[1]CHEN Yang,QIN Hong,LI Weijun,et al.Progress in research and application of biomimetic pattern recognition technology[J].CAAI Transactions on Intelligent Systems,2016,11(1):1-14.[doi:10.11992/tis.201506011]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
11
Number of periods:
2016 1
Page number:
1-14
Column:
综述
Public date:
2016-02-25
- Title:
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Progress in research and application of biomimetic pattern recognition technology
- Author(s):
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CHEN Yang1; QIN Hong2; LI Weijun2; ZHOU Xinqi3; DONG Xiaoli2; ZHANG Liping2; LI Haoguang2
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1. China Center of Information Industry Development, Ministry of Industry and Information Technology of the People’s Republic of China, Beijing 100846, China;
2. Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China;
3. Focused Photonics(Hangzhou), Inc., Hangzhou 310052, China
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- Keywords:
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pattern recognition; biomimetic pattern recognition; homology continuity; topological analysis; covering algorithm; object recognition; biometric feature identification; text recognition
- CLC:
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TP391
- DOI:
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10.11992/tis.201506011
- Abstract:
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An essential difference between traditional pattern recognition and biomimetic pattern recognition (BPR) is reviewed. Different from the idea of "matter classification" of traditional pattern recognition, BPR considers the problem of pattern recognition as the "cognition" of every type of sample, uses the principle of "homology continuity" as a priori knowledge, and performs class recognition by a union of geometrical cover sets in high-dimensional space and feature space, thus overcoming the shortcomings of traditional pattern recognition. The effectiveness of BPR has gradually drawn extensive attention from scholars. In this study, research on BPR and its applications are summarized. The research method includes the topological analysis of the distribution of sample points, covering algorithm research, and a sample’s attribute in the overlapping space. Applications of BPR involve object recognition, biometric identification, text recognition, NIR spectroscopy qualitative analysis, and so on. Results show that BPR is an innovative and effective means of pattern recognition. Finally, important development directions of BPR are reported, such as manifold analytical methods of sample distribution in the same class, topological theory, and algorithm research in a high-dimensional space.