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Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
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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 | Issue | Pages | 278-285 | |||
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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 | |||||
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Publisher | IEEE Xplore | Place of Publication | Editor | |||
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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 |
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Title | Color Naming | Type | Book Chapter | |||
Year | 2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | ||
Volume | Issue | 17 | Pages | 287-317 | ||
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Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek | ||
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ WBV2012 | Serial | 2063 | |||
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Author | Abel Gonzalez-Garcia; Robert Benavente; Olivier Penacchio; Javier Vazquez; Maria Vanrell; C. Alejandro Parraga |
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Title | Coloresia: An Interactive Colour Perception Device for the Visually Impaired | Type | Book Chapter | |||
Year | 2013 | Publication | Multimodal Interaction in Image and Video Applications | Abbreviated Journal | ||
Volume | 48 | Issue | Pages | 47-66 | ||
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Abstract | A significative percentage of the human population suffer from impairments in their capacity to distinguish or even see colours. For them, everyday tasks like navigating through a train or metro network map becomes demanding. We present a novel technique for extracting colour information from everyday natural stimuli and presenting it to visually impaired users as pleasant, non-invasive sound. This technique was implemented inside a Personal Digital Assistant (PDA) portable device. In this implementation, colour information is extracted from the input image and categorised according to how human observers segment the colour space. This information is subsequently converted into sound and sent to the user via speakers or headphones. In the original implementation, it is possible for the user to send its feedback to reconfigure the system, however several features such as these were not implemented because the current technology is limited.We are confident that the full implementation will be possible in the near future as PDA technology improves. | |||||
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Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | |||
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ISSN | 1868-4394 | ISBN | 978-3-642-35931-6 | Medium | ||
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Notes | CIC; 600.052; 605.203 | Approved | no | |||
Call Number | Admin @ si @ GBP2013 | Serial | 2266 | |||
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Author | Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras |
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Title | Intrinsic Image Evaluation On Synthetic Complex Scenes | Type | Conference Article | |||
Year | 2013 | Publication | 20th IEEE International Conference on Image Processing | Abbreviated Journal | ||
Volume | Issue | Pages | 285 - 289 | |||
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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. |
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Address | Melbourne; Australia; September 2013 | |||||
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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 | Susana Alvarez; Maria Vanrell |
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Title | Texton theory revisited: a bag-of-words approach to combine textons | Type | Journal Article | |||
Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR | |
Volume | 45 | Issue | 12 | Pages | 4312-4325 | |
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Abstract | The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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ISSN | 0031-3203 | ISBN | Medium | |||
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ AlV2012a | Serial | 2130 | |||
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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 | Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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Title | Predicting categorical colour perception in successive colour constancy | Type | Abstract | |||
Year | 2012 | Publication | Perception | Abbreviated Journal | PER | |
Volume | 41 | Issue | Pages | 138 | ||
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Abstract | Colour constancy is a perceptual mechanism that seeks to keep the colour of objects relatively stable under an illumination shift. Experiments haveshown that its effects depend on the number of colours present in the scene. We
studied categorical colour changes under different adaptation states, in particular, whether the colour categories seen under a chromatically neutral illuminant are the same after a shift in the chromaticity of the illumination. To do this, we developed the chromatic setting paradigm (2011 Journal of Vision11 349), which is as an extension of achromatic setting to colour categories. The paradigm exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. Our experiments were run on a CRT monitor (inside a dark room) under various simulated illuminants and restricting the number of colours of the Mondrian background to three, thus weakening the adaptation effect. Our results show a change in the colour categories present before (under neutral illumination) and after adaptation (under coloured illuminants) with a tendency for adapted colours to be less saturated than before adaptation. This behaviour was predicted by a simple affine matrix model, adjusted to the chromatic setting results. |
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ISSN | 0301-0066 | ISBN | Medium | |||
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Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ RPV2012 | Serial | 2188 | |||
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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 | ||
Volume | 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 | 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 | ||
Volume | 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 | Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
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Title | DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition | Type | Conference Article | |||
Year | 2015 | Publication | Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II | Abbreviated Journal | ||
Volume | 9475 | Issue | Pages | 463-473 | ||
Keywords | Projector-camera systems; Feature descriptors; Object recognition | |||||
Abstract | Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. | |||||
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Publisher | Springer International Publishing | Place of Publication | Editor | |||
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Series Editor | Series Title | Abbreviated Series Title | LNCS | |||
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ISSN | 0302-9743 | ISBN | 978-3-319-27862-9 | Medium | ||
Area | Expedition | Conference | ISVC | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ SMG2015 | Serial | 2736 | |||
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Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez |
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Title | Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | |||
Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A | |
Volume | 34 | Issue | 5 | Pages | 827-837 | |
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Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | |||||
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Notes | CIC; 600.087 | Approved | no | |||
Call Number | Admin @ si @ RVB2017 | Serial | 2892 | |||
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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 | ||
Volume | 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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Author | Ivet Rafegas; Maria Vanrell |
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Title | Colour Visual Coding in trained Deep Neural Networks | Type | Abstract | |||
Year | 2016 | Publication | European Conference on Visual Perception | Abbreviated Journal | ||
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Address | Barcelona; Spain; August 2016 | |||||
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Area | Expedition | Conference | ECVP | |||
Notes | CIC | Approved | no | |||
Call Number | Admin @ si @ RaV2016b | Serial | 2895 | |||
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Author | Ivet Rafegas; Maria Vanrell |
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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 | ||
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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). |
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Address | Venice; Italy; October 2017 | |||||
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Area | Expedition | Conference | ICCV-MBCC | |||
Notes | CIC; 600.087; 600.051 | Approved | no | |||
Call Number | Admin @ si @ RaV2017 | Serial | 2984 | |||
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