2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)最新文献_第3页

Research Progress on 3D Visualization of Flowers in China 中国花卉三维可视化研究进展

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492848

Li Hui, Zou Chengjun, Luo Min

引用次数: 0

Four-Directional Total Variation Denoising Using Fast Fourier Transform and ADMM 基于快速傅里叶变换和ADMM的四向全变分去噪

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492869

Zhuyuan Cheng, Yuqun Chen, Lingzhi Wang, Fan Lin, Haiguang Wang, Yingpin Chen

引用次数: 7

A Template Matching Method for Multi-Scale and Rotated Images Using Ring Projection Vector Conversion 基于环投影矢量转换的多尺度旋转图像模板匹配方法

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492726

Xinwei Qi, Ligang Miao

{"title":"A Template Matching Method for Multi-Scale and Rotated Images Using Ring Projection Vector Conversion","authors":"Xinwei Qi, Ligang Miao","doi":"10.1109/ICIVC.2018.8492726","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492726","url":null,"abstract":"Template matching is a process of finding template image location from a known image, which is one of the main research contents in machine vision. For the multi-scale and rotated image template matching, most of template matching algorithms usually form a templates collection with different scaling ratios templates, and then the templates in the collection are matched separately. The algorithm will greatly increase the calculation burden of template matching, and the matching efficiency will be greatly reduced. This paper proposes an algorithm for multi-scale and rotated image template matching. The algorithm first computes the ring projection vector of the template, and then, the ring projection of the scaled template can be obtained by ring projection vector conversion. The normalized cross correlation is used to calculate the similarity between the new ring projection vector and the ring projection vector of each point of the scene image. In the end the similarities determine the optimal matching position and scale ratio. Experimental results show that the proposed algorithm can accurately find the correct matching position for multi-scale and rotated image template matching.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123559030","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: 8

Review of Programming and Performance Optimization on CPU-MIC Heterogeneous System CPU-MIC异构系统的编程与性能优化研究进展

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492841

Cheng Chen, Funchun Yang, F. Wang, Liang Deng, Dan Zhao

引用次数: 2

Crack Detection Algorithm for Photovoltaic Image Based on Multi-Scale Pyramid and Improved Region Growing 基于多尺度金字塔和改进区域生长的光伏图像裂纹检测算法

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492810

Meiping Song, Dongqing Cui, Chunyan Yu, Jubai An, Chein-I. Chang, Meping Song

引用次数: 6

Design and Implementation of a Video Surveillance System for Linear Wireless Multimedia Sensor Networks 线性无线多媒体传感器网络视频监控系统的设计与实现

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492776

Nasim Abbas, Fengqi Yu

引用次数: 5

A Defect Status Detecting Method for External Gear in Railway 铁路外齿轮缺陷状态检测方法

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492899

Chuangang Wang, Fuqiang Li, Yanfeng Li, Houjin Chen, Xuyang Cao

引用次数: 0

ICIVC 2018 ICIVC 2018

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/icivc.2018.8492740

引用次数: 0

Research on Bank Anti-Fraud Model Based on K-Means and Hidden Markov Model 基于k均值和隐马尔可夫模型的银行反欺诈模型研究

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492795

Xiaoguo Wang, Hao Wu, Zhichao Yi

引用次数: 13

An Efficient Hardware Architecture for Activation Function in Deep Learning Processor 一种高效的深度学习处理器激活函数硬件结构

2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2018-06-01 DOI: 10.1109/ICIVC.2018.8492754

Lin Li, Shengbing Zhang, Juan Wu

{"title":"An Efficient Hardware Architecture for Activation Function in Deep Learning Processor","authors":"Lin Li, Shengbing Zhang, Juan Wu","doi":"10.1109/ICIVC.2018.8492754","DOIUrl":"https://doi.org/10.1109/ICIVC.2018.8492754","url":null,"abstract":"In order to explore the efficient design and implementation of activation function in deep learning processor, this paper presents an efficient five-stage pipelined hardware architecture for activation function based on the piecewise linear interpolation, and a novel neuron data-LUT address mapping algorithm. Compared with the previous designs based on serial calculation, the proposed hardware architecture can achieve at least 3 times of acceleration. Four commonly used activation functions are designed based on the proposed hardware architecture, which is implemented on the XC6VLX240T of Xilinx. The LeNet-5 and AlexNet are selected as benchmarks to test the inference accuracy of different activation functions with different piecewise numbers on the MNIST and CIFAR-10 test sets in the deep learning processor prototype system. The experiment results show that the proposed hardware architecture can effectively accomplish the relevant calculation of activation functions in the deep learning processor and the accuracy loss is negligible. The proposed hardware architecture is adaptable for numerous activation functions, which can be widely used in the design of other deep learning processors.","PeriodicalId":173981,"journal":{"name":"2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125673734","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: 9