This paper mainly studies a signal enhancement algorithm in target image acquisition system developed based on VC++6.0 under strong background light, which is used for target image acquisition under a simulated scene, it can accurately extract target object image and completely filter background light interference. Even though target area image is entirely concealed by background light, this algorithm can still extract it effectively. Using image multi-cycle integral can increase the signal strength of target area image and realize the clear extraction of target area image information under the condition of filtering background light interference. Using semiconductor lasers with different wavelengths to experiment can still obtain the same result.
{"title":"Research and Analyze about Signal Enhancement Algorithm in Image Recognition System","authors":"Han Cuiying, Kong Juan","doi":"10.1109/ICMV.2009.76","DOIUrl":"https://doi.org/10.1109/ICMV.2009.76","url":null,"abstract":"This paper mainly studies a signal enhancement algorithm in target image acquisition system developed based on VC++6.0 under strong background light, which is used for target image acquisition under a simulated scene, it can accurately extract target object image and completely filter background light interference. Even though target area image is entirely concealed by background light, this algorithm can still extract it effectively. Using image multi-cycle integral can increase the signal strength of target area image and realize the clear extraction of target area image information under the condition of filtering background light interference. Using semiconductor lasers with different wavelengths to experiment can still obtain the same result.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125468712","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}
Rapidly growing quantities of digital video have made video data become a more important role in many applications than ever. Virtual scene construction also uses video materials as a rich resource. In order to manage the video materials like salient objects and scenes, an effective video retrieval system is required. In this paper, we present a video database management system using a hierarchical video model consisting of Video-Scene-Shot-SalientObject. We first propose a hierarchical video data model which presents a hierarchical structuring of video material and a hierarchical annotation of video material structure. Then two effective indexing structures are proposed based on the model, including an indexing tree structure which considers the relationship between salient objects and a semantic indexing structure inspired by the inverted file indexing structure. Finally, an implementation of this model is described and the efficiency of this method is evaluated.
{"title":"Hierarchical Video Data Modeling and Indexing for Virtual Scene Construction","authors":"Huiyu Wang, Ruofeng Tong","doi":"10.1109/ICMV.2009.15","DOIUrl":"https://doi.org/10.1109/ICMV.2009.15","url":null,"abstract":"Rapidly growing quantities of digital video have made video data become a more important role in many applications than ever. Virtual scene construction also uses video materials as a rich resource. In order to manage the video materials like salient objects and scenes, an effective video retrieval system is required. In this paper, we present a video database management system using a hierarchical video model consisting of Video-Scene-Shot-SalientObject. We first propose a hierarchical video data model which presents a hierarchical structuring of video material and a hierarchical annotation of video material structure. Then two effective indexing structures are proposed based on the model, including an indexing tree structure which considers the relationship between salient objects and a semantic indexing structure inspired by the inverted file indexing structure. Finally, an implementation of this model is described and the efficiency of this method is evaluated.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125042073","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}
Visual navigation can handle complicated problems, such as kidnapping, shadowing and slipping. A low-cost video camera is particularly suitable for mobile home robots in the sense of human robot interaction, and it does not disparity map computation. An efficient vision-based simultaneous localization and map building (SLAM) method is presented for home robots using a forward monocular camera. This paper also presents a novel framework of scale-invariant feature transform (SIFT), where the difference of Gaussian (DOG)- based scale-invariant feature transform method is replaced by the difference of wavelet (DOW) transform. The modified SIFT enables real-time applications or embedded systems for home robot products. Two different types of home robots, such as cleaning and service robots serve as a tested platform of the proposed vision-based navigation. The experimental results show that the robots can provide acceptable navigation performance on unstructured environment in real-time.
