Real-time textured object recognition on distributed systems
This paper presents the development of a real-time system for recognition of textured objects. In contrast to current approaches which mostly rely on specialized multiprocessor architectures for fast processing, we use a distributed network architecture to support parallelism and attain real-time performance. In this paper, a new approach to linage matching is proposed as the basis of object localization and positioning, which involves dynamic texture feature extraction and hierarchical image matching. A mask based stochastic method is introduced to extract feature points for matching. Our experimental results demonstrate that the combination of texture feature extraction and interesting point detection provides a better solution to the search of the best matching between two textured images. Furthermore, such an algorithm is implemented on a low cost heterogeneous PVM (Parallel Virtual Machine) network to speed up the processing without specific hardware requirements.
Key wordsobject recognition image matching feature extraction interesting points distance transform parallel processing distributed systems
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