2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)最新文献_第4页

A Performance Review of Recent Corner Detectors 最新的角检测器性能综述

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691475

M. Awrangjeb, Guojun Lu

{"title":"A Performance Review of Recent Corner Detectors","authors":"M. Awrangjeb, Guojun Lu","doi":"10.1109/DICTA.2013.6691475","DOIUrl":"https://doi.org/10.1109/DICTA.2013.6691475","url":null,"abstract":"Contour-based corner detectors directly or indirectly estimate a significance measure (eg, curvature) on the points of a planar curve and select the curvature extrema points as corners. A number of promising contour-based corner detectors have recently been proposed. They mainly differ in how the curvature is estimated on each point of the given curve. As the curvature on a digital curve can only be approximated, it is important to estimate a curvature that remains stable against significant noises, for example, geometric transformations and compression, on the curve. Moreover, in many applications, for instance, in content-based image retrieval, a fast corner detector is a prerequisite. So, it is also a primary characteristic that how much time a corner detector takes for corner detection in a given image. In addition, different authors evaluated their detectors on different platforms using different evaluation systems. Evaluation systems that depend on human judgements and visual identification of corners are manual and too subjective. Application of a manual system on a large test database will be expensive. Therefore, it is important to evaluate the detectors on a common platform using an automatic evaluation system. This paper first reviews six most recent and highly performed corner detectors and analyse their theoretical running time. Then it uses an automatic evaluation system to analyse their performance. Both the robustness to noise and efficiency are estimated to rank the detectors.","PeriodicalId":231632,"journal":{"name":"2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121420034","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

Pose Estimation of Ad-Hoc Mobile Camera Networks 自组织移动摄像机网络的姿态估计

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691514

Zsolt Sánta, Z. Kato

引用次数: 3

An Improved Restoration Method for Non-Uniformly Warped Images Using Optical Flow Technique 一种改进的基于光流技术的非均匀畸变图像恢复方法

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691481

K. K. Halder, M. Tahtali, S. Anavatti

引用次数: 2

Large-Scale Analysis of Formations in Soccer 足球阵型的大规模分析

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691503

Xinyu Wei, Long Sha, P. Lucey, S. Morgan, S. Sridharan

{"title":"Large-Scale Analysis of Formations in Soccer","authors":"Xinyu Wei, Long Sha, P. Lucey, S. Morgan, S. Sridharan","doi":"10.1109/DICTA.2013.6691503","DOIUrl":"https://doi.org/10.1109/DICTA.2013.6691503","url":null,"abstract":"Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a \"role-representation\", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.","PeriodicalId":231632,"journal":{"name":"2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128484516","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: 65

Semi-Binary Based Video Features for Activity Representation 基于半二进制的活动表示视频特征

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691527

Sabanadesan Umakanthan, S. Denman, C. Fookes, S. Sridharan

{"title":"Semi-Binary Based Video Features for Activity Representation","authors":"Sabanadesan Umakanthan, S. Denman, C. Fookes, S. Sridharan","doi":"10.1109/DICTA.2013.6691527","DOIUrl":"https://doi.org/10.1109/DICTA.2013.6691527","url":null,"abstract":"Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detector-descriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio- temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices. We evaluate the combination of detector-descriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.","PeriodicalId":231632,"journal":{"name":"2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132723667","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

A 3D-2D Image Registration Algorithm for Kinematic Analysis of the Knee after Total Knee Arthroplasty (TKA) 全膝关节置换术后膝关节运动分析的3D-2D图像配准算法

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691472

M. Hossain, A. Muhit, M. Pickering, J. Scarvell, Paul N. Smith

引用次数: 3

Incremental Learning with Soft-Biometric Features for People Re-Identification in Multi-Camera Environments 基于软生物特征的增量学习在多相机环境下对人的再识别

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691500

Daniela Moctezuma, C. Conde, Isaac Martín de Diego, E. Cabello

{"title":"Incremental Learning with Soft-Biometric Features for People Re-Identification in Multi-Camera Environments","authors":"Daniela Moctezuma, C. Conde, Isaac Martín de Diego, E. Cabello","doi":"10.1109/DICTA.2013.6691500","DOIUrl":"https://doi.org/10.1109/DICTA.2013.6691500","url":null,"abstract":"In this paper, a solution for the appearance based people re-identification problem in a non-overlapping multicamera surveillance environment is presented. For this purpose, an incremental learning approach and a SVM classifier have been considered. The proposed methods update the appearance model across different camera conditions in three different ways: based on time lapses, on change of camera and on the automatic selection of the most representative samples. In order to test the proposed methods, a complete database was acquired at Barajas international airport (the MUBA proposed database). Further the well known PETS 2006 and PETS 2009 databases were considered. The system has been designed for video surveillance security. The main idea of this system is that, in an initial point, the suspect is manually identified by the user. Then, from that moment, the system is able to identify the selected subject across the different cameras in the surveillance area. The results obtained show the importance of the model update and the huge potential of the incremental learning approach.","PeriodicalId":231632,"journal":{"name":"2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130627927","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

Sketch-Based Image Retrieval by Size-Adaptive and Noise-Robust Feature Description 基于尺寸自适应和噪声鲁棒特征描述的素描图像检索

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691528

Houssem Chatbri, K. Kameyama, P. Kwan

引用次数: 12

Swimmer Localization from a Moving Camera 移动摄像机对游泳者的定位

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691533

Long Sha, P. Lucey, S. Morgan, D. Pease, S. Sridharan

{"title":"Swimmer Localization from a Moving Camera","authors":"Long Sha, P. Lucey, S. Morgan, D. Pease, S. Sridharan","doi":"10.1109/DICTA.2013.6691533","DOIUrl":"https://doi.org/10.1109/DICTA.2013.6691533","url":null,"abstract":"At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.","PeriodicalId":231632,"journal":{"name":"2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115980958","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

3D Registration in Dark Environments Using RGB-D Cameras 使用RGB-D相机在黑暗环境中的3D配准

2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) Pub Date : 2013-12-23 DOI: 10.1109/DICTA.2013.6691470

K. Yousif, A. Bab-Hadiashar, R. Hoseinnezhad

引用次数: 7