Performance evaluation of shape matching via chord length distribution
Computer Vision, Graphics, and Image Processing
HYPER: A New Approach for the Recognition and Positioning of Two-Dimensional Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Autoregressive Model Approach to Two-Dimensional Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
System identification: theory for the user
System identification: theory for the user
Using Polygons to Recognize and Locate Partially Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification of Partial 2-D Shapes Using Fourier Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour sequence moments for the classification of closed planar shapes
Pattern Recognition
Two-dimensional, model-based, boundary matching using footprints
International Journal of Robotics Research
International Journal of Robotics Research
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
Digital Image Processing
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Computer Vision
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)
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Recognition of Handwritten Hiragana Characters Based Upon a Complex Autoregressive Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models with Spectral Features for 2D Shape Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 bivariate autoregressive model is introduced for the analysis and classification of closed planar shapes. The boundary coordinate sequence of a digitized binary image is sampled to produce a polygonal approximation to an object's shape. This circular sample sequence is then represented by a vector autoregressive difference equation which models the individual Cartesian coordinate sequences as well as coordinate interdependencies. Several classification features which are functions or transformations of the estimated coefficient matrices and the associated residual error covariance matrices are developed. These features are shown to be invariant to object transformations such as translation, rotation, and scaling. Laboratory experiments involving object sets representative of industrial shapes are presented. Superior classification results are demonstrated.