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An optimum solution for scale-invariant object recognition based on the multiresolution approximation
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DOI：10.1016/S0031-3203(97)00111-8.png)
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Abstract
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This paper presents a multiresolution approximation approach to obtaining boundary representations for object recognition. Our technique combines a multiresolution approximation and the curvature scale-space representation for obtaining representations. Our research consists of two main parts. In the first part of our research, we introduce the continuous multiresolution approximation (CMA) in terms of the continuous wavelet transform (CWT). Then we implement a fast algorithm to compute the CMA. We apply the CMA to a boundary to obtain approximations of the boundary at various resolutions. The CMA provides a consistent interpretation of objects with scale-variations. Moreover, we can quickly compute our representations by using the fast algorithm for the CMA. In the second part, we propose three representations for object recognition which cover most boundary-based object recognition problems. All three representations use the approximations obtained by the CMA. Each representation has different features and covers different types of matching problems but all representations are constructed by using curvature zero crossings of the approximations. Our representations provide a general but reliable solution to most boundary based object matching problems. Finally, we investigate the properties of our representations such as validity, efficiency, and reliability. We verified our results experimentally to demonstrate the feasibility of using our representations for object recognition. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keywords:
object recognition
wavelet transform
neural network
pattern matching
multiresolution approximation
image analysis
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