Hassan Ahmed Sial, Ramon Baldrich, Maria Vanrell, & Dimitris Samaras. (2020). Light Direction and Color Estimation from Single Image with Deep Regression. In London Imaging Conference.
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.
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Sagnik Das, Hassan Ahmed Sial, Ke Ma, Ramon Baldrich, Maria Vanrell, & Dimitris Samaras. (2020). Intrinsic Decomposition of Document Images In-the-Wild. In 31st British Machine Vision Conference.
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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Maria Vanrell, Ramon Baldrich, Anna Salvatella, Robert Benavente, & Francesc Tous. (2004). Induction operators for a computational colour-texture representation. Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651).
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2004). Estimation of Fuzzy Sets for Computational Colour Categorization. Color Research and Application, 29(5):342–353 (IF: 0.739).
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Xavier Otazu, & Maria Vanrell. (2005). Perceptual representation of textured images. Journal of Imaging Science and Technology, 49(3):262–271 (IF: 0.522).
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Maria Vanrell, & Jordi Vitria. (1997). Optimal 3x3 decomposable disks for morphological transformations. Image and Vision Computing, 15(2): 845–854.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2006). A data set for fuzzy colour naming. Color Research & Application, 31(1):48–56.
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Xavier Otazu, & Maria Vanrell. (2006). Several lightness induction effects with a computational multiresolution wavelet framework. 29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56.
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Xavier Otazu, Maria Vanrell, & C. Alejandro Parraga. (2007). Mutiresolution Wavelet Framework Reproduces Induction Effects. Perception 36:167–167, supp.
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C. Alejandro Parraga, Robert Benavente, & Maria Vanrell. (2007). Modeling Colour-Naming Space with Fuzzy Sets. Perception 36:198–198, supp.
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Xavier Otazu, Maria Vanrell, & C. Alejandro Parraga. (2008). Multiresolution Wavelet Framework Models Brightness Induction Effects. VR - Vision Research, 733–751.
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Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2008). Parametric Fuzzy Sets for Automatic Color Naming. Journal of the Optical Society of America A, 2582–2593.
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Xavier Otazu, Maria Vanrell, & C. Alejandro Parraga. (2008). Colour induction effects are modelled by a low-level multiresolution wavelet framework. Perception 37(Suppl.): 107.
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Maria Vanrell, Jordi Vitria, & Xavier Roca. (1997). A multidimensional scaling approach to explore the behavior of a texture perception algorithm. Machine Vision and Applications, 9, 262–271.
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