{"title":"Robust Lossless Data Hiding Scheme Based on Region Selection","authors":"Zhu Li-Hong, Zhou Quanr","doi":"10.1109/ICIG.2013.32","DOIUrl":"https://doi.org/10.1109/ICIG.2013.32","url":null,"abstract":"Due to its superior performance, robust loss less data hiding algorithm based on statistics shifting has become hotspot in the field of data hiding recently. In this paper, on the basis of analyzing the principle of the statistics shifting algorithm, a novel robust loss less data hiding algorithm based on region selection is proposed. The proposed scheme divides a cover image into a number of non-overlapping sub regions, and then, uses image gray level co-occurrence matrix to calculate the complexity of each regions. Finally, the secret information is embedded into the optimal sub region, which is measured by the complexity. Experimental results show that the proposed algorithm has a good performance in terms of visual quality and robustness. Compared with the previous algorithm, the PSNR of proposed scheme increases 4.55 dB, and the survival bit rate decreases of 32%.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122289785","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}
X. Chen, N. Sang, Haitao Gan, Zhiping Dan, Yanfei Chen, H. Ren
{"title":"Medical Ultrasonography Denoising Using Sparse Coding Shrinkage","authors":"X. Chen, N. Sang, Haitao Gan, Zhiping Dan, Yanfei Chen, H. Ren","doi":"10.1109/ICIG.2013.20","DOIUrl":"https://doi.org/10.1109/ICIG.2013.20","url":null,"abstract":"A locally adaptive shrinkage Bayesian estimate for medical ultrasonography denoising is proposed by exploiting the correlation among image sparse coding. The Laplacian distribution is used to model the coding coefficients. The paper deduces the MAP estimate formula and adaptive threshold. Simulation experiments are carried out to show the effectiveness of the new method. Results demonstrate that compared with classical denoising algorithms, the new method has increased peak signal-to-noise ratio (PSNR) and improved the quality of subjective visual effect. Our algorithm is also proved to be effective to the medical ultrasonography.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121062111","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}
Xiaochuan He, Zhenkuan Pan, Qian Dong, Guodong Wang
{"title":"Veins Segmentation and Three-Dimensional Reconstruction from Liver CT Images Using Multilevel OTSU Method","authors":"Xiaochuan He, Zhenkuan Pan, Qian Dong, Guodong Wang","doi":"10.1109/ICIG.2013.55","DOIUrl":"https://doi.org/10.1109/ICIG.2013.55","url":null,"abstract":"OTSU method is considered to be the best algorithm for image segmentation of threshold selection. It's very simple and it's regardless of image brightness and contrast effects. Therefore it has been widely used in digital image processing. However, in the actual image, because of the influence of noise etc., traditional OTSU algorithm cannot be obtained an accurate segmentation results. In this paper, we divide the CT images of the liver by the combination of the PM filter and local gray stretch. Then we get binary images of vascular. Finally we reconstruct the venous system in liver by the use of Visualization Toolkit (VTK) and 2D segmentation results.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127109894","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}
Wenping Wang, Yan Xu, Yuanquan Wang, Baofeng Zhang, Zuo-liang Cao
{"title":"Effective Weighted Compressive Tracking","authors":"Wenping Wang, Yan Xu, Yuanquan Wang, Baofeng Zhang, Zuo-liang Cao","doi":"10.1109/ICIG.2013.77","DOIUrl":"https://doi.org/10.1109/ICIG.2013.77","url":null,"abstract":"Compressive Tracking (CT) model is a recently proposed method for visual tracking, in which the appearance model is constructed from the features selected from the multiscale image feature space based on compressive sensing. The CT tracker has been proven to be effective. However, since it does not discriminatively consider the sample importance in its learning procedure, the CT tracker may detect the less important positive samples and, therefore, suffer from drift. In this paper, we present a novel Weighted Compressive Tracking (WCT) model based on the CT tracker. The proposed WCT tracker integrates the sample importance into an efficient online learning procedure so that the features are much more discriminative. Experimental results on challenging benchmark image sequences demonstrate that the proposed WCT tracker performs more favorably than the CT tracker. In addition, the WCT and CT trackers are also applied to the video acquired by the fisheye lens, the result of WCT tracker is very promising, whereas the CT tracker fails.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122965076","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}
{"title":"Filling Free-Form n-Sided Holes toward Blending and Decoration","authors":"Pei Zhou, W. Qian, Jun Luo, Zan Pi","doi":"10.1109/ICIG.2013.142","DOIUrl":"https://doi.org/10.1109/ICIG.2013.142","url":null,"abstract":"A new approach to G1 filling of n-sided holes with free-form boundary curves (e.g., polynomial, rational, sinusoidal or other parametric forms) is presented. It has no intrinsic limitation but adequate freedoms for shape adjustment and optimization. The given n-sided hole is filled by rotating a cubic Bézier curve C around a fixed unit vector nc at a fixed center point c of the hole. C's two end points fall on the hole boundary and c respectively. nc is C's normal at c. In this way n triangular surfaces are generated in G0 contact. Then each crease between two adjacent triangular surfaces is smoothed by sweeping another cubic Bézier curve along the crease. Thus n leafy blending patches are produced to replace the overlapped parts of the triangular surfaces. The proposed algorithm is applied to some typical examples of hole-filling on free-form surfaces, such as blending and decoration.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123043385","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}
