標題: Car model recognition by utilizing symmetric property to overcome severe pose variation
作者: Gu, Hui-Zhen
Lee, Suh-Yin
資訊工程學系
Department of Computer Science
關鍵字: Car model recognition;Pose estimation;Active shape model;Mirror morphing;Template matching
公開日期: 1-二月-2013
摘要: This paper presents a mirror morphing scheme to deal with the challenging pose variation problem in car model recognition. Conventionally, researchers adopt pose estimation techniques to overcome the pose problem, whereas it is difficult to obtain very accurate pose estimation. Moreover, slight deviation in pose estimation degrades the recognition performance dramatically. The mirror morphing technique utilizes the symmetric property of cars to normalize car images of any orientation into a typical view. Therefore, the pose error and center bias can be eliminated and satisfactory recognition performance can be obtained. To support mirror morphing, active shape model (ASM) is used to acquire car shape information. An effective pose and center estimation approach is also proposed to provide a good initialization for ASM. In experiments, our proposed car model recognition system can achieve very high recognition rate (> 95%) with very low probability of false alarm even when it is dealing with the severe pose problem in the cases of cars with similar shape and color.
URI: http://dx.doi.org/10.1007/s00138-012-0414-8
http://hdl.handle.net/11536/21029
ISSN: 0932-8092
DOI: 10.1007/s00138-012-0414-8
期刊: MACHINE VISION AND APPLICATIONS
Volume: 24
Issue: 2
起始頁: 255
結束頁: 274
顯示於類別:期刊論文


文件中的檔案:

  1. 000313496100003.pdf