A view-invariant and anti-reflection algorithm for car body extraction and color classification

Hui Zhen Gu, Suh-Yin Lee, Wen-Chih Peng

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.
Original languageEnglish
Pages (from-to)387-418
Number of pages34
JournalMultimedia Tools and Applications
Issue number3
StatePublished - Aug 2013


  • View-invariant; Light reflection; Tri-state architecture; Image segmentation; Car body extraction; Color classification

Fingerprint Dive into the research topics of 'A view-invariant and anti-reflection algorithm for car body extraction and color classification'. Together they form a unique fingerprint.

Cite this