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

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A robust tracking method based on the correlation filter and correcting strategy 一种基于相关滤波和校正策略的鲁棒跟踪方法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984646
Changhong Liu, Xuwen Yao, Zhi‐xia Zhu, Shao-Hu Peng, Weiping Zheng
{"title":"A robust tracking method based on the correlation filter and correcting strategy","authors":"Changhong Liu, Xuwen Yao, Zhi‐xia Zhu, Shao-Hu Peng, Weiping Zheng","doi":"10.1109/ICIVC.2017.7984646","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984646","url":null,"abstract":"Visual tracking integrates the technology of image processing and pattern recognition, etc., which has a lot of potential applications, such as automatic driving, safety monitoring, etc. This paper analyzes the advantages and disadvantages of the Kernelized Correlation Filter (KCF) and Tracking-Learning-Detection (TLD), which are two kinds of trackers. TLD tracker has correcting capability whereas its performance highly depends on the tracker, which is not robust to some cases, such as tracking non-grid objects. Inversely, KCF achieves good performance in tracking non-grid objects. However, KCF behaves badly in the presence of occlusion and out-of-view and it cannot correct errors during the tracking process. According to the characteristics of the KCF and TLD, this paper proposes a robust tracking method based on the correlation filter and correcting strategy. By using the advantages of the KCF and TLD, the proposed method achieves high tracking accuracy and correcting capability. Experimental results show that the proposed method outperforms other methods (KCF, TLD, Struck, SCM, ASLA, MTT and DFT) according to the success and precision plots of OPE, SRE, and TRE, respectively.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"03 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131481211","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
A method of lane detection and tracking for expressway based on RANSAC 基于RANSAC的高速公路车道检测与跟踪方法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984519
Shuliang Zhu, Jianqiang Wang, Tao Yu, Jiao Wang
{"title":"A method of lane detection and tracking for expressway based on RANSAC","authors":"Shuliang Zhu, Jianqiang Wang, Tao Yu, Jiao Wang","doi":"10.1109/ICIVC.2017.7984519","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984519","url":null,"abstract":"Lane mark detection and tracking is essential for advanced driver assistance systems. We propose a computationally efficient lane mark detection and tracking method for expressway that can robustly and accurately detect lane marks in an image. A small size detection window scanner moving in the region of interest to determine whether there is a lane mark at the current position. This method can improve the detection accuracy and noise immunity. According to the correlations between video frames, we locate lane mark positions fast in current frame. We use an improved RANSAC method to fit the detected lane marks to straight lines. The proposed method is proved to be efficient through experiments for various complex environments.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"122 39","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113944910","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: 6
Infrared dim small target detection method based on background prediction and high-order statistics 基于背景预测和高阶统计量的红外弱小目标检测方法
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984517
Jiao Jiao, Lingda Wu
{"title":"Infrared dim small target detection method based on background prediction and high-order statistics","authors":"Jiao Jiao, Lingda Wu","doi":"10.1109/ICIVC.2017.7984517","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984517","url":null,"abstract":"In this paper, a new method based on background prediction and high-order statistics for infrared dim small target detection is proposed. Firstly, the wavelet filter is introduced to remove the target on image as noise, which could efficiently estimate the distribution of the background image. Secondly, the candidate target components are extracted from the foreground image. Finally, high-order statistics estimation method is used to get the target coordinates range and detect the target. Real images of photoelectric theodolite embedded with dim small targets are used to verify the efficiency of proposed method, and experimental results show effectiveness of our method.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133345241","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: 6
Inception-v3 for flower classification Inception-v3用于花卉分类
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984661
Xiaoling Xia, Cui Xu, Bing Nan
{"title":"Inception-v3 for flower classification","authors":"Xiaoling Xia, Cui Xu, Bing Nan","doi":"10.1109/ICIVC.2017.7984661","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984661","url":null,"abstract":"The study of flower classification system is a very important subject in the field of Botany. A classifier of flowers with high accuracy will also bring a lot of fun to people's lives. However, because of the complex background of flowers, the similarity between the different species of flowers, and the differences among the same species of flowers, there are still some challenges in the recognition of flower images. The traditional flower classification is mainly based on the three features: color, shape and texture, this classification requires people to select features for classification, and the accuracy is not very high. In this paper, based on Inception-v3 model of TensorFlow platform, we use the transfer learning technology to retrain the flower category datasets, which can greatly improve the accuracy of flower classification.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116498580","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: 325
Application of neural network based on SIFT local feature extraction in medical image classification 基于SIFT局部特征提取的神经网络在医学图像分类中的应用
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984525
Shuqi Cui, Hong Jiang, Zheng Wang, Chaomin Shen
{"title":"Application of neural network based on SIFT local feature extraction in medical image classification","authors":"Shuqi Cui, Hong Jiang, Zheng Wang, Chaomin Shen","doi":"10.1109/ICIVC.2017.7984525","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984525","url":null,"abstract":"In the medical image analysis, ROI (Region of Interest) is one of the key features of clinical diagnostic analysis. The applying of local features of ROI to the deep learning of image classification has the advantage of noise eliminating and information reducing. Based on existing research results, using Scale Invariant Feature Transformation (SIFT) algorithm combined with SVM classifier and sliding window to extract the local features and describe ROI precisely in the image. Finally, the extracted feature is used as the input layer of BP neural network in mammary gland X - ray image classification. The experimental results show that the accuracy of neural network classifier based on SIFT is 96.57%, which is 3.44% higher than that of traditional SVM classification accuracy. It is verified that our classifier is important to support clinical diagnosis and diagnosis.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115652299","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
