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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 | 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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| Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
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| Title | Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | |||
| Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | ||
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| Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | |||||
| Address | Virtual; September 2020 | |||||
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| Area | Expedition | Conference | LIM | |||
| Notes | CIC; 600.118; 600.140; | Approved | no | |||
| Call Number | Admin @ si @ SBV2020 | Serial | 3460 | |||
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| Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
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| Title | Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | |||
| Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | ||
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| Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
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| Address | Virtual; September 2020 | |||||
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| Area | Expedition | Conference | BMVC | |||
| Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ DSM2020 | Serial | 3461 | |||
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