Roca; Maria Vanrell; C. Alejandro Parraga |
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| 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 | ||
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Issue | Pages | 337-343 | |||
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| 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. |
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| ISSN | ISBN | 9781622767014 | Medium | |||
| Area | Expedition | Conference | CGIV | |||
| Notes | CIC | Approved | no | |||
| Call Number | RVP2012 | Serial | 2189 | |||
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| Author | Susana Alvarez |
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| Title | Revisión de la teoría de los Textons Enfoque computacional en color | Type | Book Whole | |||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | El color y la textura son dos estímulos visuales importantes para la interpretación de las imágenes. La definición de descriptores computacionales que combinan estas dos características es aún un problema abierto. La dificultad se deriva esencialmente de la propia naturaleza de ambas, mientras que la textura es una propiedad de una región, el color es una propiedad de un punto.
Hasta ahora se han utilizado tres los tipos de aproximaciones para la combinación, (a) se describe la textura directamente en cada uno de los canales color, (b) se describen textura y color por separado y se combinan al final, y (c) la combinación se realiza con técnicas de aprendizaje automático. Considerando que este problema se resuelve en el sistema visual humano en niveles muy tempranos, en esta tesis se propone estudiar el problema a partir de la implementación directa de una teoría perceptual, la teoría de los textons, y explorar así su extensión a color. Puesto que la teoría de los textons se basa en la descripción de la textura a partir de las densidades de los atributos locales, esto se adapta perfectamente al marco de trabajo de los descriptores holísticos (bag-of-words). Se han estudiado diversos descriptores basados en diferentes espacios de textons, y diferentes representaciones de las imágenes. Asimismo se ha estudiado la viabilidad de estos descriptores en una representación conceptual de nivel intermedio. Los descriptores propuestos han demostrado ser muy eficientes en aplicaciones de recuperación y clasificación de imágenes, presentando ventajas en la generación de vocabularios. Los vocabularios se obtienen cuantificando directamente espacios de baja dimensión y la perceptualidad de estos espacios permite asociar semántica de bajo nivel a las palabras visuales. El estudio de los resultados permite concluir que si bien la aproximación holística es muy eficiente, la introducción de co-ocurrencia espacial de las propiedades de forma y color de los blobs de la imagen es un elemento clave para su combinación, hecho que no contradice las evidencias en percepción |
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell;Xavier Otazu | ||
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| Notes | CIC | Approved | no | |||
| Call Number | Alv2012b | Serial | 2216 | |||
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| Author | Naila Murray |
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| Title | Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective | Type | Book Whole | |||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks. Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification. As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues. |
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Xavier Otazu;Maria Vanrell | ||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Mur2012 | Serial | 2212 | |||
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| Author | Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
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| Title | Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization | Type | Conference Article | |||
| Year | 2014 | Publication | 25th British Machine Vision Conference | Abbreviated Journal | ||
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| Abstract | We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. | |||||
| Address | Nottingham; UK; September 2014 | |||||
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| Area | Expedition | Conference | BMVC | |||
| Notes | LAMP; CIC; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ RWB2014 | Serial | 2508 | |||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Michael Felsberg |
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| Title | Scale Coding Bag-of-Words for Action Recognition | Type | Conference Article | |||
| Year | 2014 | Publication | 22nd International Conference on Pattern Recognition | Abbreviated Journal | ||
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Issue | Pages | 1514-1519 | |||
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| Abstract | Recognizing human actions in still images is a challenging problem in computer vision due to significant amount of scale, illumination and pose variation. Given the bounding box of a person both at training and test time, the task is to classify the action associated with each bounding box in an image.
Most state-of-the-art methods use the bag-of-words paradigm for action recognition. The bag-of-words framework employing a dense multi-scale grid sampling strategy is the de facto standard for feature detection. This results in a scale invariant image representation where all the features at multiple-scales are binned in a single histogram. We argue that such a scale invariant strategy is sub-optimal since it ignores the multi-scale information available with each bounding box of a person. This paper investigates alternative approaches to scale coding for action recognition in still images. We encode multi-scale information explicitly in three different histograms for small, medium and large scale visual-words. Our first approach exploits multi-scale information with respect to the image size. In our second approach, we encode multi-scale information relative to the size of the bounding box of a person instance. In each approach, the multi-scale histograms are then concatenated into a single representation for action classification. We validate our approaches on the Willow dataset which contains seven action categories: interacting with computer, photography, playing music, riding bike, riding horse, running and walking. Our results clearly suggest that the proposed scale coding approaches outperform the conventional scale invariant technique. Moreover, we show that our approach obtains promising results compared to more complex state-of-the-art methods. |
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| Address | Stockholm; August 2014 | |||||
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| Area | Expedition | Conference | ICPR | |||
| Notes | CIC; LAMP; 601.240; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ KWB2014 | Serial | 2450 | |||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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| Title | The Photometry of Intrinsic Images | Type | Conference Article | |||
| Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
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Issue | Pages | 1494-1501 | |||
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| 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 | |||||
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| 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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| Author | M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
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| Title | Adaptive color attributes for real-time visual tracking | Type | Conference Article | |||
| Year | 2014 | Publication | 27th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
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Issue | Pages | 1090 - 1097 | |||
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| Abstract | Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second. |
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| Address | Nottingham; UK; September 2014 | |||||
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| Area | Expedition | Conference | CVPR | |||
| Notes | CIC; LAMP; 600.074; 600.079 | Approved | no | |||
| Call Number | Admin @ si @ DKF2014 | Serial | 2509 | |||
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| Author | C. Alejandro Parraga |
