2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)最新文献

Using constraint programming techniques to improve incident management process in ITIL 利用约束编程技术改进ITIL中的事件管理流程

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-10-13 DOI: 10.1109/ICAIPR.2016.7585231

Ismail Ghrab, Mounir Ketata, Zied Loukil, F. Gargouri

引用次数: 9

Latent fingerprint wavelet transform image enhancement technique for optical coherence tomography 光学相干层析中潜在指纹小波变换图像增强技术

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585203

Sisanda Makinana, Portia Khanyile, R. Khutlang

{"title":"Latent fingerprint wavelet transform image enhancement technique for optical coherence tomography","authors":"Sisanda Makinana, Portia Khanyile, R. Khutlang","doi":"10.1109/ICAIPR.2016.7585203","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585203","url":null,"abstract":"In crime investigation, fingerprint identification plays a major role in identifying culprits. However, traditional procedure of acquiring latent fingerprint tends to be destructive and leads to a limitation of not being able to do further analyses like DNA. Being able to acquire latent fingerprints without physical contact with the surface could be advantageous. These advantages are as follows; being able to acquire the imprint multiple times, there is no physical or chemical processing of a substrate, the substrate can be concurrently analysed for DNA and can provide a non-destructive lifting of the fingerprint. An Optical Coherence Tomography machine is one of the promising technology that may be used for imaging latent fingerprint without contacting or destructing the fingerprint impression left on a substrate. However, owing to the coherent nature of the image formation process, OCT images suffer from speckle noise which limits the contrast of OCT images. In this paper, an algorithm that enhances this OCT latent fingerprint image to ensure reliable extraction of features is proposed. To test the proposed algorithm latent prints were collected and stored as a database. Two statistical and biometric system measurement namely False Match Rate (FMR) and Equal Error Rate (EER) were used. The results of these two measures gives the FMR of 3% and EER of 1.9% for denoised images which is better than non-denoised images where the EER is 8.7%.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Adaptive algorithm of generator reactive power control range expansion in national grid system 国网发电机无功控制范围扩展的自适应算法

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585221

Maciej Soltysiak, P. Kolendo, Michal Szuca, Tomasz Ogryczak

引用次数: 1

Building of hardware-in-the-loop simulation platform based on vehicle-infrastructure cooperation environment 基于车-基础设施协同环境的半实物仿真平台构建

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585218

Shuoji Feng, Z. Guan, F. Du

引用次数: 1

A detection method for network security based on the combination of support vector machine 基于支持向量机组合的网络安全检测方法

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585222

Xiaoqi Gu, Xiaoyong Li

引用次数: 1

Features selection for building an early diagnosis machine learning model for Parkinson's disease 建立帕金森病早期诊断机器学习模型的特征选择

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585225

A. Soliman, Mohamed Fares, M. Elhefnawi, Mahmoud Al-Hefnawy

{"title":"Features selection for building an early diagnosis machine learning model for Parkinson's disease","authors":"A. Soliman, Mohamed Fares, M. Elhefnawi, Mahmoud Al-Hefnawy","doi":"10.1109/ICAIPR.2016.7585225","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585225","url":null,"abstract":"In this work, different approaches were evaluated to optimize building machine learning classification models for the early diagnosis of the Parkinson disease. The goal was to sort the medical measurements and select the most relevant parameters to build a faster and more accurate model using feature selection techniques. Decreasing the number of features to build a model could lead to more efficient machine learning algorithm and help doctors to focus on what are the most important measurements to take into account. For feature selection we compared the Filter and Wrapper techniques. Then we selected a good machine learning algorithm to detect which technique could help us by calculate the crossover scores for each technique. This research is based on a dataset which was created by Athanasius Tsanas and Max Little of the University of Oxford, in collaboration with 10 medical centers in the US and Intel Corporation. This target of these medical measurements is to find the Unified Parkinson's disease rating scale (UPDRS) which is the most commonly used scale for clinical studies of Parkinson's disease.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115652761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Motion background modeling based on context-encoder 基于上下文编码器的运动背景建模

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585207

Zhenshen Qu, Shuanghui Yu, Mengyu Fu

引用次数: 11

Modeling and data processing of information systems 信息系统建模和数据处理

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585229

G. Panayotova, G. Dimitrov, Pavel Petrov, Bychkov Os

引用次数: 38

Multi-view matching points extraction algorithm based on union find sets 基于联合查找集的多视图匹配点提取算法

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585209

Jun Lu, Baoming Zhang, Haitao Guo, Chuan Zhao

{"title":"Multi-view matching points extraction algorithm based on union find sets","authors":"Jun Lu, Baoming Zhang, Haitao Guo, Chuan Zhao","doi":"10.1109/ICAIPR.2016.7585209","DOIUrl":"https://doi.org/10.1109/ICAIPR.2016.7585209","url":null,"abstract":"The extraction of multi-view matching points is one of the key elements in 3D reconstruction of multi-view image scene, because the extraction results will directly affect the accuracy of 3D reconstruction. With the conversion from the extraction of multi-view matching points to dynamic connectivity, a solution based on the Union Find algorithm was designed. The efficient tree structure with parent-link was used to organize the nodes in the Union Find sets, so that it was only needed to modify the addressing parameter of a single node in each process of adding the matching points pair, which avoided the computational process of the traversal array to compare with addressing parameter and improved the efficiency to find and modify. At the same time, the weighted method was applied to optimize the algorithm and the weighted encoding method was used to replace the commonly used hard encoding, which can balance the dendrogram structure and reduce the average depth of nodes in the tree. Experimental results of multiple groups of image sets showed that, compared with the traditional breadth-first search algorithm, the algorithm based on Union Find had higher reliability and computational efficiency.","PeriodicalId":127231,"journal":{"name":"2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR)","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115814052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance analysis of the method for social search of information in university information systems 高校信息系统中信息社会化搜索方法的性能分析

2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR) Pub Date : 2016-09-01 DOI: 10.1109/ICAIPR.2016.7585228

G. Dimitrov, G. Panayotova, I. Garvanov, Bychkov Os, Pavel Petrov, Angel Angelov

引用次数: 12