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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 |
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Conference Article |
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2020 |
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31st British Machine Vision Conference |
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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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Virtual; September 2020 |
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CIC; 600.087; 600.140; 600.118 |
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Admin @ si @ DSM2020 |
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3461 |
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Author |
Javier Vazquez; Robert Benavente; Maria Vanrell |
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Title |
Naming constraints constancy |
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2012 |
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2nd Joint AVA / BMVA Meeting on Biological and Machine Vision |
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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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Admin @ si @ VBV2012 |
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2131 |
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Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Title |
Multiresolution colour texture representations for tile classification |
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Miscellaneous |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS @ adas @ LBV1999a |
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3 |
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Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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Title |
Visual behaviours for binocular navigation with autonomous systems. |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ RVV1999a |
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13 |
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Maria Vanrell; Jordi Vitria; Xavier Roca |
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A multidimensional scaling approach to explore the behavior of a texture perception algorithm. |
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1997 |
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Machine Vision and Applications |
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9 |
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262–271 |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1997 |
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35 |
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Xavier Roca; Jordi Vitria; Maria Vanrell; Juan J. Villanueva |
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Title |
Gaze control in a binocular robot systems |
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1999 |
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Barcelona |
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BCNPCL @ bcnpcl @ RVV1999b |
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Felipe Lumbreras; Ramon Baldrich; Maria Vanrell; Joan Serrat; Juan J. Villanueva |
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Multiresolution texture classification of ceramic tiles. |
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1999 |
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Recent Research developments in optical engineering, Research Signpost, 2: 213–228 |
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ADAS;CIC |
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ADAS @ adas @ LBV1999b |
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45 |
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Robert Benavente; M.C. Olive; Maria Vanrell; Ramon Baldrich |
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Title |
Colour Perception: A Simple Method for Colour Naming. |
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1999 |
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Girona |
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Robert Benavente; Maria Vanrell |
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A colour naming experiment |
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2001 |
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CVC Technical Report #56 |
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Felipe Lumbreras; Joan Serrat; Ramon Baldrich; Maria Vanrell; Juan J. Villanueva |
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Color Texture Recognition Through Multiresolution Features |
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2001 |
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QCAV 2001 International Conference on Quality Control by Artificial Vision, France, 1:114–121. |
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ADAS @ adas @ LSB2001 |
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124 |
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Maria Vanrell; Jordi Vitria; Xavier Roca |
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A General Morphological Framework for Perceptual Texture Discrimination based on Granulometries. |
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1993 |
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Technical Workshop on Mathematical Morphology and its Applications to Signal Processing. |
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Barcelona |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1993 |
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154 |
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Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
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Title |
Colour Normalisation Based on Background Information. |
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2001 |
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Proceeding ICIP 2001, IEEE International Conference on Image Processing |
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ICIP 2001 |
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874–877 |
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Grecia. |
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ADAS @ adas @ VLP2001 |
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167 |
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Maria Vanrell; Jordi Vitria |
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Mathematical Morphology, Granulometries and Texture Perception. |
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1993 |
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SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). |
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BCNPCL @ bcnpcl @ VaV1993 |
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178 |
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Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Normalized colour segmentation for human appearance description. |
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2000 |
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15 th International Conference on Pattern Recognition, 3:637–641. |
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Barcelona. |
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