Fourth Canadian Conference on Computer and Robot Vision (CRV '07)最新文献_第2页

Corridor Navigation and Obstacle Avoidance using Visual Potential for Mobile Robot 基于视觉势的移动机器人走廊导航与避障

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.21

N. Ohnishi, A. Imiya

引用次数: 13

Dense Stereo Range Sensing with Marching Pseudo-Random Patterns 基于行进伪随机模式的密集立体距离传感

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.22

D. Desjardins, P. Payeur

引用次数: 36

Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images 超声图像中多囊卵巢形态的计算机辅助检测

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.18

Maryruth J. Lawrence, M. Eramian, R. Pierson, E. Neufeld

{"title":"Computer Assisted Detection of Polycystic Ovary Morphology in Ultrasound Images","authors":"Maryruth J. Lawrence, M. Eramian, R. Pierson, E. Neufeld","doi":"10.1109/CRV.2007.18","DOIUrl":"https://doi.org/10.1109/CRV.2007.18","url":null,"abstract":"Polycystic ovary syndrome (PCOS) is an endocrine abnormality with multiple diagnostic criteria due to its heterogenic manifestations. One of the diagnostic criteria includes analysis of ultrasound images of ovaries for the detection of number, size, and distribution of follicles within the ovary. This involves manual tracing and counting of follicles on the ultrasound images to determine the presence of a polycystic ovary (PCO). We describe a novel method that automates PCO detection. Our algorithm involves segmentation of follicles from ultrasound images, quantifying the attributes of the automatically segmented follicles using stereology, storing follicle attributes as feature vectors, and finally classification of the feature vector into two categories. The classification categories are: PCO present and PCO absent. An automatic PCO diagnostic tool would save considerable time spent on manual tracing of follicles and measuring the length and width of every follicle. Our procedure was able to achieve classification accuracy of 92.86% using a linear discriminant classifier. Our classifier will improve the rapidity and accuracy of PCOS diagnosis, reducing the risk of the severe complications that can arise from delayed diagnosis.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126774305","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: 38

Figure-ground segmentation using a hierarchical conditional random field 使用分层条件随机场的图像-地面分割

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.32

Jordan Reynolds, Kevin P. Murphy

引用次数: 62

Can Lucas-Kanade be used to estimate motion parallax in 3D cluttered scenes? 卢卡斯-卡纳德能否用于估计3D混乱场景中的运动视差?

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.15

V. Chapdelaine-Couture, M. Langer

{"title":"Can Lucas-Kanade be used to estimate motion parallax in 3D cluttered scenes?","authors":"V. Chapdelaine-Couture, M. Langer","doi":"10.1109/CRV.2007.15","DOIUrl":"https://doi.org/10.1109/CRV.2007.15","url":null,"abstract":"When an observer moves in a 3D static scene, the motion field depends on the depth of the visible objects and on the observer's instantaneous translation and rotation. By computing the difference between nearby motion field vectors, the observer can estimate the direction of local motion parallax and in turn the direction of heading. It has recently been argued that, in 3D cluttered scenes such as a forest, computing local image motion using classical optical flow methods is problematic since these classical methods have problems at depth discontinuities. Hence, estimating local motion parallax from optical flow should be problematic as well. In this paper we evaluate this claim. We use the classical Lucas-Kanade method to estimate optical flow and the Rieger-Lawton method to estimate the direction of motion parallax from the estimated flow. We compare the motion parallax estimates to those of the frequency based method of Mann-Langer. We find that if the Lucas-Kanade estimates are sufficiently pruned, using both an eigenvalue condition and a mean absolute error condition, then the Lucas- Kanade/Rieger-Lawton method can perform as well as or better than the frequency-based method.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114936017","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

Terrain Modelling for Planetary Exploration 行星探测的地形建模

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.63

Ioannis M. Rekleitis, Jean-Luc Bedwani, S. Gemme, T. Lamarche, E. Dupuis

引用次数: 15

Local Graph Matching for Object Category Recognition 局部图匹配的目标类别识别

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.44

E. F. Ersi, J. Zelek

{"title":"Local Graph Matching for Object Category Recognition","authors":"E. F. Ersi, J. Zelek","doi":"10.1109/CRV.2007.44","DOIUrl":"https://doi.org/10.1109/CRV.2007.44","url":null,"abstract":"A novel model for object category recognition in real-world scenes is proposed. Images in our model are represented by a set of triangular labelled graphs, each containing information on the appearance and geometry of a 3-tuple of distinctive image regions. In the learning stage, our model automatically learns a set of codebooks of model graphs for each object category, where each codebook contains information about which local structures may appear on which parts of the object instances of the target category. A two-stage method for optimal matching is developed, where in the first stage a Bayesian classifier based on ICA factorization is used efficiently to select the matched codebook, and in the second stage a nearest neighbourhood classifier is used to assign the test graph to one of the learned model graphs of the selected codebook. Each matched test graph casts votes for possible identity and poses of an object instance, and then a Hough transformation technique is used in the pose space to identify and localize the object instances. An extensive evaluation on several large datasets validates the robustness of our proposed model in object category recognition and localization in the presence of scale and rotation changes.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117308905","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: 4

3D Tree-Structured Object Tracking for Autonomous Ground Vehicles 自主地面车辆的三维树结构目标跟踪

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.1

Changsoo Jeong, A. C. Parker

引用次数: 2

Efficient camera motion and 3D recovery using an inertial sensor 有效的相机运动和3D恢复使用惯性传感器

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.23

M. Labrie, P. Hébert

引用次数: 17

Energy Efficient Robot Rendezvous 节能机器人交会

Fourth Canadian Conference on Computer and Robot Vision (CRV '07) Pub Date : 2007-05-28 DOI: 10.1109/CRV.2007.27

Pawel Zebrowski, Y. Litus, R. Vaughan

引用次数: 31