2006 9th International Conference on Control, Automation, Robotics and Vision最新文献_第2页

Emotional Communication with the Robot Head MEXI 与机器人头的情感交流

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345162

Natascha Esau, L. Kleinjohann, B. Kleinjohann

引用次数: 11

Flexible Fuzzy Co-clustering with Feature-cluster Weighting 基于特征聚类加权的柔性模糊共聚类

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345069

William-Chandra Tjhi, Lihui Chen

{"title":"Flexible Fuzzy Co-clustering with Feature-cluster Weighting","authors":"William-Chandra Tjhi, Lihui Chen","doi":"10.1109/ICARCV.2006.345069","DOIUrl":"https://doi.org/10.1109/ICARCV.2006.345069","url":null,"abstract":"Fuzzy co-clustering is an unsupervised technique that performs simultaneous fuzzy clustering of objects and features. In this paper, we propose a new flexible fuzzy co-clustering algorithm which incorporates feature-cluster weighting in the formulation. We call it Flexible Fuzzy Co-clustering with Feature-cluster Weighting (FFCFW). By flexible we mean the algorithm allows the number of object clusters to be different from the number of feature clusters. There are two motivations behind this work. First, in the fuzzy framework, many co-clustering algorithms still require the number of object clusters to be the same as the number of feature clusters. This is despite the fact that such rigid structure is hardly found in real-world applications. The second motivation is that while there have been numerous attempts for flexible co-clustering, it is common that in such scheme the relationships between object and feature clusters are not clearly represented. For this reason we incorporate a feature-cluster weighting scheme for each object cluster generated by FFCFW so that the relationships between the two types of clusters are manifested in the feature-cluster weights. This enables the new algorithm to generate more accurate representation of fuzzy co-clusters. FFCFW is formulated by fusing together the core components of two existing algorithms. Like its predecessors, FFCFW adopts an iterative optimization procedure. We discuss in details the derivation of the proposed algorithm and the advantages it has over other existing works. Experiments on several large benchmark document datasets reveal the feasibility of our proposed algorithm","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127330566","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: 5

Communication Robots in the Network Robot Framework 网络机器人框架中的通信机器人

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345253

N. Hagita

引用次数: 6

Coordination Optimization-based Variable Structure Control for Main Steam Pressure of Power Plant 基于协调优化的电厂主蒸汽压力变结构控制

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345208

Yang Yong, Luo An

引用次数: 2

A Novel Conceptual Fish-like Robot Inspired by Rhinecanthus Aculeatus 一种新颖的概念鱼状机器人，灵感来自于无角犀

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345100

Tianjiang Hu, Guangming Wang, Lincheng Shen, Fei Li

引用次数: 7

Analysis of Vehicle Emissions and Prediction of Gross Emitter using Remote Sensing Data 基于遥感数据的汽车排放分析与总排放源预测

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345143

Jun Zeng, Huafang Guo, Yueming Hu, Tao Ye

引用次数: 4

An Interconnection between Combined Classical Block Diagrams and Linear Fractional Transformation Block Diagrams 组合经典方框图与线性分数变换方框图的相互联系

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345215

P. Houlis, V. Sreeram

引用次数: 1

Robot Space Exploration Using Peano Paths Generated by Self-Organizing Maps 基于自组织地图生成Peano路径的机器人空间探索

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345432

W. K. Lee, Wlodzislaw Duch, G. S. Ng

引用次数: 2

2D/3D Vision-Based Mango's Feature Extraction and Sorting 基于2D/3D视觉的芒果特征提取与分类

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345248

T. Chalidabhongse, Panitnat Yimyam, P. Sirisomboon

{"title":"2D/3D Vision-Based Mango's Feature Extraction and Sorting","authors":"T. Chalidabhongse, Panitnat Yimyam, P. Sirisomboon","doi":"10.1109/ICARCV.2006.345248","DOIUrl":"https://doi.org/10.1109/ICARCV.2006.345248","url":null,"abstract":"This paper describes a vision system that can extract 2D and 3D visual properties of mango such as size (length, width, and thickness), projected area, volume, and surface area from images and use them in sorting. The 2D/3D visual properties are extracted from multiple view images of mango. The images are first segmented to extract the silhouette regions of mango. The 2D visual properties are then measured from the top view silhouette as explained by Yimyam et al. (2005). The 3D mango volume reconstruction is done using volumetric caving on multiple silhouette images. First the cameras are calibrated to obtain the intrinsic and extrinsic camera parameters. Then the 3D volume voxels are crafted based on silhouette images of the fruit in multiple views. After craving all silhouettes, we obtain the coarse 3D shape of the fruit and then we can compute the volume and surface area. We then use these features in automatic mango sorting which we employ a typical backpropagation neural networks. In this research, we employed the system to evaluate visual properties of a mango cultivar called \"Nam Dokmai\". There were two sets total of 182 mangoes in three various sizes sorted by weights according to a standard sorting metric for mango export. Two experiments were performed. One is for showing the accuracy of our vision-based feature extraction and measurement by comparing results with the measurements using various instruments. The second experiment is to show the sorting accuracy by comparing to human sorting. The results show the technique could be a good alternative and more feasible method for sorting mango comparing to human's manual sorting.","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127037707","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: 53

Dynamic Vision in the Dynamic Scene: An Algebraic Approach 动态场景中的动态视觉:一种代数方法

2006 9th International Conference on Control, Automation, Robotics and Vision Pub Date : 2006-12-01 DOI: 10.1109/ICARCV.2006.345439

Jianchao Yao

{"title":"Dynamic Vision in the Dynamic Scene: An Algebraic Approach","authors":"Jianchao Yao","doi":"10.1109/ICARCV.2006.345439","DOIUrl":"https://doi.org/10.1109/ICARCV.2006.345439","url":null,"abstract":"In this paper, we address the issue of dynamically recovering 3D position vectors of a moving target based on its images in three views under the following scenario: (1) dynamic vision in dynamic scene, meaning that both camera and target are in motion; (2) The distance between target and camera is extremely large, so that we have only one image observation of the moving target for each view, the recovery of relative motion of target with respect to camera is impossible. By imposing the kinematical and dynamic constraints on the motion of target, its future position and velocity, given previous ones can be described by what we call f and g function. By combining with motion of the platform, the slant range for the middle time (or middle view) can be estimated by solving an eight-order polynomial. Since the three position vectors corresponding to the three image points on the three views can be assumed to lie in a single plane (approximate true if interval of the consecutive frames is small, and absolutely true if we consider the target to be a satellite moving on its orbit), the other two position vectors can be further derived. Initial experimental results demonstrated the correctness of the developed algorithm","PeriodicalId":415827,"journal":{"name":"2006 9th International Conference on Control, Automation, Robotics and Vision","volume":"161 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115037501","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