{"title":"Mobile Robot Navigation Using Difference of Wavelet SIFT","authors":"Jung Ilkyun, Jun Sewoong, Kim Youngouk","doi":"10.1109/ICMV.2009.36","DOIUrl":"https://doi.org/10.1109/ICMV.2009.36","url":null,"abstract":"Visual navigation can handle complicated problems, such as kidnapping, shadowing and slipping. A low-cost video camera is particularly suitable for mobile home robots in the sense of human robot interaction, and it does not disparity map computation. An efficient vision-based simultaneous localization and map building (SLAM) method is presented for home robots using a forward monocular camera. This paper also presents a novel framework of scale-invariant feature transform (SIFT), where the difference of Gaussian (DOG)- based scale-invariant feature transform method is replaced by the difference of wavelet (DOW) transform. The modified SIFT enables real-time applications or embedded systems for home robot products. Two different types of home robots, such as cleaning and service robots serve as a tested platform of the proposed vision-based navigation. The experimental results show that the robots can provide acceptable navigation performance on unstructured environment in real-time.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"450 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121326645","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}
This paper proposes a new positioning method using an image sensor and visible light LEDs (Light Emitting Diode). The color LEDs are used to detect position. We achieved position accuracy of less than 5cm using our method. We applied our method to a robot and demonstrated that accurate position control of a robot was feasible.
{"title":"New Position Detection Method Using Image Sensor and Visible Light LEDs","authors":"Toshiya Tanaka, Shinichro Haruyama","doi":"10.1109/ICMV.2009.44","DOIUrl":"https://doi.org/10.1109/ICMV.2009.44","url":null,"abstract":"This paper proposes a new positioning method using an image sensor and visible light LEDs (Light Emitting Diode). The color LEDs are used to detect position. We achieved position accuracy of less than 5cm using our method. We applied our method to a robot and demonstrated that accurate position control of a robot was feasible.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699533","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}
Visual object tracking is an important topic in multimedia technologies. This paper presents robust implementation of an object tracker using a vision system that takes into consideration partial occlusions, rotation and scale for a variety of different objects. A scale invariant feature transform (SIFT) based color particle filter algorithm is proposed for object tracking in real scenarios. The Scale Invariant Feature Transform (SIFT) has become a popular feature extractor for vision based applications. It has been successfully applied for metric localization and mapping. Then the object is tracked by a color based particle filter. The color particle filter has proven to be an efficient, simple and robust tracking algorithm. Experimental results of applying this technique show improvement in tracking and robustness in recovering from partial occlusions, rotation and scale.
{"title":"Particle Filter Based Object Tracking with Sift and Color Feature","authors":"S. Fazli, H. M. Pour, H. Bouzari","doi":"10.1109/ICMV.2009.47","DOIUrl":"https://doi.org/10.1109/ICMV.2009.47","url":null,"abstract":"Visual object tracking is an important topic in multimedia technologies. This paper presents robust implementation of an object tracker using a vision system that takes into consideration partial occlusions, rotation and scale for a variety of different objects. A scale invariant feature transform (SIFT) based color particle filter algorithm is proposed for object tracking in real scenarios. The Scale Invariant Feature Transform (SIFT) has become a popular feature extractor for vision based applications. It has been successfully applied for metric localization and mapping. Then the object is tracked by a color based particle filter. The color particle filter has proven to be an efficient, simple and robust tracking algorithm. Experimental results of applying this technique show improvement in tracking and robustness in recovering from partial occlusions, rotation and scale.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132965955","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}
Many organizations collect large amounts of data to support their business and decision making processes. The data collected from various sources may have data quality problems in it. These kinds of issues become prominent when various databases are integrated. The integrated databases inherit the data quality problems that were present in the source database. The data in the integrated systems need to be cleaned for proper decision making. Cleansing of data is one of the most crucial steps. In this research, focus is on one of the major issue of data cleansing i.e. “duplicate record detection” which arises when the data is collected from various sources. As a result of this research study, comparison among standard duplicate elimination algorithm (SDE), sorted neighborhood algorithm (SNA), duplicate elimination sorted neighborhood algorithm (DE-SNA), and adaptive duplicate detection algorithm (ADD) is provided. A prototype is also developed which shows that adaptive duplicate detection algorithm is the optimal solution for the problem of duplicate record detection. For approximate matching of data records, string matching algorithms (recursive algorithm with word base and recursive algorithm with character base) have been implemented and it is concluded that the results are much better with recursive algorithm with word base.
