Abstract

Patten recognition techniques are often an important component of intelligent systems to describe, classify and recognise the objects. Object recognition using linear vector quantization neural network which is trained using descriptors such as boundary and regional descriptors is presented in this paper. Out of the various descriptors available, a combination of these descriptors extracted from the input patterns is proposed for recognizing objects using a vector quantization neural network. The feature vector obtained from the object is the combination of a boundary descriptor, the signature or and a regional descriptor, the Zernike moment magnitudes of the image. The results of the testing phase are included at the end of this paper.

Keywords:
Artificial intelligence Pattern recognition (psychology) Zernike polynomials Computer science Cognitive neuroscience of visual object recognition Learning vector quantization Artificial neural network Feature vector Vector quantization Quantization (signal processing) Feature extraction Computer vision Object (grammar) Support vector machine

Metrics

4
Cited By
0.29
FWCI (Field Weighted Citation Impact)
5
Refs
0.64
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Image Retrieval and Classification Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Image and Video Retrieval Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Remote-Sensing Image Classification
Physical Sciences →  Engineering →  Media Technology

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