An Autoregressive Model Approach to Two-Dimensional Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic rotational symmetry determination for shape analysis
Pattern Recognition
Fast classification of discrete shape contours
Pattern Recognition
Shape analysis based on boundary curve segmentation
Proceedings of the NATO Advanced Research workshop on Real-time object measurement and classification
2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complex Autoregressive Model for Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shapes Recognition Using the Straight Line Hough Transform: Theory and Generalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Polygonal Approximations by Newton's Method
IEEE Transactions on Computers
Fourier Descriptors for Plane Closed Curves
IEEE Transactions on Computers
Concept, content and the convict
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A robust shape retrieval method based on hough-radii
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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We present a new method for feature extraction of two-dimensional shape information based on segmentation of the boundary curve. This approach partitions closed shapes into segments and finds their angular spans. The number of segments and the angular spans form the first two feature parameters of a given shape. Fourier coefficients of all segments constitute the final feature parameters. The algorithm renders the shapes independent of scale, rotation and translation. The main advantage of this method is to speed up substantially the recognition process of the shapes, mainly because it is possible to design the classification rule in a hierarchical way. It is therefore suitable for objects to be sorted in a factory environment where the silhouette boundary supplies sufficient information for identification.