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Arjan Gijsenij; Theo Gevers; Joost Van de Weijer |
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Title  |
Improving Color Constancy by Photometric Edge Weighting |
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2012 |
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IEEE Transaction on Pattern Analysis and Machine Intelligence |
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TPAMI |
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34 |
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5 |
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918-929 |
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: Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. |
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Los Alamitos; CA; USA; |
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0162-8828 |
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CIC;ISE |
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Admin @ si @ GGW2012 |
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1850 |
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Author |
O. Fors; J. Nuñez; Xavier Otazu; A. Prades; Robert D. Cardinal |
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Title |
Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques |
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2010 |
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Sensors |
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SENS |
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10 |
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3 |
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1743–1752 |
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image processing; image deconvolution; faint stars; space debris; wavelet transform |
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Abstract: In this paper we show how the techniques of image deconvolution can increase the ability of image sensors as, for example, CCD imagers, to detect faint stars or faint orbital objects (small satellites and space debris). In the case of faint stars, we show that this benefit is equivalent to double the quantum efficiency of the used image sensor or to increase the effective telescope aperture by more than 30% without decreasing the astrometric precision or introducing artificial bias. In the case of orbital objects, the deconvolution technique can double the signal-to-noise ratio of the image, which helps to discover and control dangerous objects as space debris or lost satellites. The benefits obtained using CCD detectors can be extrapolated to any kind of image sensors. |
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CAT @ cat @ FNO2010 |
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1285 |
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Maria Vanrell; Ramon Baldrich; Anna Salvatella; Robert Benavente; Francesc Tous |
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Induction operators for a computational colour-texture representation |
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2004 |
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Computer Vision and Image Understanding, 94(1–3):92–114, ISSN: 1077–3142 (IF: 0.651) |
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453 |
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Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown |
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Title |
Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring |
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2023 |
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Computer Graphics Forum |
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CGF |
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Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework. |
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CIC; MACO |
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Admin @ si @ XVH2023 |
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3883 |
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Author |
Xavier Otazu; M. Gonzalez-Audicana; O. Fors; J. Nuñez |
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Introduction of Sensor Spectral Response Into Image Fusion Methods. Application to Wavelet-Based Methods |
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2005 |
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IEEE Transactions on Geoscience and Remote Sensing, 43(10): 2376–2385 (IF: 1.627) |
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CAT @ cat @ OGF2005 |
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564 |
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