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检索条件"任意字段=Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision"

共 753 条记录，以下是241-250 订阅

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Scene understanding and intelligent tactical behavior of mobile robots with perception system based on Network-Symbolic Models

Scene understanding and intelligent tactical behavior of mob...

引用

intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Kuvich, Gary Smart Computer Vision Systems

intelligent tactical behaviors of robots and UGVs cannot be achieved without a perception system that is similar to human vision. The traditional linear bottom-up "segmentation-grouping-learning-recognition" approach to image processing and analysis cannot provide a reliable separation of an object from its background or clutter, while human vision unambiguously solves this problem. The nature of informational processes in the visual system does not allow separating from the informational processes in the top-level knowledge system. An Image/Video Analysis that is based on Network-Symbolic approach is a combination of recursive hierarchical bottom-up and top-down processes. Instead of precise computations of 3-dimensional models a Network-Symbolic system converts image information into an "understandable" Network-Symbolic format that is similar to the relational knowledge models. The logic of visual scenes can be captured in the Network-Symbolic models and used for the reliable disambiguation of visual information, including object detection and identification. View-based object recognition is a hard problem for traditional algorithms that directly match a primary view of an object to a model. In Network-Symbolic Models, the derived structure and not the primary view is a subject for recognition. Such recognition is not affected by local changes and appearances of the object from a set of similar views, and a robot can better interpret images and video for intelligent tactical behavior.

关键词： Mobile robots

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Positioning approach of a spherical rolling robot

Positioning approach of a spherical rolling robot

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Wang, Liangqing Sun, Hanxu Jia, Qingxuan Xiao, Aiping Yu, Zhonghua Cao, Xiaowei Beijing University of Aeronautics and Astronautics Beijing 100083 China Beijing University of Posts and Telecommunications Beijing 100876 China

In comparison to other mobile robots, spherical rolling robots offer greater mobility, stability, and scope for operation in hazardous environments. Spherical rolling robots have been attracting much attention in not only mechanical but also control literature recently, due to both their relevance to practical applications, and to the difficulties in the analysis and control of these robots. The positioning of a spherical rolling robot at an arbitrary pose and at any time is one of the fundamental and difficult problems in the research of spherical rolling robots. Because spherical rolling robot touches the floor at a point, the positioning is difficult, especially when it moves at a high speed. Up to now, this problem has not been solved perfectly. In this paper, we present an efficient positioning approach for a spherical rolling robot. Based on the approach, a moving spherical rolling robot can be positioned at an arbitrary pose and at any time.

关键词： Mobile robots

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FPGA implementation of vision algorithms for small autonomous robots

FPGA implementation of vision algorithms for small autonomou...

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Anderson, J.D. Lee, D.J. Archibald, J.K. Brigham Young University Provo UT 84602 United States

The use of on-board vision with small autonomous robots has been made possible by the advances in the field of Field Programmable Gate Array (FPGA) technology. By connecting a CMOS camera to an FPGA board, on-board vision has been used to reduce the computation time inherent in vision algorithms. The FPGA board allows the user to create custom hardware in a faster, safer, and more easily verifiable manner that decreases the computation time and allows the vision to be done in real-time. Real-time vision tasks for small autonomous robots include object tracking, obstacle detection and avoidance, and path planning. Competitions were created to demonstrate that our algorithms work with our small autonomous vehicles in dealing with these problems. These competitions include Mouse-Trapped-in-a-Box, where the robot has to detect the edges of a box that it is trapped in and move towards them without touching them;Obstacle Avoidance, where an obstacle is placed at any arbitrary point in front of the robot and the robot has to navigate itself around the obstacle;Canyon Following, where the robot has to move to the center of a canyon and follow the canyon walls trying to stay in the center;the Grand Challenge, where the robot had to navigate a hallway and return to its original position in a given amount of time;and Stereo vision, where a separate robot had to catch tennis balls launched from an air powered cannon. Teams competed on each of these competitions that were designed for a graduate-level robotic vision class, and each team had to develop their own algorithm and hardware components. This paper discusses one team's approach to each of these problems.

关键词： computer vision

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Perception system with scene understanding capabilities upon Network-Symbolic models for intelligent tactical behavior of mobile robots in real-world environment

Perception system with scene understanding capabilities upon...

