A genetic algorithm for affine invariant recognition of object shapes from broken boundaries
Pattern Recognition Letters
Boundary-based corner detection using eigenvalues of covariance matrices
Pattern Recognition Letters
Invariant 2D object recognition using the wavelet modulus maxima
Pattern Recognition Letters
Cooperative object recognition
Pattern Recognition Letters
Affine invariants for object recognition using the wavelet transform
Pattern Recognition Letters
Simple Gabor feature space for invariant object recognition
Pattern Recognition Letters
The application of DBF neural networks for object recognition
Information Sciences—Informatics and Computer Science: An International Journal
Pattern recognition using higher-order local autocorrelation coefficients
Pattern Recognition Letters
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This paper presents a new boundary-based part recognition method for two-dimensional part. The proposed method adopts the eigenvalues of covariance matrix, re-sampling and transformation of autocorrelation coefficient for feature extraction and the simple minimum Euclidean distance for object classification. The boundary of the binary digital object is represented into the form of the eigenvalues of covariance matrix over a given region of support, and then is further transformed with autocorrelation function. The derived features are unique and invariant to translation, rotation, and scaling. Finally, the minimum Euclidean distance is used for pattern recognition for simplicity. Twenty-five standard patterns are acquired, and for each object ten extra images using different positions, orientation and scales are digitized for system verification. The experimental results show that the proposed system achieves the high recognition rate.