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International Conference on Digital Signal Processing (DSP)

作者： H. Veeraraghavan O. Masoud N. Papanikolopoulos Artificial Intelligence Vision and Robotics Lab Department of Computer Science and Engineering University of Minnesota USA

The goal of this project is to develop a passive vision-based sensing system capable of monitoring an intersection by observing the vehicle and pedestrian flow, and predicting situations that might give rise to accidents. A single camera mounted at an arbitrary position looking at an intersection is used. However, for extended applications multiple cameras will be needed. Some of the key elements are camera calibration, motion tracking, vehicle classification, and predicting collisions. In this paper, we focus on motion tracking. Motion segmentation is performed using an adaptive background model that models each pixel as a mixture of Gaussians. The method used is similar to the Stauffer method for motion segmentation. Tracking of objects is performed by computing the overlap between oriented bounding boxes. The oriented boxes are computed by vector quantization of blobs in the scene. The principal angles computed during vector quantization along with other cues of the object are used for classification of detected entities into vehicles and pedestrians.

关键词： Cameras Tracking Motion segmentation computer vision Vector quantization Vehicle detection Monitoring Accidents Calibration Gaussian processes

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Contour migration: solving object ambiguity with shape-space visual guidance

Contour migration: solving object ambiguity with shape-space...

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IEEE International Workshop on Intelligent Robots and Systems (IROS)

作者： W. Abd-Almageed C.E. Smith Department of Electrical and Computer Engineering Robotics Artificial Intelligence and Vision Laboratory University of New Mexico Albuquerque NM USA

A fundamental problem in computer vision is the issue of shape ambiguity. Simply stated, a silhouette cannot uniquely identify an object or an object's classification since many unique objects can present identical occluding contours. This problem has no solution in the general case for a monocular vision system. This paper presents a method for disambiguating objects during silhouette matching using a visual servoing system. This method identifies the camera motion(s) that gives disambiguating views of the objects. These motions are identified through a new technique called contour migration. The occluding contour's shape is used to identify objects or object classes that are potential matches for that shape. A contour migration is then determined that disambiguates the possible matches by purposive viewpoint adjustment. The technique is demonstrated using an example set of objects.

关键词： Shape Cameras computer vision Object recognition Machine vision Databases Intelligent robots Robot vision systems artificial intelligence Laboratories

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Mixture models for dynamic statistical pressure snakes

Mixture models for dynamic statistical pressure snakes

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International Conference on Pattern Recognition

作者： W. Abd-Almageed C.E. Smith Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA

This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models.

关键词： Image edge detection computer vision Equations Robot vision systems Robustness Bayesian methods Active contours Gaussian distribution Intelligent robots artificial intelligence

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Complementary linear biases in spatial derivative estimation for improving geometry-driven diffusion processes

Complementary linear biases in spatial derivative estimation...

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15th International Conference on Pattern Recognition, ICPR 2000

作者： Toczyski, William D. Papanikolopoulos, Nikolaos P. Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota 4-192 EE/CS Bldg. 200 Union Street SE MinneapolisMN55455 United States

ISBN: (纸本)0769507506

This paper introduces a broadly applicable technique for visibly improving the digitized, grey-level outputs produced by a host of iterative geometric diffusion methods. By replacing standard, central-difference estimates of discrete spatial gradients with alternating image derivative estimates that are offset by known, complementary biases, errors accumulated during iteration are reduced and the quality of geometric diffusions is improved. This unexpected synergy occurs at no added computational cost over central difference methods. Very simple to implement, the innovation is introduced at the level of spatial derivatives;hence, for a given process, any derived higher level mathematical properties - for example, group invariance or scale-space properties - can be preserved. © 2000 IEEE.

关键词： Iterative methods

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Counting bicycles using computer vision

Counting bicycles using computer vision

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International Conference on Intelligent Transportation

作者： S. Rogers N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA

This paper describes a system for monitoring bicycle activity in sequences of gray scale images acquired by a stationary camera. The system is suitable for use in applications that aim to increase the efficiency and safety of existing traffic systems. One such application is to determine usage and congestion of bicycle path. The output of the system is a count of the number of bicycles detected in the image sequence. The system is model-based in the sense that it uses a simple model of two circular objects separated by a relatively known distance. Our system uses four levels of abstraction: raw images, blobs, edge images, and the bicycle model. The system was implemented on a dual Pentium computer equipped with a Matrox imaging board and achieved a peak performance of 8 frames per second. Experimental results based on outdoor scenes have shown promising results for a variety of weather conditions.

关键词： Bicycles computer vision Application software computerized monitoring Cameras Safety Traffic control Image sequences Image edge detection Layout

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Generating polyhedral convex cones from contact graphs for the identification of assembly process states

Generating polyhedral convex cones from contact graphs for t...

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IEEE International Conference on robotics and Automation (ICRA)

作者： H. Mosemann T. Bierwirth F. Wahl S.A. Stoeter Institute for Robotics and Process Control Technical University of Braunschweig Brunswick Germany Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis USA

A new approach to automatically generate polyhedral convex cones from contact graphs for identification of assembly process states is presented. The state of contact between subassemblies is changing as operation proceeds in manipulative tasks such as part-mating. Measured contact forces and the resulting torques provide information about the contact geometry which is used to guide the assembly operation. In this paper, we present a method to calculate contact graphs for polyhedral subassemblies. The nodes of the contact graph represent contact formations of the subassemblies and the arcs represent neighborhoods of contact formations. This information is used to generate polyhedral convex cones for each contact formation considering friction. These polyhedral convex cones describe static equilibrium for each contact formation and are used to identify the contact state by measuring forces and torques during a part-mating.

