The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)最新文献_第6页

Segment-based hand pose estimation 基于片段的手部姿态估计

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.72

Christopher G. Schwarz, N. Lobo

引用次数: 15

Minimum Bayes error features for visual recognition by sequential feature selection and extraction 最小贝叶斯误差特征的序列特征选择和提取视觉识别

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.53

G. Carneiro, N. Vasconcelos

{"title":"Minimum Bayes error features for visual recognition by sequential feature selection and extraction","authors":"G. Carneiro, N. Vasconcelos","doi":"10.1109/CRV.2005.53","DOIUrl":"https://doi.org/10.1109/CRV.2005.53","url":null,"abstract":"The extraction of optimal features, in a classification sense, is still quite challenging in the context of large-scale classification problems (such as visual recognition), involving a large number of classes and significant amounts of training data per class. We present an optimal, in the minimum Bayes error sense, algorithm for feature design that combines the most appealing properties of the two strategies that are currently dominant: feature extraction (FE) and feature selection (FS). The new algorithm proceeds by interleaving pairs of FS and FE steps, which amount to a sequential search for the most discriminant directions in a collection of two dimensional subspaces. It combines the fast convergence rate of FS with the ability of FE to uncover optimal features that are not part of the original basis functions, leading to solutions that are better than those achievable by either FE or FS alone, in a small number of iterations. Because the basic iteration has very low complexity, the new algorithm is scalable in the number of classes of the recognition problem, a property that is currently only available for feature extraction methods that are either sub-optimal or optimal under restrictive assumptions that do not hold for generic recognition. Experimental results show significant improvements over these methods, either through much greater robustness to local minima or by achieving significantly faster convergence.","PeriodicalId":307318,"journal":{"name":"The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114083359","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: 18

On processing and registration of forward-scan acoustic video imagery 前向扫描声学视频图像的处理与配准

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.57

S. Negahdaripour, P. Firoozfam, P. Sabzmeydani

引用次数: 55

Combining local and global features for image segmentation using iterative classification and region merging 结合局部和全局特征，采用迭代分类和区域合并的方法进行图像分割

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.27

Qiyao Yu, David A Clausi

引用次数: 10

Body tracking in human walk from monocular video sequences 基于单目视频序列的人体行走跟踪

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.24

F. Jean, R. Bergevin, A. Albu

引用次数: 30

Automated behavioral phenotype detection and analysis using color-based motion tracking 使用基于颜色的运动跟踪自动行为表型检测和分析

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.20

A. Shimoide, Ilmi Yoon, M. Fuse, Holly C. Beale, Rahul Singh

{"title":"Automated behavioral phenotype detection and analysis using color-based motion tracking","authors":"A. Shimoide, Ilmi Yoon, M. Fuse, Holly C. Beale, Rahul Singh","doi":"10.1109/CRV.2005.20","DOIUrl":"https://doi.org/10.1109/CRV.2005.20","url":null,"abstract":"The problem of elucidating the functional significance of genes is a key challenge of modern science. Solving this problem can lead to fundamental advancements across multiple areas such starting from pharmaceutical drug discovery to agricultural sciences. A commonly used approach in this context involves studying genetic influence on model organisms. These influences can be expressed at behavioral, morphological, anatomical, or molecular levels and the expressed patterns are called phenotypes. Unfortunately, detailed studies of many phenotypes, such as the behavior of an organism, is highly complicated due to the inherent complexity of the phenotype pattern and because of the fact that it may evolve over long time periods. In this paper, we propose applying color-based tracking to study Ecdysis in the hornworm - a biologically highly relevant phenotype whose complexity had thus far, prevented application of automated approaches. We present experimental results which demonstrate the accuracy of tracking and phenotype determination under conditions of complex body movement, partial occlusions, and body deformations. A key additional goal of our paper is to expose the computer vision community to such novel applications, where techniques from vision and pattern analysis can have a seminal influence on other branches of modern science.","PeriodicalId":307318,"journal":{"name":"The 2nd Canadian Conference on Computer and Robot Vision (CRV'05)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114690940","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

Segmentation of laparoscopic images: integrating graph-based segmentation and multistage region merging 腹腔镜图像分割:结合基于图的分割和多阶段区域合并

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.74

Yueyun Shu, Guillaume-Alexandre Bilodeau, F. Cheriet

引用次数: 17

Recognition of partially occluded objects using perfect hashing: an efficient and robust approach 使用完美哈希的部分遮挡物体识别:一种高效且稳健的方法

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.66

R. Dinesh, D. S. Guru

引用次数: 4

An experimental comparison of a hierarchical range image segmentation algorithm 一种分层距离图像分割算法的实验比较

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.15

G. Osorio, P. Boulanger, F. Prieto

引用次数: 6

Kinematic variables estimation using eye-in-hand robot camera system 眼手机器人摄像系统的运动变量估计

The 2nd Canadian Conference on Computer and Robot Vision (CRV'05) Pub Date : 2005-05-09 DOI: 10.1109/CRV.2005.51

Siddharth Verma, I. Sharf, G. Dudek

引用次数: 0