发布求助
文献互助 智能选刊 最新文献

2017 2nd International Conference on Image, Vision and Computing (ICIVC)最新文献

筛选
英文 中文
Vehicle sparse recognition via class dictionary learning 基于类字典学习的车辆稀疏识别
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984543
Jixin Liu, Ning Sun, G. Han, Hai-geng Yang
{"title":"Vehicle sparse recognition via class dictionary learning","authors":"Jixin Liu, Ning Sun, G. Han, Hai-geng Yang","doi":"10.1109/ICIVC.2017.7984543","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984543","url":null,"abstract":"As the main body of modern traffic, transport vehicle is the focus of intelligent transportation systems. For three typical vehicles (including the automobile, motorcycle and bicycle), this paper proposes a new transport vehicle recognition system via class dictionary learning. For solving problems in the traditional transport vehicle recognition under sparse recognition framework, our method use SURF feature and class dictionary learning as the core. By the experiment with heterogeneous database, the effectiveness and feasibility of this method has been verified.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131682334","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
A joint stereo matching in the pixel and image level 在像素级和图像级进行联合立体匹配
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984534
Liu Jiaoli, Zhang Linfeng, Jia Tao
{"title":"A joint stereo matching in the pixel and image level","authors":"Liu Jiaoli, Zhang Linfeng, Jia Tao","doi":"10.1109/ICIVC.2017.7984534","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984534","url":null,"abstract":"Image noise, textureless regions, and occlusions are still problems of stereo matching. We propose a novel method to address these problems. Firstly, initial disparity map and reliability map are obtained by stereo matching in the pixel level. In this stage, the proposed approach not only imposes the photo-consistency constraint, but also explicitly associates the geometric coherence to solve the problem of occlusion. However, after this stage in the textureless regions there are still some bad pixels whose disparities are wrongly estimated. In the second stage, we use twice surface interpolation in the image level to correct these bad pixels via image segmentation and initial disparity map segmentation. Quantitative evaluation results show that it outperforms all the other local methods using edge-aware filtering in terms of accuracy on Middlebury benchmark. And subjective comfort of experimental results outperform other stereo matching algorithms. The experiment results show that the proposed method can obtain accurate disparity map and handle problems of stereo matching very well.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124119751","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
Computer simulations of imaging astronomical objects through Kolmogorov turbulence 通过柯尔莫哥洛夫湍流成像天文物体的计算机模拟
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984622
A. T. Mohammed
{"title":"Computer simulations of imaging astronomical objects through Kolmogorov turbulence","authors":"A. T. Mohammed","doi":"10.1109/ICIVC.2017.7984622","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984622","url":null,"abstract":"Two-dimensional mathematical modeling and simulations are carried out to observe a reference and a binary star in the absence and presence of a Kolmogorov turbulence via ground-based optical telescopes. This involve the quantitative assessment of the modulation transfer function (MTF) of a reference star, the Fourier magnitude (FM) and the autocorrelation function (AUT) of a binary star. As a result of this assessment a second degree polynomial equation is introduced to describe the average MTF of a reference star and the average FM of an image of a binary star that observed by different telescope diameters. The results also indicate that the height of the secondary peaks of the AUT remain constant despite of the strength of atmospheric turbulence and the diameter of the telescope while the width of these peaks change significantly.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123648204","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
Evaluation and simulation of LPI radar signals' low probability of exploitation LPI雷达信号低被利用概率的评估与仿真
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984673
Shu Yirong, Cheng Zengping
{"title":"Evaluation and simulation of LPI radar signals' low probability of exploitation","authors":"Shu Yirong, Cheng Zengping","doi":"10.1109/ICIVC.2017.7984673","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984673","url":null,"abstract":"With the development of investigation technology, stealthy performance of modern LPI radar is not only related to the signal waveform, but also related to the signal processing methods the intercept receiver used. Using the most common signal processing methods extract the signal feature, and distinguish them by Neural Networks algorithm, take the recognition rate results as the metrication of LPE, to quantify and evaluate the effect that the secure waveform design have on minimizing the risk of divulging radar. And combine it with LPI coefficient to analyze signal waveform. The simulation result indicates that Poly-Phase Shift Keying signal has the best stealthy performance, Linear Frequency Modulation signal has low probability of interception, but high probability of exploitation, Frequency Shift Keying signal has the temperate stealthy feature and the result can be referred, when it comes to the LPI signal waveform design.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126151270","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