Weighted orthogonal constrained maximum likelihood ICA algorithm and its application in image feature extraction 加权正交约束最大似然ICA算法及其在图像特征提取中的应用
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984526
Tian Tian
{"title":"Weighted orthogonal constrained maximum likelihood ICA algorithm and its application in image feature extraction","authors":"Tian Tian","doi":"10.1109/ICIVC.2017.7984526","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984526","url":null,"abstract":"The higher-order statistics based independent component analysis (ICA) algorithm can extract natural image features. Based on the maximum likelihood ICA criterion, and using the weighted orthogonal constrained natural gradient, a new ICA algorithm is proposed. Natural image feature extraction simulation results show that, compared with other ICA algorithms, the proposed algorithm has faster convergence rate, most of the extracted basis vectors are localized in space, frequency, and orientation, which can describe the features of the natural images well, and the corresponding coefficients are very sparse, obey stronger super-Gaussian distribution with very high kurtosis.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125899933","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
Detecting Chinese calligraphy style consistency by deep learning and one-class SVM 基于深度学习和一类支持向量机的中国书法风格一致性检测
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984523
Zhang Jiulong, Guo Luming, Yang Su, Sun Xudong, L. Xiaoshan
{"title":"Detecting Chinese calligraphy style consistency by deep learning and one-class SVM","authors":"Zhang Jiulong, Guo Luming, Yang Su, Sun Xudong, L. Xiaoshan","doi":"10.1109/ICIVC.2017.7984523","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984523","url":null,"abstract":"When beginners practice Chinese calligraphy, they often copy from ancient calligraphic works and try to imitate the style as closely as possible. However there are inevitably some characters whose styles are not correctly followed. Thus we are motivated to detect the style consistency of all written characters in one practice. With the styles extracted by using stacked autoencoders of deep neural network model, we discriminate correctly styled and alien styled characters using a trained one-class support vector machine. Thus we can pick out those outliers. The proposed algorithm reaches satisfactory results. The algorithm can also be applied to other image style detection problems.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130730358","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: 10
Image analysis with symmetry properties of Legendre moments 具有勒让德矩对称性的图像分析
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984583
A. Chiang, S. Liao
{"title":"Image analysis with symmetry properties of Legendre moments","authors":"A. Chiang, S. Liao","doi":"10.1109/ICIVC.2017.7984583","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984583","url":null,"abstract":"In this research, we have studied the symmetrical properties of Legendre moments. Our research leads to the conclusion that if the original image is centrally symmetrical, all Legendre moments composed of any odd order of Legendre polynomials are nil. We conducted the image reconstructions from different sets of Legendre moments, and verified the proposed symmetry properties. Our experimental results show that the reconstructed central symmetrical images from the Legendre moments with only even orders of Legendre polynomials are identical to those from the corresponding complete order sets of Legendre moments.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134325083","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
A mobile augmented reality system for exhibition hall based on Vuforia 基于Vuforia的展厅移动增强现实系统
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984714
Fuguo Peng, Jing Zhai
{"title":"A mobile augmented reality system for exhibition hall based on Vuforia","authors":"Fuguo Peng, Jing Zhai","doi":"10.1109/ICIVC.2017.7984714","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984714","url":null,"abstract":"Mobile augmented reality (MAR) is a newly-emerging technology which covers the real scene with virtual information by utilizing the mobile terminal and thus enables users to have a better understanding for and interaction with the real environment. The article makes a research on the application of MAR technology in the smart exhibition hall, expounds the technological principles and key technologies of MAR in detail and introduces the Vuforia augmented reality framework. Meanwhile, it also designs and realizes the MAR system of the exhibition hall based on Vuforia, explains the systematic structure, recognition of the creating method of the target library and working process of the system, conducts application experiments on the system and then makes summarizations and analysis on the system effects. Finally, the article discusses the deficiencies on the system and its further work.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114706355","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: 39
Ship detection in harbor area in SAR images based on constructing an accurate sea-clutter model 基于精确海杂波模型的SAR图像港区船舶检测
2017 2nd International Conference on Image, Vision and Computing (ICIVC) Pub Date : 2017-06-02 DOI: 10.1109/ICIVC.2017.7984450
Li Zhi, Qu Chang-wen, Z. Qiang, Liu Chen, Peng Shujuan, L. Jianwei
{"title":"Ship detection in harbor area in SAR images based on constructing an accurate sea-clutter model","authors":"Li Zhi, Qu Chang-wen, Z. Qiang, Liu Chen, Peng Shujuan, L. Jianwei","doi":"10.1109/ICIVC.2017.7984450","DOIUrl":"https://doi.org/10.1109/ICIVC.2017.7984450","url":null,"abstract":"Aiming at the problem of low ship detection performance of SAR image in port area, this paper proposes SAR image ship detection based on sea clutter accurate modeling in port area. In order to establish the sea clutter model accurately, this paper tries to eliminate all the pixels of land and the target of the suspected ships before modeling. Firstly, the SAR image is preprocessed by fine Lee rate ratio, and then the land mask is realized by the land-sea segmentation algorithm based on SLIC super-pixel segmentation. Secondly, the Fisher model parameters are obtained and the global threshold is obtained by dichotomy to eliminate the suspected targets. Based on the land mask and the removal and suspected ship target, the algorithm of model similarity fitting is used to self-adapt the sea clutter model distribution of SAR image in port area. Finally, to prevent from leakage, expand some size of strips on the land side of the land-sea divider line, and then conduct ship detection in with constant false alarm rate on the fitting sea clutter distribution. The simulation results verify the validity of the SAR image in ship detection in port area.","PeriodicalId":181522,"journal":{"name":"2017 2nd International Conference on Image, Vision and Computing (ICIVC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122608188","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
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