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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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Author |
J. Nuñez; O. Fors; Xavier Otazu; Vicenç Pala; Roman Arbiol; M.T. Merino |
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A Wavelet-Based Method for the Determination of the Relative Resolution Between Remotely Sensed Images |
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2006 |
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IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2539–2548 |
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CAT @ cat @ NFO2006 |
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660 |
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Arjan Gijsenji; Theo Gevers; Joost Van de Weijer |
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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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Javier Vazquez; Maria Vanrell; Ramon Baldrich; Francesc Tous |
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Color Constancy by Category Correlation |
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2012 |
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IEEE Transactions on Image Processing |
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TIP |
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21 |
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4 |
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1997-2007 |
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Finding color representations which are stable to illuminant changes is still an open problem in computer vision. Until now most approaches have been based on physical constraints or statistical assumptions derived from the scene, while very little attention has been paid to the effects that selected illuminants have
on the final color image representation. The novelty of this work is to propose
perceptual constraints that are computed on the corrected images. We define the
category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy. |
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Admin @ si @ VVB2012 |
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1999 |
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Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou |
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Title |
Multi-Illuminant Estimation with Conditional Random Fields |
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2014 |
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IEEE Transactions on Image Processing |
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TIP |
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23 |
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1 |
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83-95 |
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color constancy; CRF; multi-illuminant |
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Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ BRW2014 |
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2451 |
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