Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
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
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
Invariant 2D object recognition using the wavelet modulus maxima
Pattern Recognition Letters
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
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
Complete invariants for robust face recognition
Pattern Recognition
A new feature extractor invariant to intensity, rotation, and scaling of color images
Information Sciences: an International Journal
Invariant 2D object recognition using KRA and GRA
Expert Systems with Applications: An International Journal
Hybrid machine learning approach for object recognition: fusion of features and decisions
Machine Graphics & Vision International Journal
Hi-index | 12.05 |
This study presents a novel invariant object recognition method for two-dimensional object. The proposed method employs the eigenvalues of covariance matrix, re-sampling, and autocorrelation transformation to extract unique features from boundary information, and then use minimum Euclidean distance method (MD) and backpropagation neural networks (BPN) for classification. The boundary of the binary digital part is first extracted and represented as the sequence of the smaller eigenvalues of covariance matrix over a given region of support. Then the sequence is re-sampled into a pre-determined number, and transformed using autocorrelation function. The experimental results reveal that the proposed method successfully derives translation, rotation, and scaling invariant features which can be classified easily with MD and BPN.