关键词： Robotic assembly Force measurement Torque measurement Assembly systems Intelligent robots Friction Robot sensing systems Robot vision systems robotics and automation Uncertainty

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Complementary linear biases in spatial derivative estimation for improving geometry-driven diffusion processes

Complementary linear biases in spatial derivative estimation...

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International Conference on Pattern Recognition

作者： W.D. Toczyski N.P. Papnikolopoulos Artificial Intelligence Robotics vision laboratory Department of computer science and Engineering University of Minnesota Minneapolis MN USA University of Minnesota Minneapolis MN US

ISBN: (纸本)0769507506

This paper introduces a broadly applicable technique for visibly improving the digitized, grey-level outputs produced by a host of iterative geometric diffusion methods. By replacing standard, central-difference estimates of discrete spatial gradients with alternating image derivative estimates that are offset by known, complementary biases, errors accumulated during iteration are reduced and the quality of geometric diffusions is improved. This unexpected synergy occurs at no added computational cost over central difference methods. Very simple to implement, the innovation is introduced at the level of spatial derivatives; hence, for a given process, any derived higher level mathematical properties-for example, group invariance or scale-space properties-can be preserved.

关键词： Anisotropic magnetoresistance Equations Geometry Image motion analysis Optical filters Pixel Diffusion processes artificial intelligence Intelligent robots Robot vision systems

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Application of a genetic algorithm in triangulation of a 3-D object surface

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International Journal of computers and Applications 2000年第2期22卷 73-77页

作者： Zhenyu, Chen Mbede, J.B. Yan, Zhou Dehua, Li Hanping, Hu State Commn. Res. Open Lab. Image P. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Intelligent Contr. and Robotics Lab. Dept. of Contr. Sci. and Engineering Huazhong Univ. of Sci. and Technol. 430074 Wuhan China Second Artillery Institute Huazhong Univ. of Sci. and Technol. Inst. Pattern Recog. Artif. Intell. Huazhong Univ. of Sci. and Technol. Second Artillery Institute China Wuhan University China Artificial Intelligence Department University of Edinburgh Inst. of AI and Pattern Recognition Huazhong Univ. of Sci. and Technol. China Cogn. Sci. Natl. Key Found. Res. P. Douala University Cameroon Tübingen University Germany Automatic Control Laboratory Darmstadt University Department of Service Cameroon Min. of National Education Robotics Laboratory Huazhong Univ. of Sci. and Technol. Wuhan China Assoc. Connectionnistes These-ACTH Rennes France IEEE Soc. of Robotics and Automation IEEE Society of Control Systems IASTED Assoc. Jeunes Chercheurs Robotique France Navy University of Engineering China Huazhong Univ. of Sci. and Technol. China Department of Computer Science Airforce Radar Acad. of Engineering China Inst. of P.R and AI Huazhong Univ. of Sci. and Technol.

This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.

关键词： Object recognition

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Shape recognition and vision-based robot control by shape morphing

Shape recognition and vision-based robot control by shape mo...

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International Conference on Information intelligence and Systems

作者： R. Singh R.M. Voyles D. Littau N.P. Papanikolopoulos Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota Minneapolis MN USA

The article presents a unified approach for object recognition and recognition based control of robotic interactions with objects. The approach is based on morphing the shape of objects. The morph of one object to another is treated as a deformation and is defined such that it can be quantified using a physics based model. This quantification serves as a dissimilarity measure and is used for shape recognition. By storing the views that represent the grasp positions for different object, the proposed framework is used to identify the desired end position a robot needs to attain for interacting with the given object. Furthermore, the images synthesized during the morph define a view based trajectory, starting with the view defining the initial robot-object orientation to the view defining the desired robot-object orientation. We propose a technique where these synthetic images are used to control the motion of a PUMA 560 eye-in-hand manipulator to execute alignment and grasping tasks. The proposed approach does not require complete calibration information, obviates manual feature selection and correspondence, can provide smooth trajectories, and needs a single image for each object (in a 4-DOF formulation). Potential applications range from recognition and positioning with respect to partially occluded or deformable objects as well as planning robotic grasping based on human demonstration.

关键词： Shape control Robot control Shape measurement Object recognition Physics Deformable models Motion control Manipulators Calibration Humans

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Shape recognition and vision-based robot control by shape morphing

Shape recognition and vision-based robot control by shape mo...

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1999 International Conference on Information intelligence and Systems, ICIIS 1999

作者： Singh, Rahul Voyles, Richard M. Littau, David Papanikolopoulos, Nikolaos P. Artificial Intelligence Robotics and Vision Laboratory Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Bio-Informatics Division Exelixis Pharmaceuticals 280 E. Grand Avenue So. San FranciscoCA94080 United States

ISBN: (纸本)0769504469

In this paper we article presents a unified approach for object recognition and recognition based control of robotic interactions with objects. The approach is based on morphing the shape of objects. The morph of one object to another is treated as a deformation and is defined such that it can be quantified using a physics based model. This quantification serves as a dissimilarity measure and is used for shape recognition. By storing the views that represent the grasp positions for different object, the proposed framework is used to identify the desired end position a robot needs to attain for interacting with the given object. Furthermore, the images synthesized during the morph define a view based trajectory, starting with the view defining the initial robot-object orientation to the view defining the desired robot-object orientation. We propose a technique where these synthetic images are used to control the motion of a PUMA 560 eye-in-hand manipulator to execute alignment and grasping tasks. The proposed approach does not require complete calibration information, obviates manual feature selection and correspondence, can provide smooth trajectories, and needs a single image for each object (in a 4-DOF formulation). Potential applications range from recognition and positioning with respect to partially occluded or deformable objects as well as planning robotic grasping based on human demonstration. © 1999 IEEE.

关键词： Object recognition

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