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	Author	Ivet Rafegas; Maria Vanrell
	Title	Color spaces emerging from deep convolutional networks			Type	Conference Article
	Year	2016	Publication	24th Color and Imaging Conference	Abbreviated Journal
	Volume		Issue		Pages	225-230
	Keywords
	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.
	Address	San Diego; USA; November 2016
	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	CIC
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ RaV2016a			Serial	2894
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	Author	Ivet Rafegas; Maria Vanrell
	Title	Color representation in CNNs: parallelisms with biological vision			Type	Conference Article
	Year	2017	Publication	ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features are efficiently represented. Here, we dissect a trained CNN [2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions of them to quantify color tuning properties of artificial neurons to provide a classification of the network population. We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT).
	Address	Venice; Italy; October 2017
	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	ICCV-MBCC
	Notes	CIC; 600.087; 600.051			Approved	no
	Call Number	Admin @ si @ RaV2017			Serial	2984
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	Author	Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus
	Title	Color Naming			Type	Book Chapter
	Year	2012	Publication	Color in Computer Vision: Fundamentals and Applications	Abbreviated Journal
	Volume		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	Javier Vazquez; Maria Vanrell; Robert Benavente
	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		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	Ivet Rafegas
	Title	Color in Visual Recognition: from flat to deep representations and some biological parallelisms			Type	Book Whole
	Year	2017	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks. Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias.
	Address	November 2017
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Maria Vanrell
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-945373-7-0	Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ Raf2017			Serial	3100
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	Author	Ramon Baldrich; Maria Vanrell; Robert Benavente; Anna Salvatella
	Title	Color Enhancement based on perceptual sharpening			Type	Miscellaneous
	Year	2003	Publication	Proceedings of the IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona
	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
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ BVB2003			Serial	370
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	Author	Ivet Rafegas; Maria Vanrell
	Title	Color encoding in biologically-inspired convolutional neural networks			Type	Journal Article
	Year	2018	Publication	Vision Research	Abbreviated Journal	VR
	Volume	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.
	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
	Notes	CIC; 600.051; 600.087			Approved	no
	Call Number	Admin @ si @RaV2018			Serial	3114
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	Author	Javier Vazquez; Maria Vanrell; Ramon Baldrich; Francesc Tous
	Title	Color Constancy by Category Correlation			Type	Journal Article
	Year	2012	Publication	IEEE Transactions on Image Processing	Abbreviated Journal	TIP
	Volume	21	Issue	4	Pages	1997-2007
	Keywords
	Abstract	Finding color representations which are stable to illuminant changes is still an open problem in computer vision. Until now most approaches have been based on physical constraints or statistical assumptions derived from the scene, while very little attention has been paid to the effects that selected illuminants have on the final color image representation. The novelty of this work is to propose perceptual constraints that are computed on the corrected images. We define the category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy.
	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	1057-7149	ISBN		Medium
	Area		Expedition		Conference
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ VVB2012			Serial	1999
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	Author	Javier Vazquez; C. Alejandro Parraga; Maria Vanrell; Ramon Baldrich
	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	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.
	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
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ VPV2009a			Serial	1171
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	Author	Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez
	Title	Color Attributes for Object Detection			Type	Conference Article
	Year	2012	Publication	25th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		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
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	Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
	Title	Chromatic settings and the structural color constancy index			Type	Journal Article
	Year	2013	Publication	Journal of Vision	Abbreviated Journal	JV
	Volume	13	Issue	4-3	Pages	1-26
	Keywords
	Abstract	Color constancy is usually measured by achromatic setting, asymmetric matching, or color naming paradigms, whose results are interpreted in terms of indexes and models that arguably do not capture the full complexity of the phenomenon. Here we propose a new paradigm, chromatic setting, which allows a more comprehensive characterization of color constancy through the measurement of multiple points in color space under immersive adaptation. We demonstrated its feasibility by assessing the consistency of subjects' responses over time. The paradigm was applied to two-dimensional (2-D) Mondrian stimuli under three different illuminants, and the results were used to fit a set of linear color constancy models. The use of multiple colors improved the precision of more complex linear models compared to the popular diagonal model computed from gray. Our results show that a diagonal plus translation matrix that models mechanisms other than cone gain might be best suited to explain the phenomenon. Additionally, we calculated a number of color constancy indices for several points in color space, and our results suggest that interrelations among colors are not as uniform as previously believed. To account for this variability, we developed a new structural color constancy index that takes into account the magnitude and orientation of the chromatic shift in addition to the interrelations among colors and memory effects.