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Kuvich, Gary Smart Computer Vision Systems

Tactical behavior of UGVs, which is needed for successful autonomous off-road driving, can be in many cases achieved by covering most possible driving situations with a set of rules and switching into a "drive-me-away" semi-autonomous mode when no such rule exists. However, the unpredictable and rapidly changing nature of combat situations requires more intelligent tactical behavior that must be based on predictive situation awareness with ongoing scene understanding and fast autonomous decision making. The implementation of image understanding and active vision is possible in the form of biologically inspired Network-Symbolic models, which combine the power of Computational Intelligence with graph and diagrammatic representation of knowledge. A Network-Symbolic system converts image information into an "understandable" Network-Symbolic format, which is similar to relational knowledge models. The traditional linear bottom-up "segmentation-grouping-learning-recognition" approach cannot provide a reliable separation of an object from its background/clutter, while human vision unambiguously solves this problem. An Image/Video Analysis that is based on Network-Symbolic approach is a combination of recursive hierarchical bottom-up and top-down processes. Logic of visual scenes can be captured in the Network-Symbolic models and used for the reliable disambiguation of visual information, including object detection and identification. Such a system can better interpret images/video for situation awareness, target recognition, navigation and actions and seamlessly integrates into 4D/RCS architecture.

关键词： Mobile robots

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People detection in crowded scenes using active contour models and texture analysis

People detection in crowded scenes using active contour mode...

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Sidla, Oliver Andreu, Jean P. Paletta, Lucas Lypetskyy, Yuriy JOANNEUM RESEARCH Institute of Digital Image Processing Graz Austria

A vision system designed to detect people in complex backgrounds is presented. The purpose of the proposed algorithms is to allow the identification and tracking of single persons under difficult conditions - in crowded places, under partial occlusion and in low resolution images. In order to detect people reliably, we combine different information channels from video streams. Most emphasis for the initialization of trajectories and the subsequent pedestrian recognition is placed on the detection of the head-shoulder contour. In the first step a simple and fast shape model selects promising candidates, then a local active shape model is matched against the gradients found in the image with the help of a cost function. Texture analysis in the form of co-occurrence features ensures that shape candidates form coherent trajectories over time. In order to reduce the amount of false positives and to become more robust, a pattern analysis step based on Eigenimage analysis is presented. The cues which form the basis of pedestrian detection are integrated into a tracking algorithm which uses the shape information for initial pedestrian detection and verification, propagates positions into new frames using local motion and matches pedestrians with the help of texture information.

关键词： computer vision

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Retinex software or diffractive-optical correlator hardware: The basis of human color vision?

Retinex software or diffractive-optical correlator hardware:...

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Lauinger, N. CORRSYS-DATRON Sensorsysteme GmbH Wetzlar

Edwin Land - based on photometric data - tried to explain through a retinex (retina + cortex) model calculating scaled integrated reflectances, how human color vision determines the perceived hues of colored Mondrian patches by relating illuminants and 'energies at the eye' and including calculation over the whole image. An alternative - purely optical - model, the diffractive-optical correlator hardware in aperture and image space of the human eye, relating 'local' data onto 'global' data in color vision, becomes illustrated. Based on Edwin Land's experimental data it is shown how the perceived hues result from diffractive-optical transformations and cross-correlations between object space and reciprocal space (RGB space), from matrix multiplications and divisions in vector space. Optical pre-processing causes that our eyes do not see what (physically) is real, but what they optically have calculated. This same diffractive-optical mechanism has also lead to an explanation of the phenomenon of paradoxically colored shadows, shortly re-presented in the introduction.

关键词： computer software

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A simple approach to a vision-guided unmanned vehicle

A simple approach to a vision-guided unmanned vehicle

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Archibald, Christopher Millar, Evan Anderson, Jon D. Archibald, James K. Lee, Dah-Jye Department of Electrical and Computer Engineering Brigham Young University 459 CB Provo UT 84602

This paper describes the design and implementation of a vision-guided autonomous vehicle that represented BYU in the 2005 intelligent Ground Vehicle Competition (IGVC), in which autonomous vehicles navigate a course marked with white lines while avoiding obstacles consisting of orange construction barrels, white buckets and potholes. Our project began in the context of a senior capstone course in which multi-disciplinary teams of five students were responsible for the design, construction, and programming of their own robots. Each team received a computer motherboard, a camera, and a small budget for the purchase of additional hardware, including a chassis and motors. The resource constraints resulted in a simple vision-based design that processes the sequence of images from the single camera to determine motor controls. Color segmentation separates white and orange from each image, and then the segmented image is examined using a 10×10 grid system, effectively creating a low resolution picture for each of the two colors. Depending on its position, each filled grid square influences the selection of an appropriate turn magnitude. Motor commands determined from the white and orange images are then combined to yield the final motion command for video frame. We describe the complete algorithm and the robot hardware and we present results that show the overall effectiveness of our control approach.

