A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active shape models—their training and application
Computer Vision and Image Understanding
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Detection of 3D objects in cluttered scenes using hierarchical eigenspace
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Generic Object Recognition: Building and Matching Coarse Descriptions from Line Drawings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple and robust line detection algorithm based on small eigenvalue analysis
Pattern Recognition Letters
A straight line detection using principal component analysis
Pattern Recognition Letters
Designing eigenspace manifolds: with application to object identification and pose estimation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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This paper introduces a novel scheme which works on symbolizing every line in an object image for object recognition. Symbolizing is accomplished in terms of angles of intersection with regard to a line under consideration. Spatial relationship existing among the symbolized lines is represented using the notion of Triangular Spatial Relationship (TSR). A set of quadruples which preserves the TSR is subjected to principal component analysis to obtain the principal component vectors. These vectors are then stored in the knowledgebase for the purpose of recognition. Experimental results demonstrate that the proposed approach is efficient, invariant to linear transformations and capable of learning. To substantiate the success of the proposed model, a comparative study is performed with Murase and Nayar approach.