2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)最新文献_第3页

Pornographic video classification using fast motion Features 使用快速动作特征的色情视频分类

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103714

Jung-Jae Yu, S. Han

引用次数: 1

Autonomous flight system using marker recognition on drone 无人机标记识别自主飞行系统

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103712

Jin Kim, Yoon Suk Lee, Sang Su Han, Sangkwon Kim, G. Lee, Ho Jun Ji, Hye Ji Choi, K. Choi

引用次数: 15

Rigid and non-rigid object image matching using deformable object image discrimination 利用可变形物象判别进行刚性和非刚性物象匹配

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103704

J. Feng, In-su Won, Jae-hyup Jeong, D. Jeong

引用次数: 1

Depth extended online RPCA with spatiotemporal constraints for robust background subtraction 基于时空约束的深度扩展在线RPCA鲁棒背景减法

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103745

S. Javed, T. Bouwmans, Soon Ki Jung

{"title":"Depth extended online RPCA with spatiotemporal constraints for robust background subtraction","authors":"S. Javed, T. Bouwmans, Soon Ki Jung","doi":"10.1109/FCV.2015.7103745","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103745","url":null,"abstract":"The detection of moving objects is the first step in video surveillance systems. But due to the challenging backgrounds such as illumination conditions, color saturation, and shadows, etc., the state of the art methods do not provide accurate segmentation using only a single camera. Recently, subspace learning model such as Robust Principal Component analysis (RPCA) shows a very nice framework towards object detection. But, RPCA presents the limitations of computational and memory issues due to the batch optimization methods, and hence it cannot process high dimensional data. Recent research on RPCA methods such as Online RPCA (OR-PCA) alleviates the traditional RPCA limitations. However, OR-PCA using only color or intensity features shows a weak performance specially when the background and foreground objects have a similar color or shadows appear in the background scene. To handle these challenges, this paper presents an extension of OR-PCA with the integration of depth and color information for robust background subtraction. Depth is less affected by shadows or background/foreground color saturation issues. However, the foreground object may not be detected when it is far from the camera field as depth is less useful without color information. We show that the OR-PCA including spatiotemporal constraints provides accurate segmentation with the utilization of both color and depth features. Experimental evaluations on a well-defined benchmark dataset with other methods demonstrate that our proposed technique is a top performer using color and range information.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134291543","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: 19

Evaluation of foreground detection methodology for a moving camera 移动摄像机前景检测方法的评价

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103752

T. Minematsu, Hideaki Uchiyama, Atsushi Shimada, H. Nagahara, R. Taniguchi

{"title":"Evaluation of foreground detection methodology for a moving camera","authors":"T. Minematsu, Hideaki Uchiyama, Atsushi Shimada, H. Nagahara, R. Taniguchi","doi":"10.1109/FCV.2015.7103752","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103752","url":null,"abstract":"Detection of moving objects is one of the key steps for vision based applications. Many previous works leverage background subtraction using background models and assume that image sequences are captured from a stationary camera. These methods are not directly applied to image sequences from a moving camera because both foreground and background objects move with respect to the camera. One of the approaches to tackle this problem is to estimate background movement by computing pixel correspondences between frames such as homography. With this approach, moving objects can be detected by using existing background subtraction. In this paper, we evaluate detection of foreground objects for image sequences from a moving camera. Especially, we focus on homography as a camera motion. In our evaluation we change the following parameters: changing feature points, the number of them and estimation methods of homography. We analyze its effect on detection of moving objects in regard to detection accuracy, processing time. Through experiments, we show requirement of background models in image sequences form a moving camera.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129918274","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

Layered optical tomography of multiple scattering media with combined constraint optimization 结合约束优化的多散射介质分层光学层析成像

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103735

Bingzhi Yuan, Toru Tamaki, Takahiro Kushida, B. Raytchev, K. Kaneda, Y. Mukaigawa, Hiroyuki Kubo

引用次数: 3

Determination of 3D object pose in point cloud with CAD model 基于CAD模型的点云中三维物体位姿确定

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103725

D. Nguyen, J. P. Ko, J. Jeon

{"title":"Determination of 3D object pose in point cloud with CAD model","authors":"D. Nguyen, J. P. Ko, J. Jeon","doi":"10.1109/FCV.2015.7103725","DOIUrl":"https://doi.org/10.1109/FCV.2015.7103725","url":null,"abstract":"This paper introduces improvements to estimate 3D object pose from point clouds. We use point-pair feature for matching instead of traditional approaches using local feature descriptors. In order to obtain high accuracy estimation, a discriminative descriptor is introduced for point-pair features. The object model is a set of point pair descriptors computed from CAD model. The voting process is performed on a local area of each key-point to boost the performance. Due to the simplicity of descriptor, a matching threshold is defined to enable the robustness of the algorithm. A clustering algorithm is defined for grouping similar poses together. Best pose candidates will be selected for refining and final verification will be performed. The robustness and accuracy of our approach are demonstrated through experiments. Our approach can be compared to state-of-the-art algorithms in terms of recognition rates. These high accurate poses especially useful for robot in manipulating objects in the factory. Since our approach does not use color feature, it is independent to light conditions. The system give accurate pose estimation even when there is no light in the area.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130817076","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

Nonlinear discriminant analysis using K nearest neighbor estimation 基于K最近邻估计的非线性判别分析

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103744

Xuezhen Li, Takio Kurita

引用次数: 1

Evaluation of background subtraction algorithms for video surveillance 视频监控中背景减法算法的评价

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103699

Ajmal Shahbaz, Joko Hariyono, K. Jo

引用次数: 40

Tumor detection on brain MR images using regional features: Method and preliminary results 基于区域特征的脑磁共振图像肿瘤检测方法及初步结果

2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) Pub Date : 2015-05-11 DOI: 10.1109/FCV.2015.7103705

K. Oh, Soohyung Kim, Myungeun Lee

引用次数: 4