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M. Gonzalez-Audicana; Xavier Otazu; O. Fors; A. Seco |
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Comparison between Mallats and the trous discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images |
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2005 |
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International Journal of Remote Sensing, 26(3):595–614 (IF: 0.925) |
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CAT @ cat @ GOF2005 |
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530 |
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Arjan Gijsenji; Theo Gevers; Joost Van de Weijer |
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Title |
Computational Color Constancy: Survey and Experiments |
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2011 |
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IEEE Transactions on Image Processing |
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TIP |
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20 |
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9 |
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2475-2489 |
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computational color constancy;computer vision application;gamut-based method;learning-based method;static method;colour vision;computer vision;image colour analysis;learning (artificial intelligence);lighting |
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Computational color constancy is a fundamental prerequisite for many computer vision applications. This paper presents a survey of many recent developments and state-of-the- art methods. Several criteria are proposed that are used to assess the approaches. A taxonomy of existing algorithms is proposed and methods are separated in three groups: static methods, gamut-based methods and learning-based methods. Further, the experimental setup is discussed including an overview of publicly available data sets. Finally, various freely available methods, of which some are considered to be state-of-the-art, are evaluated on two data sets. |
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1057-7149 |
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ISE;CIC |
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Admin @ si @ GGW2011 |
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1717 |
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Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell |
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Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects |
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2020 |
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Journal of the Optical Society of America A |
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JOSA A |
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37 |
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1 |
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1-15 |
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Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. |
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CIC; 600.140; 600.12; 600.118 |
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Admin @ si @ SBV2019 |
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3311 |
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Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell |
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Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures |
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2011 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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33 |
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5 |
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917-930 |
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The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost. |
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Los Alamitos; CA; USA; |
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IEEE Computer Society |
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0162-8828 |
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Admin @ si @ VBW2011 |
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1715 |
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Author |
Xavier Otazu; M. Ribo; M. Peracaula; J.M. Paredes; J. Nuñez |
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Detection of superimposed periodic signals using wavelets |
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2002 |
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Monthly Notices of the Royal Astronomical Society, 333, 2: 365–372 (IF: 4.671) |
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CAT @ cat @ ORP2002 |
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272 |
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