Properties of Orthogonal Gaussian-Hermite Moments and Their Applications

Moments are widely used in pattern recognition, image processing, and computer vision and multiresolution analysis. In this paper, we first point out some properties of the orthogonal Gaussian-Hermite moments, and propose a new method to detect the moving objects by using the orthogonal Gaussian-Hermite moments. The experiment results are reported, which show the good performance of our method.

[1]  Jun Shen,et al.  An optimal linear operator for step edge detection , 1992, CVGIP Graph. Model. Image Process..

[2]  Olaf Munkelt,et al.  Adaptive Background Estimation and Foreground Detection using Kalman-Filtering , 1995 .

[3]  Jun Shen.  Orthogonal Gaussian-Hermite moments for image characterization , 1997, Other Conferences.

[4]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Robert C. Bolles,et al.  Background modeling for segmentation of video-rate stereo sequences , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Abdol-Reza Mansouri,et al.  Region Tracking via Level Set PDEs without Motion Computation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Jun Shen,et al.  Two-dimensional local moment, surface fitting and their fast computation , 1994, Pattern Recognit..

[9]  Edward H. Adelson,et al.  Analyzing and recognizing walking figures in XYT , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  R. Nelson,et al.  Low level recognition of human motion (or how to get your man without finding his body parts) , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[11]  Christoph Bregler,et al.  Learning and recognizing human dynamics in video sequences , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Michael Isard,et al.  Learning to Track the Visual Motion of Contours , 1995, Artif. Intell..

[13]  Jun Shen,et al.  Orthogonal Legendre moments and their calculation , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[14]  Randal C. Nelson.  Qualitative detection of motion by a moving observer , 2004, International Journal of Computer Vision.

[15]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[16]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[18]  P. Green.  On Use of the EM Algorithm for Penalized Likelihood Estimation , 1990 .

[19]  Wei Shen,et al.  Orthogonal Moments and their Application to Motion Detection in Image Sequences , 2004, Int. J. Inf. Acquis..

[20]  Jun Shen,et al.  Image characterization by fast calculation of Legendre moments , 1996, Remote Sensing.

[21]  Jun Shen,et al.  Moving object detection using orthogonal Gaussian-Hermite moments , 2004, IS&T/SPIE Electronic Imaging.

[22]  Randal C. Nelson,et al.  Detecting activities , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Wei Shen,et al.  On Geometric and Orthogonal Moments , 2000, Int. J. Pattern Recognit. Artif. Intell..