TY - GEN
T1 - Deciding the number of color histogram bins for vehicle color recognition
AU - Kim, Ku Jin
AU - Park, Sun Mi
AU - Choi, Yoo Joo
PY - 2008
Y1 - 2008
N2 - Given vehicle images, we suggest a way to recognize the color of the vehicle contained in the image. The color feature of a vehicle is represented by a color histogram, and we decide the appropriate number of color histogram bins, which mainly affects the successful recognition rate. After generating the histograms, template matching is used to decide the vehicle color. In HSI (hue saturation intensity) color space, experimental results show that the partition of H, S, and I into 8, 4, 4, respectively, achieves the highest success rate up to 88.34%.
AB - Given vehicle images, we suggest a way to recognize the color of the vehicle contained in the image. The color feature of a vehicle is represented by a color histogram, and we decide the appropriate number of color histogram bins, which mainly affects the successful recognition rate. After generating the histograms, template matching is used to decide the vehicle color. In HSI (hue saturation intensity) color space, experimental results show that the partition of H, S, and I into 8, 4, 4, respectively, achieves the highest success rate up to 88.34%.
UR - http://www.scopus.com/inward/record.url?scp=67049137615&partnerID=8YFLogxK
U2 - 10.1109/APSCC.2008.207
DO - 10.1109/APSCC.2008.207
M3 - Conference contribution
AN - SCOPUS:67049137615
SN - 9780769534732
T3 - Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
SP - 134
EP - 138
BT - Proceedings of the 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
PB - IEEE Computer Society
T2 - 3rd IEEE Asia-Pacific Services Computing Conference, APSCC 2008
Y2 - 9 December 2008 through 12 December 2008
ER -