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| Title | Color Vision, Computational Methods for | Type | Book Chapter | |||
| Year | 2014 | Publication | Encyclopedia of Computational Neuroscience | Abbreviated Journal | ||
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Issue | Pages | 1-11 | |||
| Keywords | Color computational vision; Computational neuroscience of color | |||||
| Abstract | The study of color vision has been aided by a whole battery of computational methods that attempt to describe the mechanisms that lead to our perception of colors in terms of the information-processing properties of the visual system. Their scope is highly interdisciplinary, linking apparently dissimilar disciplines such as mathematics, physics, computer science, neuroscience, cognitive science, and psychology. Since the sensation of color is a feature of our brains, computational approaches usually include biological features of neural systems in their descriptions, from retinal light-receptor interaction to subcortical color opponency, cortical signal decoding, and color categorization. They produce hypotheses that are usually tested by behavioral or psychophysical experiments. | |||||
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| Publisher | Springer-Verlag Berlin Heidelberg | Place of Publication | Editor | Dieter Jaeger; Ranu Jung | ||
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| ISSN | ISBN | 978-1-4614-7320-6 | Medium | |||
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| Notes | CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Par2014 | Serial | 2512 | |||
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| Author | C. Alejandro Parraga |
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| Title | Perceptual Psychophysics | Type | Book Chapter | |||
| Year | 2015 | Publication | Biologically-Inspired Computer Vision: Fundamentals and Applications | Abbreviated Journal | ||
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| Publisher | Place of Publication | Editor | G.Cristobal; M.Keil; L.Perrinet | |||
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| ISSN | ISBN | 978-3-527-41264-8 | Medium | |||
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| Notes | CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Par2015 | Serial | 2600 | |||
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| Author | Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris |
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| Title | Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code | Type | Conference Article | |||
| Year | 2015 | Publication | European Conference on Visual Perception ECVP2015 | Abbreviated Journal | ||
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| Address | Liverpool; uk; August 2015 | |||||
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| Area | Expedition | Conference | ECVP | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ POW2015 | Serial | 2633 | |||
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| Author | Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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| Title | An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort | Type | Conference Article | |||
| Year | 2015 | Publication | Barcelona Computational, Cognitive and Systems Neuroscience | Abbreviated Journal | ||
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| Address | Barcelona; June 2015 | |||||
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| Area | Expedition | Conference | BARCCSYN | |||
| Notes | NEUROBIT;CIC | Approved | no | |||
| Call Number | Admin @ si @ OPC2015b | Serial | 2634 | |||
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| Author | Marc Serra |
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| Title | Modeling, estimation and evaluation of intrinsic images considering color information | Type | Book Whole | |||
| Year | 2015 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...). In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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| Address | September 2015 | |||||
| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Robert Benavente;Olivier Penacchio | ||
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| ISSN | ISBN | 978-84-943427-4-5 | Medium | |||
| Area | Expedition | Conference | ||||
| Notes | CIC; 600.074 | Approved | no | |||
| Call Number | Admin @ si @ Ser2015 | Serial | 2688 | |||
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| Author | Jordi Roca |
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| Title | Constancy and inconstancy in categorical colour perception | Type | Book Whole | |||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | ||
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| Abstract | To recognise objects is perhaps the most important task an autonomous system, either biological or artificial needs to perform. In the context of human vision, this is partly achieved by recognizing the colour of surfaces despite changes in the wavelength distribution of the illumination, a property called colour constancy. Correct surface colour recognition may be adequately accomplished by colour category matching without the need to match colours precisely, therefore categorical colour constancy is likely to play an important role for object identification to be successful. The main aim of this work is to study the relationship between colour constancy and categorical colour perception. Previous studies of colour constancy have shown the influence of factors such the spatio-chromatic properties of the background, individual observer's performance, semantics, etc. However there is very little systematic study of these influences. To this end, we developed a new approach to colour constancy which includes both individual observers' categorical perception, the categorical structure of the background, and their interrelations resulting in a more comprehensive characterization of the phenomenon. In our study, we first developed a new method to analyse the categorical structure of 3D colour space, which allowed us to characterize individual categorical colour perception as well as quantify inter-individual variations in terms of shape and centroid location of 3D categorical regions. Second, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state. Our results lead to the following conclusions: (1) There exists large inter-individual variations in the categorical structure of colour space, and thus colour naming ability varies significantly but this is not well predicted by low-level chromatic discrimination ability; (2) Analysis of the average colour naming space suggested the need for an additional three basic colour terms (turquoise, lilac and lime) for optimal colour communication; (3) Chromatic setting improved the precision of more complex linear colour constancy models and suggested that mechanisms other than cone gain might be best suited to explain colour constancy; (4) The categorical structure of colour space is broadly stable under illuminant changes for categorically balanced backgrounds; (5) Categorical inconstancy exists for categorically unbalanced backgrounds thus indicating that categorical information perceived in the initial stages of adaptation may constrain further categorical perception. | |||||
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| Corporate Author | Thesis | Ph.D. thesis | ||||
| Publisher | Place of Publication | Editor | Maria Vanrell;C. Alejandro Parraga | |||
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| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ Roc2012 | Serial | 2893 | |||
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| Author | Ivet Rafegas; Maria Vanrell |
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| Title | Color spaces emerging from deep convolutional networks | Type | Conference Article | |||
| Year | 2016 | Publication | 24th Color and Imaging Conference | Abbreviated Journal | ||
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Issue | Pages | 225-230 | |||
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| Abstract | Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
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| Address | San Diego; USA; November 2016 | |||||
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| Area | Expedition | Conference | CIC | |||
| Notes | CIC | Approved | no | |||
| Call Number | Admin @ si @ RaV2016a | Serial | 2894 | |||
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