关键词： Unmanned vehicles

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Intersection-point determination in Virtual Keeper using machine vision

Intersection-point determination in Virtual Keeper using mac...

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Koljonen, Janne Kanniainen, Olli Alander, Jarmo T. Dept. of Electrical Engineering and Industrial Management University of Vaasa Finland

Virtual Keeper is a goalkeeper simulator. It estimates the trajectory of a ball thrown by a player using machine vision and animates a goalkeeper with a video projector. The first version of Virtual Keeper used only one camera. In this paper, a new version that uses two gray-scale cameras for trajectory estimation is proposed. In addition, a color camera and a microphone are used to determine the intersection-point of the ball trajectory and the goal line, in order to enable feedback and online calibration of the machine vision with neural networks, which in turn allows varying external parameters of the cameras and the video projector. The color camera takes images of the goal and determines the positions of the goalposts with pattern matching. After the gray-scale cameras have observed the ball and estimated its trajectory, the sound processing block is triggered. When the ball hits the screen, the noise pattern is recognized with a neural network, whose input consists of temporal and spectral features. The sound processing block in turn triggers the color camera image processing block. The color of the ball differs from the colors of the background and goalkeeper to make the segmentation problem easier. The ball is recognized with fuzzy color based segmentation and fuzzy pattern matching.

关键词： Color image processing

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intelligent mobile robot system for path planning using stereo camera-based geometry information

Intelligent mobile robot system for path planning using ster...

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Ko, Jung-Hwan Hwang, Dong-Choon Jung, Yong-Woo Kim, Eun-Soo Dept. of Electronic Eng. 3D Display Research Center Kwangwoon University Seoul 139-701 Korea Republic of

In this paper, a new real-time and intelligent mobile robot system for path planning and navigation using stereo camera embedded on the pan/tilt system is proposed. In the proposed system, face area of a moving person is detected from a sequence of the stereo image pairs by using the YCbCr color model and using the disparity map obtained from the left and right images captured by the pan/tilt-controlled stereo camera system and depth information can be detected. And then, the distance between the mobile robot system and the face of the moving person can be calculated from the detected depth information. Accordingly, based-on the analysis of these data, three-dimensional objects can be detected. Finally, by using these detected data, 2-D spatial map for a visually guided robot that can plan paths, navigate surrounding objects and explore an indoor environment is constructed. From some experiments on target tracking with 480 frames of the sequential stereo images, it is analyzed that error ratio between the calculated and measured values of the relative position is found to be very low value of 1.4 % on average. Also, the proposed target tracking system has achieved a high speed of 0.04 sec/frame for target detection and 0.06 sec/frame for target tracking.

关键词： Mobile robots

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Foreground object segmentation from binocular stereo video

Foreground object segmentation from binocular stereo video

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intelligent robots and computer vision xxiii: algorithms, techniques, and active vision

作者： Law, Kevin Sclaroff, Stan Boston University Department of Computer Science 111 Cummington St. Boston United States

Moving cameras are needed for a wide range of applications in robotics, vehicle systems, surveillance, etc. However, many foreground object segmentation methods reported in the literature are unsuitable for such settings;these methods assume that the camera is fixed and the background changes slowly, and are inadequate for segmenting objects in video if there is significant motion of the camera or background. To address this shortcoming, a new method for segmenting foreground objects is proposed that utilizes binocular video. The method is demonstrated in the application of tracking and segmenting people in video who are approximately facing the binocular camera rig. Given a stereo image pair, the system first tries to find faces. Starting at each face, the region containing the person is grown by merging regions from an over-segmented color image. The disparity map is used to guide this merging process. The system has been implemented on a consumer-grade PC, and tested on video sequences of people indoors obtained from a moving camera rig. As can be expected, the proposed method works well in situations where other foreground-background segmentation methods typically fail. We believe that this superior performance is partly due to the use of object detection to guide region merging in disparity/color foreground segmentation, and partly due to the use of disparity information available with a binocular rig, in contrast with most previous methods that assumed monocular sequences.

关键词： Object recognition

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