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Computer Vision, Graphics, and Image Processing
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Prediction of Speech
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Decision-making processes in pattern recognition (ACM monograph series)
Decision-making processes in pattern recognition (ACM monograph series)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction in shape recognition using segmentation of the boundary curve
Pattern Recognition Letters
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Hidden Markov Models with Spectral Features for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust feature extractions from geometric data using geometric algebra
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Using diagonals of orthogonal projection matrices for affine invariant contour matching
Image and Vision Computing
Bag-of-GraphPaths descriptors for symbol recognition and spotting in line drawings
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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A complex autoregressive model for invariant feature extraction to recognize arbitrary shapes on a plane is presented. A fast algorithm to calculate complex autoregressive coefficients and complex PARCOR coefficients of the model is also shown. The coefficients are invariant to rotation around the origin and to choice of the starting point in tracing a boundary. It is possible to make them invariant to scale and translation. Experimental results that the complicated shapes like nonconvex boundaries can be recognized in high accuracy, even in the low-order model. It is seen that the complex PARCOR coefficients tend to provide more accurate classification than the complex AR coefficients.