Highlight Detection and Removal Based on Chromaticity
The presence of highlight can lead to erroneous results in Computer Vision applications such as edge detection, and motion tracking. Many algorithms have been developed to detect and remove highlight. In this paper, we propose a simple and effective method for detecting and removal of highlight. We...
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Published in | Image Analysis and Recognition pp. 199 - 206 |
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Main Authors | , , , |
Format | Book Chapter Conference Proceeding |
Language | English |
Published |
Berlin, Heidelberg
Springer Berlin Heidelberg
2005
Springer |
Edition | 1ère éd |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Abstract | The presence of highlight can lead to erroneous results in Computer Vision applications such as edge detection, and motion tracking. Many algorithms have been developed to detect and remove highlight. In this paper, we propose a simple and effective method for detecting and removal of highlight. We first use a window to help to remove the noise and reduce the data amount for analysis. We then apply K-means algorithm in a 5-D vector space to computer diffuse chromaticity. In the case of non-white illuminant, illuminant chromaticity is estimated in the inverse-intensity space, and we use Fuzzy C-mean clustering and linear fitting to get illuminant chromaticity. Finally, we use Specular-to-Diffuse mechanism to separate specular reflection component from image. Experiments show that it is robust and can give good results. |
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AbstractList | The presence of highlight can lead to erroneous results in Computer Vision applications such as edge detection, and motion tracking. Many algorithms have been developed to detect and remove highlight. In this paper, we propose a simple and effective method for detecting and removal of highlight. We first use a window to help to remove the noise and reduce the data amount for analysis. We then apply K-means algorithm in a 5-D vector space to computer diffuse chromaticity. In the case of non-white illuminant, illuminant chromaticity is estimated in the inverse-intensity space, and we use Fuzzy C-mean clustering and linear fitting to get illuminant chromaticity. Finally, we use Specular-to-Diffuse mechanism to separate specular reflection component from image. Experiments show that it is robust and can give good results. |
Author | Xu, Shu-Chang Ye, Xiuzi Wu, Yin Zhang, Sanyuan |
Author_xml | – sequence: 1 givenname: Shu-Chang surname: Xu fullname: Xu, Shu-Chang organization: College of Computer Science, Zhejiang University, Hangzhou, China – sequence: 2 givenname: Xiuzi surname: Ye fullname: Ye, Xiuzi organization: College of Computer Science, Zhejiang University, Hangzhou, China – sequence: 3 givenname: Yin surname: Wu fullname: Wu, Yin organization: College of Computer Science, Zhejiang University, Hangzhou, China – sequence: 4 givenname: Sanyuan surname: Zhang fullname: Zhang, Sanyuan organization: College of Computer Science, Zhejiang University, Hangzhou, China |
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Keywords | Cluster analysis Computer vision Data analysis Target tracking Motion estimation K means algorithm Pattern recognition Fuzzy logic Image analysis Vector space Specular reflection Classification Chromaticity Motion detection Edge detection |
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Snippet | The presence of highlight can lead to erroneous results in Computer Vision applications such as edge detection, and motion tracking. Many algorithms have been... |
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StartPage | 199 |
SubjectTerms | Applied sciences Artificial intelligence Chromaticity Space Computer science; control theory; systems Diffuse Chromaticity Exact sciences and technology Highlight Detection Highlight Region Pattern recognition. Digital image processing. Computational geometry Reflection Component |
Title | Highlight Detection and Removal Based on Chromaticity |
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