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Fast Recognition of Mechanical Objects Using Neural Networks Under Robust Aspect
Abstract Image recognition systems, which are invariant to rotation and scale, can be useful for a variety of automated-tasks, and, therefore, command considerable interest. A fast and highly robust vision system is very important in real-time object recognition. Neural network, which allows large parallel interconnections, presents a promising alternative to traditional real-time object recognition techniques such as template matching. In this paper, a novel neural-network based and scale and rotation invariant object recognition system is presented, that is faster than the template matching technique traditionally used, and thus, has an edge in real-time operation. Results obtained are included in the paper that compare favorably with the template-matching technique in terms of search time.
Fast Recognition of Mechanical Objects Using Neural Networks Under Robust Aspect
Abstract Image recognition systems, which are invariant to rotation and scale, can be useful for a variety of automated-tasks, and, therefore, command considerable interest. A fast and highly robust vision system is very important in real-time object recognition. Neural network, which allows large parallel interconnections, presents a promising alternative to traditional real-time object recognition techniques such as template matching. In this paper, a novel neural-network based and scale and rotation invariant object recognition system is presented, that is faster than the template matching technique traditionally used, and thus, has an edge in real-time operation. Results obtained are included in the paper that compare favorably with the template-matching technique in terms of search time.
Fast Recognition of Mechanical Objects Using Neural Networks Under Robust Aspect
Chakraborty, A. K. (author) / Pal, Dipankar (author) / Chatterjee, Pradeep (author)
2012-01-01
8 pages
Article (Journal)
Electronic Resource
English
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