1,721,255 research outputs found
The Web-OEM approach to Web information extraction
Full text linkThe enormous amount of information available through the World Wide Web requires the development of effective tools for extracting and summarizing relevant data from Web sources. In this article we present a data model for representing Web documents and an associated SQL-like query language. Our framework provides an easy-to-use and well-formalized method for automatic generation of wrappers extracting data from Web documents. (C) 1999 Academic Press
Robust color segmentation through adaptive color distribution transformation
No full textColor segmentation is typically the first step of vision processing for a robot operating in a color-coded environment, such as RoboCup soccer, and many object recognition modules rely on that. Although many approaches to color segmentation have been proposed, in the official games of the RoboCup Four Legged League manual calibration is still preferred by most of the teams. In this paper we present a method for color segmentation that is based on an adaptive transformation of the color distribution of the image: the transformation is dynamically computed depending on the current image (i.e., it adapts to condition changes) and then it is used for color segmentation with static thresholds. The method requires the setting of only a few parameters and has been proved to be very robust to noise and light variations, allowing for setting parameters only once when arriving at a competition site. The approach has been implemented on AIBO robots, extensively tested in our laboratory, and successfully experimented in the some of the games of the Four Legged League in RoboCup 2005. © Springer-Verlag Berlin Heidelberg 2007
Human Posture Tracking and Classification through Stereo Vision and 3D Model Matching
Full text linkAbstract The ability of detecting human postures is particularly important in several fields like ambient intelligence, surveillance, elderly care, and human-machine interaction. This problem has been studied in recent years in the computer vision community, but the proposed solutions still suffer from some limitations due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.). In this article, we present a system for posture tracking and classification based on a stereo vision sensor. The system provides both a robust way to segment and track people in the scene and 3D information about tracked people. The proposed method is based on matching 3D data with a 3D human body model. Relevant points in the model are then tracked over time with temporal filters and a classification method based on hidden Markov models is used to recognize principal postures. Experimental results show the effectiveness of the system in determining human postures with different orientations of the people with respect to the stereo sensor, in presence of partial occlusions and under different environmental conditions.</p
Hough Localization for mobile robots in polygonal environments
No full textKnowing the position and orientation of a mobile robot situated in an environment is a critical element for effectively accomplishing complex tasks requiring autonomous navigation, and many techniques for robot self-localization have been extensively studied in the past. In this paper, we present a self-localization method that is based on the Hough transform for matching a geometric reference map with a representation of range information acquired by the robot's sensors. The technique is adequate for indoor office-like environments, especially for those environments that can be suitably represented by a set of segments. Many experiments are described to evaluate the effectiveness of the proposed method. Moreover, we have successfully tested this method in some dynamic environments populated with unknown and moving obstacles (e.g. persons or other robots moving around): office environments as well as the RoboCup environment. (C) 2002 Elsevier Science B.V All rights reserved
Human posture tracking and classification through stereo vision
No full textThe ability of detecting human postures is very relevant for applications related to the analysis of human behaviors. Techniques for posture detection and classification can be thus very important in several fields, like ambient intelligence, surveillance, elderly care, etc. This problem has been studied in recent years in the Computer Vision community, but proposed solutions still suffer from some limitations that are due to the difficulty of dealing with complex scenes (e.g., occlusions, different view points, etc.). In this paper we present a system for posture tracking and classification that uses a stereo vision sensor, which provides both for a robust way to segment and track people in the scene and 3D information about tracked people. The presented method uses a 3D model of human body, performs model matching through a variant of the ICP algorithm and then uses a Hidden Markov Model to model posture transitions. Experimental results show the effectiveness of the system in determining human postures in presence of partial occlusions and from different view points
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