	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
	Notes	CIC; 600.052; 600.051; 605.203			Approved	no
	Call Number	Admin @ si @ RPV2013			Serial	2288
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	Author	Sandra Jimenez; Xavier Otazu; Valero Laparra; Jesus Malo
	Title	Chromatic induction and contrast masking: similar models, different goals?			Type	Conference Article
	Year	2013	Publication	Human Vision and Electronic Imaging XVIII	Abbreviated Journal
	Volume	8651	Issue		Pages
	Keywords
	Abstract	Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality assessment. Although the above models have formal and qualitative similarities, it does not necessarily mean that the mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms (disregarding spatial information), different parameters in the normalization give rise to optimal discrimination or adaptation, and different non-linearities may give rise to error minimization or component independence. In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits of masking in statistical independence terms. However, such statistical analysis has not been performed for spatio-chromatic induction models where chromatic perception depends on spatial configuration. In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence similarly as previously reported for masking models. Mutual information analysis suggests that seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple images. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
	Address	San Francisco CA; USA; February 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	HVEI
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ JOL2013			Serial	2240
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	Author	Jordi Roca; C. Alejandro Parraga; Maria Vanrell
	Title	Categorical Focal Colours are Structurally Invariant Under Illuminant Changes			Type	Conference Article
	Year	2011	Publication	European Conference on Visual Perception	Abbreviated Journal
	Volume		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	Enric Marti; Jordi Rocarias; Ricardo Toledo
	Title	Caront: gestió flexible de grups d’alumnes en una asignatura i activitats sobre grups. Nova activitat de control			Type	Miscellaneous
	Year	2008	Publication	V Jornades d’Innovació Docent	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	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
	Notes	IAM;RV;CIC;ADAS			Approved	no
	Call Number	IAM @ iam @ MRT2008a			Serial	1617
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	Author	Danna Xue; Luis Herranz; Javier Vazquez; Yanning Zhang
	Title	Burst Perception-Distortion Tradeoff: Analysis and Evaluation			Type	Conference Article
	Year	2023	Publication	IEEE International Conference on Acoustics, Speech and Signal Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Burst image restoration attempts to effectively utilize the complementary cues appearing in sequential images to produce a high-quality image. Most current methods use all the available images to obtain the reconstructed image. However, using more images for burst restoration is not always the best option regarding reconstruction quality and efficiency, as the images acquired by handheld imaging devices suffer from degradation and misalignment caused by the camera noise and shake. In this paper, we extend the perception-distortion tradeoff theory by introducing multiple-frame information. We propose the area of the unattainable region as a new metric for perception-distortion tradeoff evaluation and comparison. Based on this metric, we analyse the performance of burst restoration from the perspective of the perception-distortion tradeoff under both aligned bursts and misaligned bursts situations. Our analysis reveals the importance of inter-frame alignment for burst restoration and shows that the optimal burst length for the restoration model depends both on the degree of degradation and misalignment.
	Address	Rodhes Islands; Greece; June 2023
	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	ICASSP
	Notes	CIC; MACO			Approved	no
	Call Number	Admin @ si @ XHV2023			Serial	3909
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	Author	Xavier Otazu; Maria Vanrell
	Title	Building Perceived Colour Images.			Type	Miscellaneous
	Year	2004	Publication	CGIV 2004 Second European Conference on Colour in Graphics, Imaging, and Vision, 140:145	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Aachen (Germany)
	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
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ OtV2004			Serial	450
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	Author	Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco
	Title	Brightness induction by contextual influences in V1: a neurodynamical account			Type	Abstract
	Year	2012	Publication	Journal of Vision	Abbreviated Journal	VSS
	Volume	12	Issue	9	Pages
	Keywords
	Abstract	Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas and reveals fundamental properties of neural organization in the visual system. Several phenomenological models have been proposed that successfully account for psychophysical data (Pessoa et al. 1995, Blakeslee and McCourt 2004, Barkan et al. 2008, Otazu et al. 2008). Neurophysiological evidence suggests that brightness information is explicitly represented in V1 and neuronal response modulations have been observed followingluminance changes outside their receptive fields (Rossi and Paradiso, 1999). 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 (1999) 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 contour detection and preattentive segmentation, which share with brightness induction the relevant effect of contextual influences. In our model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition which makes it possible to recover an image reflecting the perceived intensity. The proposed model successfully accounts for well known pyschophysical effects (among them: the White's and modified White's effects, the Todorović, Chevreul, achromatic ring patterns, and grating induction effects). Our work suggests that intra-cortical interactions in the primary visual cortex could partially explain perceptual brightness induction effects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual pathway.
	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
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ OPD2012b			Serial	2178
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	Author	Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
	Title	Brightness and colour induction through contextual influences in V1			Type	Conference Article
	Year	2015	Publication	Scottish Vision Group 2015 SGV2015	Abbreviated Journal
	Volume	12	Issue	9	Pages	1208-2012
	Keywords
	Abstract
	Address	Carnoustie; Scotland; March 2015
	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	SGV
	Notes	NEUROBIT;CIC			Approved	no
	Call Number	Admin @ si @ OPC2015a			Serial	2632
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