An optimal linear operator for step edge detection
CVGIP: Graphical Models and Image Processing
Qualitative detection of motion by a moving observer
International Journal of Computer Vision
Learning to track the visual motion of contours
Artificial Intelligence - Special volume on computer vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Background Modeling for Segmentation of Video-Rate Stereo Sequences
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Orthogonal Legendre Moments and Their Calculation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Rotation and translation invariants of Gaussian-Hermite moments
Pattern Recognition Letters
Image analysis by Gaussian-Hermite moments
Signal Processing
A new SVM-based image watermarking using Gaussian-Hermite moments
Applied Soft Computing
Fast computation of accurate Gaussian-Hermite moments for image processing applications
Digital Signal Processing
Engineering Applications of Artificial Intelligence
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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.