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2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)最新文献

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Blind image steganalysis based on reciprocal singular value curve 基于互反奇异值曲线的盲图像隐写分析
2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397519
Roya Nouri, Azadeh Mansouri
{"title":"Blind image steganalysis based on reciprocal singular value curve","authors":"Roya Nouri, Azadeh Mansouri","doi":"10.1109/IRANIANMVIP.2015.7397519","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397519","url":null,"abstract":"In this paper, a new SVD-based feature set is introduced for steganalysis both in spatial and JPEG domains. Previously, reciprocal singular value curve has been used for no-reference image quality assessment. In fact, embedding secret messages in steganographic approaches is similar to adding some weak noise to the original media. Hence, the disturbance of natural image statistics is explored to extract the feature vector for steganalysis. In the proposed scheme, the alternation of singular value curve is utilized for constructing the steganalysis feature vector. The experimental results illustrate an acceptable performance of the proposed feature in universal steganalysis.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134467130","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
Modifying ARPS method by use of global motion information for fast and precise motion estimation 利用全局运动信息改进ARPS方法，实现快速、精确的运动估计
2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-11-01 DOI: 10.1109/IRANIANMVIP.2015.7397517
M. Fani, Fatemeh Ghofrani, M. Yazdi
{"title":"Modifying ARPS method by use of global motion information for fast and precise motion estimation","authors":"M. Fani, Fatemeh Ghofrani, M. Yazdi","doi":"10.1109/IRANIANMVIP.2015.7397517","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397517","url":null,"abstract":"In this article we introduce a novel motion estimation method for video sequences. Proposed method can be employed as a part of video encoders, and decoders to increase the compression ratio, and decrease the temporal redundancies. In the presented method, first a global motion vector is computed for each frame, by use of a fast procedure. This step is in fact, the key to the whole algorithm, because its result affects the accuracy and computational load of the next steps. The obtained motion vector is then directly attributed to the blocks with the global motion. To deal with the blocks whose motions are not equal to the global motion, a fast-search method, dubbed M-ARPS (M-ARPS), is suggested. Finally, by performing thorough experiments we demonstrate the supremacy of the proposed method in relation to some popular block-based motion estimation algorithms.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122584777","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
Mixed Gaussian-impulse noise removal from highly corrupted images via adaptive local and nonlocal statistical priors 利用自适应局部和非局部统计先验去除高度损坏图像中的混合高斯脉冲噪声
2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-08-29 DOI: 10.1109/IRANIANMVIP.2015.7397507
Nasser Eslahi, Hami Mahdavinataj, A. Aghagolzadeh
{"title":"Mixed Gaussian-impulse noise removal from highly corrupted images via adaptive local and nonlocal statistical priors","authors":"Nasser Eslahi, Hami Mahdavinataj, A. Aghagolzadeh","doi":"10.1109/IRANIANMVIP.2015.7397507","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397507","url":null,"abstract":"The motivation of this paper is to introduce a novel framework for the restoration of images corrupted by mixed Gaussian-impulse noise. To this aim, first, an adaptive curvelet thresholding criterion is proposed which tries to adaptively remove the perturbations appeared during denoising process. Then, a new statistical regularization term, called joint adaptive statistical prior (JASP), is established which enforces both the local and nonlocal statistical consistencies, simultaneously, in a unified manner. Furthermore, a novel technique for mixed Gaussian plus impulse noise removal using JASP in a variational scheme is developed-we refer to it as De-JASP. To efficiently solve the above variational scheme, an efficient alternating minimization algorithm is developed based on split Bregman iterative framework. Extensive experimental results manifest the effectiveness of the proposed method comparing with the current state-of-the-art methods in mixed Gaussian-impulse noise removal.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123243042","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
A novel approach for Finger Vein verification based on self-taught learning 一种基于自学的手指静脉验证新方法
2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2015-08-15 DOI: 10.1109/IRANIANMVIP.2015.7397511
Mohsen Fayyaz, M. PourReza, M. H. Saffar, M. Sabokrou, M. Fathy
{"title":"A novel approach for Finger Vein verification based on self-taught learning","authors":"Mohsen Fayyaz, M. PourReza, M. H. Saffar, M. Sabokrou, M. Fathy","doi":"10.1109/IRANIANMVIP.2015.7397511","DOIUrl":"https://doi.org/10.1109/IRANIANMVIP.2015.7397511","url":null,"abstract":"In this paper, we propose a method for user Finger Vein Authentication (FVA) as a biometric system. Using the discriminative features for classifying theses finger veins is one of the main tips that make difference in related works, thus we propose to learn a set of representative features, based on auto-encoders. We model the represented users' finger vein structure using a Gaussian distribution. Experimental results show that our method performs like a state-of-the-art method on SDUMLA-HMT benchmark.","PeriodicalId":326511,"journal":{"name":"2015 9th Iranian Conference on Machine Vision and Image Processing (MVIP)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123643990","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: 16
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