2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops最新文献_第6页

A GPU-based implementation of motion detection from a moving platform 基于gpu的移动平台运动检测实现

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563096

Qian Yu, G. Medioni

引用次数: 40

Efficient scan-window based object detection using GPGPU 使用GPGPU高效的基于扫描窗口的目标检测

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563097

Li Zhang, R. Nevatia

引用次数: 62

Circular generalized cylinder fitting for 3D reconstruction in endoscopic imaging based on MRF 基于MRF的内镜成像三维重建的圆形广义圆柱体拟合

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563010

Jin Zhou, Ananya Das, Feng Li, Baoxin Li

{"title":"Circular generalized cylinder fitting for 3D reconstruction in endoscopic imaging based on MRF","authors":"Jin Zhou, Ananya Das, Feng Li, Baoxin Li","doi":"10.1109/CVPRW.2008.4563010","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563010","url":null,"abstract":"Endoscopy has become an established procedure for the diagnosis and therapy of various gastrointestinal (GI) ailments, and has also emerged as a commonly-used technique for minimally-invasive surgery. Most existing endoscopes are monocular, with stereo-endoscopy facing practical difficulties, preventing the physicians/surgeons from having a desired, realistic 3D view. Traditional monocular 3D reconstruction approaches (e.g., structure from motion) face extraordinary challenges for this application due to issues including noisy data, lack of textures supporting robust feature matching, nonrigidity of the objects, and glare artifacts from the imaging process, etc. In this paper, we propose a method to automatically reconstruct 3D structure from a monocular endoscopic video. Our approach attempts to address the above challenges by incorporating a circular generalized cylinder (CGC) model in 3D reconstruction. The CGC model is decomposed as a series of 3D circles. To reconstruct this model, we formulate the problem as one of maximum a posteriori estimation within a Markov random field framework, so as to ensure the smoothness constraints of the CGC model and to support robust search for the optimal solution, which is achieved by a two-stage heuristic search scheme. Both simulated and real data experiments demonstrate the effectiveness of the proposed approach.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"40 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113976041","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: 25

Exploiting spatio-temporal information for view recognition in cardiac echo videos 利用时空信息进行心脏回波视频的视点识别

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563008

D. Beymer, T. Syeda-Mahmood, Fei Wang

{"title":"Exploiting spatio-temporal information for view recognition in cardiac echo videos","authors":"D. Beymer, T. Syeda-Mahmood, Fei Wang","doi":"10.1109/CVPRW.2008.4563008","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563008","url":null,"abstract":"2D Echocardiography is an important diagnostic aid for morphological and functional assessment of the heart. The transducer position is varied during an echo exam to elicit important information about the heart function and its anatomy. The knowledge of the transducer viewpoint is important in automatic cardiac echo interpretation to understand the regions being depicted as well as in the quantification of their attributes. In this paper, we address the problem of inferring the transducer viewpoint from the spatio-temporal information in cardiac echo videos. Unlike previous approaches, we exploit motion of the heart within a cardiac cycle in addition to spatial information to discriminate between viewpoints. Specifically, we use an active shape model (ASM) to model shape and texture information in an echo frame. The motion information derived by tracking ASMs through a heart cycle is then projected into the eigen-motion feature space of the viewpoint class for matching. We report comparison with a re-implementation of state-of-the-art view recognition methods in echos on a large database of patients with various cardiac diseases.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123917349","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: 20

Design and calibration of a multi-view TOF sensor fusion system 多视点TOF传感器融合系统的设计与标定

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563160

Y. Kim, Derek Chan, C. Theobalt, S. Thrun

引用次数: 142

Mutual information computation and maximization using GPU 基于GPU的互信息计算与最大化

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563101

Yuping Lin, G. Medioni

引用次数: 39

Investigating how and when perceptual organization cues improve boundary detection in natural images 研究感知组织线索如何以及何时改善自然图像的边界检测

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562974

Leandro A. Loss, G. Bebis, M. Nicolescu, A. Skurikhin

{"title":"Investigating how and when perceptual organization cues improve boundary detection in natural images","authors":"Leandro A. Loss, G. Bebis, M. Nicolescu, A. Skurikhin","doi":"10.1109/CVPRW.2008.4562974","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4562974","url":null,"abstract":"Boundary detection in natural images represents an important but also challenging problem in computer vision. Motivated by studies in psychophysics claiming that humans use multiple cues for segmentation, several promising methods have been proposed which perform boundary detection by optimally combining local image measurements such as color, texture, and brightness. Very interesting results have been reported by applying these methods on challenging datasets such as the Berkeley segmentation benchmark. Although combining different cues for boundary detection has been shown to outperform methods using a single cue, results can be further improved by integrating perceptual organization cues with the boundary detection process. The main goal of this study is to investigate how and when perceptual organization cues improve boundary detection in natural images. In this context, we investigate the idea of integrating with segmentation the iterative multi-scale tensor voting (IMSTV), a variant of tensor voting (TV) that performs perceptual grouping by analyzing information at multiple-scales and removing background clutter in an iterative fashion, preserving salient, organized structures. The key idea is to use IMSTV to post-process the boundary posterior probability (PB) map produced by segmentation algorithms. Detailed analysis of our experimental results reveals how and when perceptual organization cues are likely to improve or degrade boundary detection. In particular, we show that using perceptual grouping as a post-processing step improves boundary detection in 84% of the grayscale test images in the Berkeley segmentation dataset.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"54 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470794","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

Camera localization and building reconstruction from single monocular images 单目图像的摄像机定位与建筑物重建

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563132

Ruisheng Wang, F. Ferrie

引用次数: 6

Binocular dance pose recognition and body orientation estimation via multilinear analysis 基于多线性分析的双目舞蹈姿态识别与身体方位估计

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4562970

Bo Peng, G. Qian

引用次数: 23

Improving the selection and detection of visual landmarks through object tracking 通过目标跟踪改进视觉地标的选择和检测

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops Pub Date : 2008-06-23 DOI: 10.1109/CVPRW.2008.4563133

P. Espinace, A. Soto

{"title":"Improving the selection and detection of visual landmarks through object tracking","authors":"P. Espinace, A. Soto","doi":"10.1109/CVPRW.2008.4563133","DOIUrl":"https://doi.org/10.1109/CVPRW.2008.4563133","url":null,"abstract":"The unsupervised selection and posterior recognition of visual landmarks is a highly valuable perceptual capability for a mobile robot. Recently, we proposed a system that aims to achieve this capability by combining a bottom-up data driven approach with top-down feedback provided by high level semantic representations. The bottom-up approach is based on three main mechanisms: visual attention, area segmentation, and landmark characterization. The top-down feedback is based on two information sources: i) An estimation of the robot position that reduces the searching scope for potential matches with previously selected landmarks, ii) A set of weights that, according to the results of previous recognitions, controls the influence of different segmentation algorithms in the recognition of each landmark. In this paper we explore the benefits of extending our previous work by including a visual tracking step for each of the selected landmarks. Our intuition is that the inclusion of a tracking step can help to improve the model of each landmark by associating and selecting information from its most significant views. Furthermore, it can also help to avoid problems related to the selection of spurious landmarks. Our results confirm these intuitions by showing that the inclusion of the tracking step produces a significant increase in the recall rate for landmark recognition.","PeriodicalId":102206,"journal":{"name":"2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops","volume":"11 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120807545","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