Cross-view object identification using principal color transformation

Shin Yu Chen*, Jun-Wei Hsieh, Duan Yu Chen

*Corresponding author for this work

研究成果: Conference contribution同行評審

摘要

This paper presents a novel color correction technique for object identification across different cameras. First of all, we project the analyzed object onto the LAB color space and then find its principal color axis through the principal component analysis. Since the L axis corresponds to the intensity, we then rotate the found principal color axis for making it parallel to the L axis. After this rotation, the color distortions among different cameras can be reduced into minimum. Then, a hybrid classifier is designed for classifying objects into different categories even though they are captured under different lighting conditions. Based on a polar coordinate, a sampling technique is then proposed for extracting several important color features from A-B plane. Then, using the SVM learning algorithm, a color classifier can be trained for classifying each object into different categories. For the non-color categories, we quantize the RGB channels into different levels. Then, another classifier is obtained for classifying each gray object into its corresponding category. Since the proposed color correction scheme reduce the problem of color distortions into a minimum, each object can be well classified and identified even though they are captured across different cameras and under lighting condition.

原文English
主出版物標題2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
頁面2777-2781
頁數5
DOIs
出版狀態Published - 15 十一月 2010
事件2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
持續時間: 11 七月 201014 七月 2010

出版系列

名字2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
6

Conference

Conference2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
國家China
城市Qingdao
期間11/07/1014/07/10

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