Applying image pre-processing techniques for appearance-based human posture recognition: an experimental analysis

Authors:
M. Masudur Rahman;Seiji Ishikawa
Affiliations:
Department of Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan;Department of Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

Citing 7
Cited 0

Visual learning and recognition of 3-D objects from appearance

International Journal of Computer Vision
Moving object recognition in eigenspace representation: gait analysis and lip reading

Pattern Recognition Letters
Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

International Journal of Computer Vision
Robust recognition using eigenimages

Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Face recognition from one example view

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Separating Style and Content with Bilinear Models

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates that loose clothing such as wearing dresses and human body shapes create individual eigenspaces and, as a result, conventional appearance-based method cannot be effective for recognizing human body postures We introduce a dress effect due to loose clothing and a figure effect due to various human body shapes in this particular study This study particularly proposes an image pre-processing by ‘Laplacian of Gaussian (LoG) ' filter over input images and a ‘mean posture matrix ' for creating an eigenspace in order to overcome the preceding effects We have tested the proposed approach on various dress environments and body shapes, and robustness of the method has been demonstrated.