{"title":"Duplicate Record Detection for Database Cleansing","authors":"M. Rehman, Vatcharapon Esichaikul","doi":"10.1109/ICMV.2009.43","DOIUrl":"https://doi.org/10.1109/ICMV.2009.43","url":null,"abstract":"Many organizations collect large amounts of data to support their business and decision making processes. The data collected from various sources may have data quality problems in it. These kinds of issues become prominent when various databases are integrated. The integrated databases inherit the data quality problems that were present in the source database. The data in the integrated systems need to be cleaned for proper decision making. Cleansing of data is one of the most crucial steps. In this research, focus is on one of the major issue of data cleansing i.e. “duplicate record detection” which arises when the data is collected from various sources. As a result of this research study, comparison among standard duplicate elimination algorithm (SDE), sorted neighborhood algorithm (SNA), duplicate elimination sorted neighborhood algorithm (DE-SNA), and adaptive duplicate detection algorithm (ADD) is provided. A prototype is also developed which shows that adaptive duplicate detection algorithm is the optimal solution for the problem of duplicate record detection. For approximate matching of data records, string matching algorithms (recursive algorithm with word base and recursive algorithm with character base) have been implemented and it is concluded that the results are much better with recursive algorithm with word base.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132082811","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}
Simulating the natural motion of cloth has attracted many researchers interest to create high quality of cloth simulation in realistic virtual environment applications such as games, animation, virtual reality and medical. Generally, to simulate realistic, interactive, stable, complex and handle the collision detection for cloth simulation is a very complex tasks. Self-collision detection is the most time-consuming part in cloth simulation. Since all particles are on the surface, all particles may potentially collide with each other. More analysis of the method to speed up the collision detection that must be resolved in order to come out with good collision detection algorithm has been done. This paper tries to provide the efficiency of bounding volume hierarchy techniques for building and traversing these hierarchies. The heuristics can be speed up the hierarchy update that allows pruning of the hierarchy and enables to reduce the number of triangles with a minimum computational cost.
{"title":"Hierarchy Techniques in Self-Collision Detection for Cloth Simulation","authors":"Nur Saadah Mohd Shapri, A. Bade, D. Daman","doi":"10.1109/ICMV.2009.23","DOIUrl":"https://doi.org/10.1109/ICMV.2009.23","url":null,"abstract":"Simulating the natural motion of cloth has attracted many researchers interest to create high quality of cloth simulation in realistic virtual environment applications such as games, animation, virtual reality and medical. Generally, to simulate realistic, interactive, stable, complex and handle the collision detection for cloth simulation is a very complex tasks. Self-collision detection is the most time-consuming part in cloth simulation. Since all particles are on the surface, all particles may potentially collide with each other. More analysis of the method to speed up the collision detection that must be resolved in order to come out with good collision detection algorithm has been done. This paper tries to provide the efficiency of bounding volume hierarchy techniques for building and traversing these hierarchies. The heuristics can be speed up the hierarchy update that allows pruning of the hierarchy and enables to reduce the number of triangles with a minimum computational cost.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128430840","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}
Clustering is an important research topic for mobile ad hoc networks (MANETs) because clustering makes it possible to guarantee basic levels of system performance, such as throughput and delay, in the presence of both mobility and a large number of mobile terminals. A large variety of approaches for ad hoc clustering have been presented, whereby different approaches typically focus on different performance metrics. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance; the clustering schemes for mobile ad hoc networks therefore aim at handling topology maintenance, managing node movement or reducing overhead. This paper presents the reasons for clustering algorithms in ad hoc networks, as well as a short survey of the basic ideas and priorities of existing clustering algorithms.
