2009 Digital Image Computing: Techniques and Applications最新文献

Multi-projective Parameter Estimation for Sets of Homogeneous Matrices 齐次矩阵集的多投影参数估计

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.27

W. Chojnacki, R. Hill, A. Hengel, M. Brooks

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引用次数: 2

Self Occlusions and Graph Based Edge Measurement Schemes for Visual Tracking Applications 视觉跟踪应用的自遮挡和基于图形的边缘测量方案

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.74

Andrew W. B. Smith, B. Lovell

引用次数: 0

Refining Local 3D Feature Matching through Geometric Consistency for Robust Biometric Recognition 基于几何一致性的局部三维特征匹配鲁棒性生物特征识别

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.87

S. Islam, Rowan Davies

引用次数: 5

A Low Complexity Algorithm for Global Motion Parameter Estimation Targeting Hardware Implementation 一种低复杂度的全局运动参数估计算法

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.11

Md. Nazmul Haque, Moyuresh Biswas, M. Pickering, M. Frater

引用次数: 0

Combined Time Domain and Spectral Domain Data Compression for Fast Multispectral Imagery Updating 结合时域和谱域数据压缩的多光谱图像快速更新

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.54

Md. Al Mamun, X. Jia, M. Ryan

引用次数: 5

Content-Based Video Retrieval (CBVR) System for CCTV Surveillance Videos 基于内容的CCTV监控视频检索系统

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.36

Yan Yang, B. Lovell, F. Dadgostar

{"title":"Content-Based Video Retrieval (CBVR) System for CCTV Surveillance Videos","authors":"Yan Yang, B. Lovell, F. Dadgostar","doi":"10.1109/DICTA.2009.36","DOIUrl":"https://doi.org/10.1109/DICTA.2009.36","url":null,"abstract":"The inherent nature of image and video and its multi-dimension data space makes its processing and interpretation a very complex task, normally requiring considerable processing power. Moreover, understanding the meaning of video content and storing it in a fast searchable and readable form, requires taking advantage of image processing methods, which when running them on a video stream per query, would not be cost effective, and in some cases is quite impossible due to time restrictions. Hence, to speed up the search process, storing video and its extracted meta-data together is desired. The storage model itself is one of the challenges in this context, as based on the current CCTV technology; it is estimated to require a petabyte size data management system. This estimate however, is expected to grow rapidly as current advances in video recording devices are leading to higher resolution sensors, and larger frame size. On the other hand, the increasing demand for object tracking on video streams has invoked the research on Content-Based Image Retrieval (CBIR) and Content-Based Video Retrieval (CBVR). In this paper, we present the design and implementation of a framework and a data model for CCTV surveillance videos on RDBMS which provides the functions of a surveillance monitoring system, with a tagging structure for event detection. On account of some recent results, we believe this is a promising direction for surveillance video search in comparison to the existing solutions.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124047457","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: 21

Paper Fingerprinting Using alpha-Masked Image Matching 基于蒙面图像匹配的纸张指纹识别

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.82

Tuan Q. Pham, S. Perry, P. Fletcher

引用次数: 9

Applying Sum and Max Product Algorithms of Belief Propagation to 3D Shape Matching and Registration 信念传播的和与最大积算法在三维形状匹配配准中的应用

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.70

Pengdong Xiao, N. Barnes, P. Lieby, T. Caetano

引用次数: 2

Greedy Approximation of Kernel PCA by Minimizing the Mapping Error 最小化映射误差的核主成分分析贪心逼近

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.57

Peng Cheng, W. Li, P. Ogunbona

引用次数: 4

Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint 基于Contourlet和非下采样Contourlet组合变换的掌纹个人识别方法

2009 Digital Image Computing: Techniques and Applications Pub Date : 2009-12-01 DOI: 10.1109/DICTA.2009.73

Hassan Masood, Mohammad Asim, Mustafa Mumtaz, A. Mansoor

{"title":"Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint","authors":"Hassan Masood, Mohammad Asim, Mustafa Mumtaz, A. Mansoor","doi":"10.1109/DICTA.2009.73","DOIUrl":"https://doi.org/10.1109/DICTA.2009.73","url":null,"abstract":"Palmprint based personal verification is an accepted biometric modality due to its reliability, ease of acquisition and user acceptance. This paper presents a novel palmprint based identification approach which draw on the textural information available on the palmprint by utilizing a combination of Contourlet and Non Subsampled Contourlet Transforms. Center of the palm is computed using the Distance Transform whereas the parameters of best fitting ellipse help determine the alignment of the palmprint. ROI of 256X256 pixels is cropped around the center, and subsequently it is divided into fine slices, using iterated directional filterbanks. Next, directional energy components for each block of the decomposed subband outputs are computed using Contourlet and Non Subsampled Contourlet Transforms. The proposed algorithm captures global details in a palmprint as compact fixed length palm codes for Contourlet and NSCT respectively which are further concatenated at feature level for identification using normalized Euclidean distance classifier. The proposed algorithm is tested on a total of 500 palm images of GPDS Hand database, acquired from University of Las Palmas de Gran Canaria. The experimental results were compiled for individual transforms as well as for their optimized combination at feature level. CT based approach demonstrated the Decidability Index of 2.6212 and Equal Error Rate (EER) of 0.7082% while NSCT based approach depicted Decidability Index of 2.7278 and EER of 0.5082%. The feature level fusion achieved Decidability Index of 2.7956 and EER of 0.3112%.","PeriodicalId":277395,"journal":{"name":"2009 Digital Image Computing: Techniques and Applications","volume":"62 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128692800","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