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[1]陈阳,覃鸿,李卫军,等.仿生模式识别技术研究与应用进展[J].智能系统学报编辑部,2016,11(1):1-14.[doi:10.11992/tis.201506011]
　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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仿生模式识别技术研究与应用进展

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《智能系统学报》编辑部[ISSN 1673-4785/CN 23-1538/TP] 卷: 11 期数: 2016年第1期页码: 1-14 栏目: 综述出版日期: 2016-02-25

Title:: Progress in research and application of biomimetic pattern recognition technology

作者:: 陈阳¹, 覃鸿², 李卫军², 周新奇³, 董肖莉², 张丽萍², 李浩光²; 1. 工业和信息化部中国电子信息产业发展研究院, 北京 100846;
2. 中国科学院半导体研究所高速电路与神经网络实验室, 北京 100083;
3. 聚光科技(杭州)股份有限公司, 浙江杭州 310052

Author(s):: CHEN Yang¹, QIN Hong², LI Weijun², ZHOU Xinqi³, DONG Xiaoli², ZHANG Liping², LI Haoguang²; 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

关键词:: 模式识别; 仿生模式识别; 同源连续性; 拓扑分析; 覆盖算法; 目标识别; 生物特征识别; 文本识别

Keywords:: pattern recognition; biomimetic pattern recognition; homology continuity; topological analysis; covering algorithm; object recognition; biometric feature identification; text recognition

分类号:: TP391

DOI:: 10.11992/tis.201506011

摘要:: 回顾了仿生模式识别与传统模式识别的本质区别,与传统模式识别"分类划分"思想不同,仿生模式识别把模式识别问题看成是各类样本的"认识",并将"同源连续性"规律作为先验知识,用高维空间几何形体覆盖方法实现对同类事物的学习,因此克服了传统模式识别的缺点。其有效性逐渐受到学者的广泛关注。分析总结了目前已有的仿生模式识别方法的研究和应用,方法研究包括样本点分布的拓扑分析、覆盖算法和重叠空间中样本的归属;应用研究方面包括目标识别、生物特征识别、文本识别、近红外光谱定性分析等。分析表明仿生模式识别是创新、有效的模式识别方法。最后指出同类样本点分布流形的分析方法和高维空间拓扑理论与算法研究等是仿生模式识别未来重要的发展方向。

Abstract:: 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.

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备注/Memo

收稿日期:2015-06-08;改回日期:。
基金项目:国家自然科学基金资助项目(61572458);国家重大科学仪器设备开发专项项目(2014YQ470377);国家公派访问学者资助项目(留金发[2014]3012号).
作者简介:陈阳,女,1984年生,博士后,主要研究方向为模式识别、云计算、大数据等;覃鸿,女,1977年生,高级工程师,博士,主要研究方向为图像处理、仿生模式识别理论与方法、近红外光谱定性分析技术、高维信息计算等;李卫军,男,1975年生,研究员,博士,主要研究方向为机器视觉、模式识别与智能系统、高维计算等。主持完成多项国家"863"计划、国家自然科学基金、国际合作交流等科研项目。发表学术论文30余篇。
通讯作者:陈阳.E-mail:xz.zhou@scu.edu.cn.

更新日期/Last Update: 1900-01-01

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仿生模式识别技术研究与应用进展

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