Invariant Image Recognition by Zernike Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analysis of Class Separation and Combination of Class-Dependent Features for Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zernike moment-based image analysis and its application
Pattern Recognition Letters
Complete Sets of Complex Zernike Moment Invariants and the Role of the Pseudoinvariants
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gradient-based polyhedral segmentation for range images
Pattern Recognition Letters
Oriya Handwritten Numeral Recognition Syste
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Wavelet networks for nonlinear system modeling
Neural Computing and Applications
Immune clonal selection wavelet network based intrusion detection
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
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This paper presents a robust automatic shape classifier using modular wavelet networks (MWNs). A shape descriptor is constructed based on a combination of global geometric features (modified Zernike moments and circularity features) and local intensity features (ordered histogram of image gradient orientations). The proposed method utilizes a supervised modular wavelet network to perform shape classification based on the extracted shape descriptors. Affine invariance is achieved using a novel eigen-based normalization approach. Furthermore, appropriate shape features are selected based on the inter- and intra-class separation indices. Therefore, the proposed classifier is robust to scale, translation, rotation and noise. Modularity is introduced to the wavelet network to decompose the complex classifier into an ensemble of simple classifiers. Wavelet network parameters are learned using an extended Kalman filter (EKF). The classification performance of proposed approaches is tested on a variety of standard symbol data sets (i.e., mechanical tools, trademark, and Oriya numerals) and the average classification accuracy is found to be 98.1% which is higher compared to other shape classifier techniques.