{"title":"Survey of Stable Clustering for Mobile Ad Hoc Networks","authors":"Abolfazl Akbari, Mahdi Soruri, Seyed Vahid Jalali","doi":"10.1109/ICMV.2009.12","DOIUrl":"https://doi.org/10.1109/ICMV.2009.12","url":null,"abstract":"Clustering is an important research topic for mobile ad hoc networks (MANETs) because clustering makes it possible to guarantee basic levels of system performance, such as throughput and delay, in the presence of both mobility and a large number of mobile terminals. A large variety of approaches for ad hoc clustering have been presented, whereby different approaches typically focus on different performance metrics. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance; the clustering schemes for mobile ad hoc networks therefore aim at handling topology maintenance, managing node movement or reducing overhead. This paper presents the reasons for clustering algorithms in ad hoc networks, as well as a short survey of the basic ideas and priorities of existing clustering algorithms.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116671221","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}
Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. This paper presents a robust method to detect positive obstacles including staircases in highly textured environments. The proposed method is easy to implement and fast enough for obstacle avoidance. This work is partly inspired by the work of Nicholas Molton et al [1]. The algorithm consists of several steps including calibration, pre processing, obstacle detection, analysis of disparity map and depth computation. This method works well in highly textured environments and ideal for real applications. An adaptive thresholding is also applied for better noise and texture removal. Experimental results show the effectiveness of the proposed method.
{"title":"A Robust Obstacle Detection Method in Highly Textured Environments Using Stereo Vision","authors":"S. Fazli, Hajar Mohammadi Dehnavi, P. Moallem","doi":"10.1109/ICMV.2009.48","DOIUrl":"https://doi.org/10.1109/ICMV.2009.48","url":null,"abstract":"Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. This paper presents a robust method to detect positive obstacles including staircases in highly textured environments. The proposed method is easy to implement and fast enough for obstacle avoidance. This work is partly inspired by the work of Nicholas Molton et al [1]. The algorithm consists of several steps including calibration, pre processing, obstacle detection, analysis of disparity map and depth computation. This method works well in highly textured environments and ideal for real applications. An adaptive thresholding is also applied for better noise and texture removal. Experimental results show the effectiveness of the proposed method.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123015866","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}
There are various approaches for web news extraction, including tree-edit distance approach that needs to assume the existence of web templates, visual wrapper based approach that requires large training sets and statistical approach whose flexibility is low. In this paper, a blocking tag based Web news extraction approach is proposed, which automatically detects the blocking tags that break the web page down into functional areas and then analyzes the web page according to the blocking tags to find out the news content. We have implemented the proposed news extraction approach in a news search engine which has been applied in business of an intelligence enterprise. Compared with related work, our approach does not require the web page templates or large training sets, and the complexity is lower.
{"title":"Automatic Web News Extraction Using Blocking Tag","authors":"Lin Ziyi, Shen Beijun, Tang Xinhuai, Chen Delai","doi":"10.1109/ICMV.2009.17","DOIUrl":"https://doi.org/10.1109/ICMV.2009.17","url":null,"abstract":"There are various approaches for web news extraction, including tree-edit distance approach that needs to assume the existence of web templates, visual wrapper based approach that requires large training sets and statistical approach whose flexibility is low. In this paper, a blocking tag based Web news extraction approach is proposed, which automatically detects the blocking tags that break the web page down into functional areas and then analyzes the web page according to the blocking tags to find out the news content. We have implemented the proposed news extraction approach in a news search engine which has been applied in business of an intelligence enterprise. Compared with related work, our approach does not require the web page templates or large training sets, and the complexity is lower.","PeriodicalId":315778,"journal":{"name":"2009 Second International Conference on Machine Vision","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010378","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}
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