{"title":"The Color Fusion of Infrared and Visual Images Based on NSCT","authors":"Yajie Wang, Yanyan Wu, Xiangbin Shi, Yongsheng Ye","doi":"10.1109/ICIG.2013.124","DOIUrl":"https://doi.org/10.1109/ICIG.2013.124","url":null,"abstract":"In the night vision fusion, traditional methods, such as the wavelet transform, always suffer from the problems of shift-variant, limited direction. In order to obtain a more ideal fused image, this paper proposes a new night vision fusion scheme based on NSCT (nonsubsampled contour let transform). And we can convert the fused grayscale image into the color image by YUV color space, ultimately generating the color fused image. The experimental results demonstrate that the proposed method not only keep abundant details of the background, but also improves the contrast and detect ability of the target object. And the color based on color transfer algorithm closes to natural color, superior to the traditional method such as false color, it's more conducive to identify the target and the environment for people.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123045395","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}
{"title":"Adaptive Bilateral Filtering and Its Application in Retinex Image Enhancement","authors":"Chuanmin Xiao, Zelin Shi","doi":"10.1109/ICIG.2013.15","DOIUrl":"https://doi.org/10.1109/ICIG.2013.15","url":null,"abstract":"Bilateral filtering has the advantage of keeping the edge well in image smoothing, but the selection of its parameters is difficult. In this paper, we propose an adaptive parameter estimation method for bilateral filtering, by estimating the noise variance automatically through image wavelet decomposition. When adaptive bilateral filtering method is used in Retinex image enhancement, it can either remove image noise or avoid the halo artifacts. The experimental results show that our method is effective.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128980540","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}
{"title":"Learning Encoded Facial Curvature Information for 3D Facial Emotion Recognition","authors":"Yiding Wang, Meng Meng, Qingkai Zhen","doi":"10.1109/ICIG.2013.112","DOIUrl":"https://doi.org/10.1109/ICIG.2013.112","url":null,"abstract":"In current 3D facial expression recognition system, feature extraction has always been a critical point. We focus on encoding feature by using curvature information. 3D facial expression images are described by means of four images which gray level are the value of curvature-based descriptors (principal curvatures k1, k2, mean curvature, shape index) and then encoded by LBP. SVM classifier is employed for classification. Then experimental result illustrates that the proposed feature classified by SVM is effective.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129002994","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}
{"title":"Object-Based Classification of Worldview Data for Rehabilitated Vegetation from Mining Site","authors":"Nisha Bao, B. Ye, Xiaocui Liu","doi":"10.1109/ICIG.2013.86","DOIUrl":"https://doi.org/10.1109/ICIG.2013.86","url":null,"abstract":"Vegetation mapping and classification would present valuable information for understanding and evaluating the rehabilitation process in man-made environment of mining site. In this study, object-based classification approach would be applied in rehabilitated vegetation classification using VHR Worldview-2 imagery. The results showed that the largest rehabilitated area occurred in the Hippophae rhamnoides, which can be corresponded the rehabilitated plan. The large trees that include spectrally different features such as trunks, branches and leaves have been successfully segmented into a single image object and finally classified correctly as Pupulus and Locust with the accuracy over than 90%. This study demonstrated how the combination of two new remote sensing technologies in the form of high resolution imagery and OBIA methods can be successfully combined to classify rehabilitated.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124293882","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}
{"title":"Segmentation of Pulmonary Nodules Using Fuzzy Clustering Based on Coefficient of Curvature","authors":"Kan Chen, Bin Li, Lianfang Tian, Jing Zhang","doi":"10.1109/ICIG.2013.51","DOIUrl":"https://doi.org/10.1109/ICIG.2013.51","url":null,"abstract":"Pulmonary nodules are potential manifestation of lung cancer. Accurate segmentation of juxta-vascular nodules and ground glass opacity (GGO) nodules are an important and active area of research in medical image processing. At present, the classical segmentation algorithm of pulmonary nodules can not accurately obtain the boundary information of pulmonary nodules. In order to solve the problem, a new segmentation algorithm of pulmonary nodules using Fuzzy clustering based on coefficient of curvature is proposed in this paper. Because the coefficient of curvature can effectively distinguish between pulmonary nodules and surrounding structures, and Fuzzy clustering algorithm is adopted, the boundary information of pulmonary nodules can be accurately obtained. In order to obtain initial segmentation results of pulmonary nodules, the expectation maximization algorithm is adopted. Experimental results show that the proposed algorithm can accurately obtain the boundary information of juxta-vascular nodules and GGO nodules.","PeriodicalId":425822,"journal":{"name":"2013 Seventh International Conference on Image and Graphics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121595358","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}