A new infrared turbulent fuzzy image restoration algorithm based on Gaussian function parameter identification 一种基于高斯函数参数辨识的红外湍流模糊图像恢复新算法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984591
Shujie Yang, Xia Ye, Shijie Zhang
{"title":"A new infrared turbulent fuzzy image restoration algorithm based on Gaussian function parameter identification","authors":"Shujie Yang, Xia Ye, Shijie Zhang","doi":"10.1109/ICIVC.2017.7984591","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984591","url":null,"abstract":"Aiming at the image distortion caused by turbulence, an infrared image fuzzy measure algorithm based on local kurtosis of wavelet transform is proposed to identify the Two-dimensional (2D) Gaussian function model of the turbulence degraded image. In the process of image restoration, the Haar wavelet transform is applied to reduce the correlation of the image region. Then, the transformed coefficients are divided into sub-blocks and normalized. Combined with the characteristics of kurtosis, the local kurtosis of the whole image is calculated to describe the image ambiguity. The simulation results show that the proposed algorithm can effectively identify the parameters of the degradation model, and the relative error is about 5.5%. Compared with the existing algorithms, the algorithm can improve the rate of parameter identification and has higher deblurring efficiency.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128188753","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: 3
Fusion of texture, color and gradient information for stereo matching cost computation 融合纹理、颜色和梯度信息进行立体匹配成本计算
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984530
Puxia Han, Meng Zhao, Shengyong Chen
{"title":"Fusion of texture, color and gradient information for stereo matching cost computation","authors":"Puxia Han, Meng Zhao, Shengyong Chen","doi":"10.1109/ICIVC.2017.7984530","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984530","url":null,"abstract":"Stereo matching is widely used in 3D reconstruction, automatic driving, image focusing and so on. The common local stereo matching algorithm is based on color and gradient features to calculate the matching cost. This paper presents a new matching cost calculation method which fuses texture, color and gradient information to reduce the error rate effectively, and obtain better optimization result. Based on the HA algorithm, we make improvements. This paper also opens up a new perspective of stereo matching, taking into account the information of multi-feature space, and enriches the intrinsic link between the various information of the image. The four standard data sets from the Middlebury website are tested, and HA algorithm results are compared, with the experimental result showing that the proposed method is more accurate.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129542718","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: 2
An improved superpixel-based saliency detection method 一种改进的超像素显著性检测方法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984648
Xin Wang, Yunyan Zhou, Chen Ning
{"title":"An improved superpixel-based saliency detection method","authors":"Xin Wang, Yunyan Zhou, Chen Ning","doi":"10.1109/ICIVC.2017.7984648","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984648","url":null,"abstract":"In this paper, an improved saliency detection method based on superpixel is proposed. First, the original image is segmented into a number of superpixels by simple linear iterative clustering, each of which has the consistent color and texture characteristics. Second, two different methods, namely, the sparse representation-based method as well as a center-surrounding idea-based approach, are applied to these superpixels to compute the initial saliency map and a center-surrounding map, respectively. Then these two maps are integrated in an additive way to obtain a modified saliency map. Compared to the initial saliency map, the modified one is more precise. Third, for the segmented superpixels, a normalized cut-based clustering method is used to cluster them into several clustering areas, and then the salient values in the same clustering area are averaged. Consequently, we can get a much more uniform saliency map. Experimental results show that, compared with the classical algorithms, the proposed method achieves a better performance since it can highlight the salient objects evenly and restrain the background clutters effectively.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129664220","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: 1
