Object Recognition Using Log-Polar Wavelet Mapping

This paper proposes a rotation and scale invariant object recognition method which combines image feature extraction in Cartesian coordinate with the log-polar mapping and similarity measure techniques for classification. The method yields robustness for fast computation without any rescale needed in the target image. The method also does not use motion estimation. Thus, the method is also robust in recognizing objects without prior information of the objectpsilas motion or previous location. Experiments with real video sequences are provided to verify the effectiveness of the proposed approach in practice.

[1]  Shih-Fu Chang,et al.  Overview of the MPEG-7 standard , 2001, IEEE Trans. Circuits Syst. Video Technol..

[2]  Dorin Comaniciu,et al.  Kernel-Based Object Tracking , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Yuan F. Zheng,et al.  Object tracking using the Gabor wavelet transform and the golden section algorithm , 2002, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[4]  B. S. Manjunath,et al.  NeTra-V: toward an object-based video representation , 1997, Electronic Imaging.

[5]  Takio Kurita,et al.  Scale invariant face detection method using higher-order local autocorrelation features extracted from log-polar image , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[6]  Chi-Man Pun,et al.  Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[8]  George Wolberg,et al.  Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations , 2005, IEEE Transactions on Image Processing.