2014 Canadian Conference on Computer and Robot Vision最新文献_第3页

Exploring Underwater Environments with Curiosity 带着好奇心探索水下环境

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.22

Yogesh A. Girdhar, G. Dudek

引用次数: 18

A New Fitness Based Adaptive Parameter Particle Swarm Optimizer 一种新的基于适应度的自适应参数粒子群优化算法

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.52

S. Akhtar, E. Abdel-Rahman, Abdul-Rahim Ahmad

引用次数: 2

Stix-Fusion: A Probabilistic Stixel Integration Technique Stixel - fusion:一种概率Stixel集成技术

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.11

M. Muffert, Nicolai Schneider, U. Franke

{"title":"Stix-Fusion: A Probabilistic Stixel Integration Technique","authors":"M. Muffert, Nicolai Schneider, U. Franke","doi":"10.1109/CRV.2014.11","DOIUrl":"https://doi.org/10.1109/CRV.2014.11","url":null,"abstract":"In summer 2013, a Mercedes S-Class drove completely autonomously for about 100 km from Mannheim to Pforzheim, Germany, using only close-to-production sensors. In this project, called Mercedes Benz Intelligent Drive, stereo vision was one of the main sensing components. For the representation of free space and obstacles we relied on the so called Stixel World, a generic 3D intermediate representation which is computed from dense disparity images. In spite of the high performance of the Stixel World in most common traffic scenes, the availability of this technique is limited. For instance under adverse weather, rain or even spray water on the windshield results in erroneous disparity images which generate false Stixel results. This can lead to undesired behavior of autonomous vehicles. Our goal is to use the Stixel World for a robust free space estimation and a reliable obstacle detection even during difficult weather conditions. In this paper, we meet this challenge and fuse the Stixels incrementally into a reference grid map. Our new approach is formulated in a Bayesian manner and is based on existence estimation methods. We evaluate our new technique on a manually labeled database with emphasis on bad weather scenarios. The number of structures which are detected mistakenly within free space areas is reduced by a factor of two whereas the detection rate of obstacles increases at the same time.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128270411","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: 9

Robust Detection of Paint Defects in Moulded Plastic Parts 模塑件漆面缺陷的鲁棒检测

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.48

Cole Tarry, Michael Stachowsky, M. Moussa

引用次数: 7

Sign Language Fingerspelling Classification from Depth and Color Images Using a Deep Belief Network 基于深度信念网络的深度和颜色图像手语拼写分类

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.20

Lucas Rioux-Maldague, P. Giguère

引用次数: 45

Image Retrieval Using Landmark Indexing for Indoor Navigation 基于地标索引的室内导航图像检索

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.17

Dwaipayan Sinha, M. Ahmed, M. Greenspan

引用次数: 13

Multi-task Learning of Facial Landmarks and Expression 面部标志和表情的多任务学习

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.21

Terrance Devries, Kumar Biswaranjan, Graham W. Taylor

引用次数: 81

Direct Matrix Factorization and Alignment Refinement: Application to Defect Detection 直接矩阵分解和对齐精化:在缺陷检测中的应用

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.26

Zhen Qin, P. Beek, Xu Chen

{"title":"Direct Matrix Factorization and Alignment Refinement: Application to Defect Detection","authors":"Zhen Qin, P. Beek, Xu Chen","doi":"10.1109/CRV.2014.26","DOIUrl":"https://doi.org/10.1109/CRV.2014.26","url":null,"abstract":"Defect detection approaches based on template differencing require precise alignment of the input and template image, however, such alignment is easily affected by the presence of defects. Often, non-trivial pre/post-processing steps and/or manual parameter tuning are needed to remove false alarms, complicating the system and hampering automation. In this work, we explicitly address alignment and defect extraction jointly, and provide a general iterative algorithm to improve both their performance to pixel-wise accuracy. We achieve this by utilizing and extending the robust rank minimization and alignment method of [12]. We propose an effective and efficient optimization algorithm to decompose a template-guided image matrix into a low-rank part relating to alignment-refined defect-free images and an explicit error component containing the defects of interest. Our algorithm is fully automatic, training-free, only needs trivial pre/post-processing procedures, and has few parameters. The rank minimization formulation only requires a linearly correlated template image, and a template-guided approach relieves the common assumption of small defects, making our system very general. We demonstrate the performance of our novel approach qualitatively and quantitatively on a real-world data-set with defects of varying appearance.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114339417","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: 2

Generalized Exposure Fusion Weights Estimation 广义曝光融合权重估计

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.8

Mohammed Elamine Moumene, R. Nourine, D. Ziou

引用次数: 8

A More Robust Feature Correspondence for more Accurate Image Recognition 一种更鲁棒的特征对应关系，用于更准确的图像识别

2014 Canadian Conference on Computer and Robot Vision Pub Date : 2014-05-06 DOI: 10.1109/CRV.2014.32

Shady Y. El-Mashad, A. Shoukry

{"title":"A More Robust Feature Correspondence for more Accurate Image Recognition","authors":"Shady Y. El-Mashad, A. Shoukry","doi":"10.1109/CRV.2014.32","DOIUrl":"https://doi.org/10.1109/CRV.2014.32","url":null,"abstract":"In this paper, a novel algorithm for finding the optimal correspondence between two sets of image features has been introduced. The proposed algorithm pays attention not only to the similarity between features but also to the spatial layout of every matched feature and its neighbors. Unlike related methods that use geometrical relations between the neighboring features, the proposed method employees topology that survives against different types of deformations like scaling and rotation, resulting in more robust matching. The features are expressed as an undirected graph where every node represents a local feature and every edge represents adjacency between them. The topology of the resulting graph can be considered as a robust global feature of the represented object. The matching process is modeled as a graph matching problem, which in turn is formulated as a variation of the quadratic assignment problem. In this variation, a number of parameters are used to control the significance of global vs. local features to tune the performance and customize the model. The experimental results show a significant improvement in the number of correct matches using the proposed method compared to different methods.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128143857","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