[1]陈 扬,曾 诚,程 成,等.一种基于CNN的足迹图像检索与匹配方法[J].南京师范大学学报(工程技术版),2018,18(03):039.[doi:10.3969/j.issn.1672-1292.2018.03.006]
 Chen Yang,Zeng Cheng,Cheng Cheng,et al.A CNN-based Approach to Footprint Image Retrieval and Matching[J].Journal of Nanjing Normal University(Engineering and Technology),2018,18(03):039.[doi:10.3969/j.issn.1672-1292.2018.03.006]
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一种基于CNN的足迹图像检索与匹配方法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
18卷
期数:
2018年03期
页码:
039
栏目:
人工智能算法与应用专栏
出版日期:
2018-09-30

文章信息/Info

Title:
A CNN-based Approach to Footprint Image Retrieval and Matching
文章编号:
1672-1292(2018)03-0039-07
作者:
陈 扬12曾 诚3程 成1邹恩岑1顾建伟12陆 悠1奚雪峰12
(1.苏州科技大学电子与信息工程学院,江苏 苏州 215009)(2.苏州科技大学苏州市虚拟现实智能交互及应用技术重点实验室,江苏 苏州 215009)(3.昆山市公安局指挥中心,江苏 苏州 215300)
Author(s):
Chen Yang12Zeng Cheng3Cheng Cheng1Zou Encen1Gu Jianwei12Lu You1Xi Xuefeng12
(1.School of Electronic and Information Engineering,Suzhou University of Science and Technology,Suzhou 215009,China)(2.Suzhou Key Laboratory of Virtual Reality and Intelligent Interaction,Suzhou University of Science and Technology,Suzhou 215009,China)(3.Command Center of Kunshan Public Security Bureau,Suzhou 215300,China)
关键词:
深度学习卷积神经网络足迹检索图像处理
Keywords:
deep learningconvolutional neural networkfootprint searchingimage processing
分类号:
TP183
DOI:
10.3969/j.issn.1672-1292.2018.03.006
文献标志码:
A
摘要:
足迹图像作为犯罪现场的重要痕迹物证之一,在破解串并案上有着不可忽视的作用. 传统的足迹图像检索与匹配,需要耗费大量的时间与人力,极大地影响了破案进展. 卷积神经网络(CNN)在图像识别与检索上表现出很好的效果. 面向公安足迹图像比对实战需求,提出了一种基于卷积神经网络的足迹图像检索与匹配方法,对检索结果设置不同检索区,可以满足不同业务需求. 初步实验表明该方法的有效性和实用性.
Abstract:
Footprint images,as one of the important evidences of crime scenes,can’t be ignored in the cracking of serial cases. Traditional footprint comparison and retrieval require a lot of time and manpower,greatly affecting the progress of the case. Convolutional Neural Network(CNN)has shown good results in image recognition and retrieval. In order to meet the actual needs of public security footprint image retrieval,this paper proposes an approach to footprint image retrievaling and matching based on convolutional neural network,and sets different search areas for search results to meet different business requirements. Preliminary experiments show that the proposed approach is effective and practical.

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备注/Memo

备注/Memo:
收稿日期:2018-04-18.
基金项目:国家自然科学基金(61750110534、61728205)、苏州市科技发展计划(重点实验室SZS201609/产业前瞻性项目SYG201707).
通讯联系人:奚雪峰,博士,副教授,研究方向:机器学习、数据挖掘. E-mail:xfxi@mail.usts.edu.cn
更新日期/Last Update: 2018-09-30