ACM Computing Surveys (CSUR)
Solving for Colour Constancy using a Constrained Dichromatic Reflection Model
International Journal of Computer Vision
Diffuse-Specular Separation and Depth Recovery from Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Non-parametric Model for Background Subtraction
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Separating Reflection Components of Textured Surfaces using a Single Image
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
A Convolution Kernel Method for Color Recognition
ALPIT '07 Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology (ALPIT 2007)
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Deciding the Number of Color Histogram Bins for Vehicle Color Recognition
APSCC '08 Proceedings of the 2008 IEEE Asia-Pacific Services Computing Conference
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Vehicle Color Extraction Based on First Sight Window
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Central object extraction for object-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Vehicle Color Recognition Using Vector Matching of Template
ISECS '10 Proceedings of the 2010 Third International Symposium on Electronic Commerce and Security
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Example-Based Color Vehicle Retrieval for Surveillance
AVSS '10 Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance
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This study proposes an intelligent algorithm with tri-state architecture for real-time car body extraction and color classification. The algorithm is capable of managing both the difficulties of viewpoint and light reflection. Because the influence of light reflection is significantly different on bright, dark, and colored cars, three different strategies are designed for various color categories to acquire a more intact car body. A SARM (Separating and Re-Merging) algorithm is proposed to separate the car body and the background, and recover the entire car body more completely. A robust selection algorithm is also performed to determine the correct color category and car body. Then, the color type of the vehicle is decided only by the pixels in the extracted car body. The experimental results show that the tri-state method can extract almost 90% of car body pixels from a car image. Over 98% of car images are distinguished correctly in their categories, and the average accuracy of the 10-color-type classification is higher than 93%. Furthermore, the computation load of the proposed method is light; therefore it is applicable for real-time systems.