An optimum solution for scale-invariant object recognition based on the multiresolution approximation

Sung H. Yoon, Jung H. Kim, Winser E. Alexander, Seong M. Park, Kwang H. Sohn

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)889-908
Number of pages20
JournalPattern Recognition
Issue number7
Publication statusPublished - 1998 Jul 31

Bibliographical note

Funding Information:
Partially supported by McDonnell Douglas under Contract No. Z50038 and Chonnam National University, 1997 research funding and the Ministry of Information and Communications.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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