An extraction method for digital camouflage texture based on human visual perception and isoperimetric theory 基于人眼视觉感知和等距理论的数字迷彩纹理提取方法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984538
Chu Miao, Tian Shaohui
{"title":"An extraction method for digital camouflage texture based on human visual perception and isoperimetric theory","authors":"Chu Miao, Tian Shaohui","doi":"10.1109/ICIVC.2017.7984538","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984538","url":null,"abstract":"To deal with the problem of lack of subjective visual perception in texture features exacting of digital camouflage, a new extraction arithmetic based on human visual perception and isoperimetric theory is proposed in this paper. The method firstly constructs edge weight function according to human visual perception, then selects isoperimetric ratio as a as a criterion to determine the optimal threshold from the candidates, finally utilizes an iterative scheme to select multiple thresholds in order to segment image into multi-regions. The experimental results of segmentation show that our method is more effective than current threshold methods in segmentation quality.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127134278","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: 2
Discovering the pathological mechanism based on the locus interaction networks with differential analysis 基于基因座相互作用网络的差异分析发现病理机制
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984563
Wenwen Ai, Fengjing Shao, Rencheng Sun
{"title":"Discovering the pathological mechanism based on the locus interaction networks with differential analysis","authors":"Wenwen Ai, Fengjing Shao, Rencheng Sun","doi":"10.1109/ICIVC.2017.7984563","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984563","url":null,"abstract":"Discovering the pathological mechanism of genetic disease is a challenging task, but has great medical significance. In this paper, a novel method to identify the pathological mechanism of the genetic disease was proposed. To validate the validity of the method, as an example, we applied the method to discovery the pathological mechanism of the human Retinitis Pigmentosa by using the gene sequencing data of Retinitis Pigmentosa (RP) and the control group. Firstly, we constructed two locus genotypes interaction networks, which named as the control and the case. Secondly, we compared and analyzed the statistical discrepancy on the proportion and topological properties of nodes between two networks. Finally, this paper discovered one pair of genes, which were closely related to RP (Retinitis Pigmentosa). The biological significance of the results were validated by literature and bioinformatics databases.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130085776","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
A chaotic immune niche genetic algorithm for target signal selection in large scale wireless sensor networks 大规模无线传感器网络中目标信号选择的混沌免疫小生境遗传算法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-01 DOI: 10.1109/ICIVC.2017.7984682
Jie Zhou, Min Tian
{"title":"A chaotic immune niche genetic algorithm for target signal selection in large scale wireless sensor networks","authors":"Jie Zhou, Min Tian","doi":"10.1109/ICIVC.2017.7984682","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984682","url":null,"abstract":"The advance of micro-sensor, nano-systems and networking technologies shown a great potential of small-size, low energy consumption, low storage, and self-adaptation sensors. Large scale wireless sensor networks (LSWSNs) consists of some them that have sensing, computation, wireless communication, and free-infrastructure abilities. The target signal selection problem in LSWSNs attracts attention of people from academic researchers, industry, and military department. The target signal selection scheme is usually designed for LSWSNs to enhance the percentage of detected targets. However, the target signal selection problem can be formulated as a nonlinear mixed integer optimization problem, which is hard to solve. In this paper, we propose a chaotic immune niche genetic algorithm (CINGA) based target signal selection approach for maximizing the percentage of detected targets. We first formulate our objective function to maximize the percentage of detected targets under multiple constraints. The proposed algorithm combines the advantages of the high efficiency of immune operation and the global search ability of the chaotic generator. An analysis is given to show the correctness of CINGA, and simulations are conducted to demonstrate the performance improvement of CINGA against parallel genetic algorithm (PGA) and ant colony optimization (ACO). Although sub-optimal for LSWSNs, simulation results show that the proposed CINGA allows to reach higher monitoring percentage compared to PGA and ACO approach. Furthermore, it was found that the immune operation helps evolution to avoid local optima.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"160 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128976077","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: 1
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信