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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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A framework for motion recognition with applications to American sign language and gait recognition

A framework for motion recognition with applications to Amer...

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Workshop on Human Motion

作者： C. Vogler H. Sun D. Metaxas Vision Analysis and Simulation Technologies Laboratory Department of Computer and Information Science University of Pennsylvania Philadelphia PA USA

Human motion recognition has many important applications, such as improved human-computer interaction and surveillance. A big problem that plagues this research area is that human movements can be very complex. Managing this complexity is difficult. We turn to American sign language (ASL) recognition to identify general methods that reduce the complexity of human motion recognition. We present a framework for continuous 3D ASL recognition based on linguistic principles, especially the phonology of ASL. This framework is based on parallel hidden Markov models (HMMs), which are able to capture both the sequential and the simultaneous aspects of the language. Each HMM is based on a single phoneme of ASL. Because the phonemes are limited in number, as opposed to the virtually unlimited number of signs that can be composed from them, we expect this framework to scale well to larger applications. We then demonstrate the general applicability of this framework to other human motion recognition tasks by extending it to gait recognition.

关键词： Handicapped aids Hidden Markov models Humans Application software computer vision Sun information analysis Computational modeling Analytical models computer simulation

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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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Factorization method for multiple perspective views via iterative depth estimation

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Systems and computers in Japan 2000年第13期31卷 87-95页

作者： Ueshiba, Toshio Tomita, Fumiaki Electrotechnical Laboratory Tsukuba 305-8568 Japan Department of Electrical Engineering University of Tokyo Electrotechnical Laboratory Min. of Intl. Trade and Industry Intelligent Systems Division Canada Nat. Inf. Eng. Lab. Information Processing Society Robotics Society of Japan Soc. of Instrum. Control Engineers Department of Control Engineering Osaka University CMU Department of Computer Science Information Processing Society Robotics Society of Japan

This paper proposes a factorization method that reconstructs camera motion and scene shape based on the matching of multiple images under the condition that the camera captures a perspective view. Starting from the affine projection camera model, the projection depth is iteratively estimated until the measurement matrix has rank 4. Then, the obtained measurement matrix is factorized to restore the three-dimensional information of the scene in the projection space. This approach eliminates noise sensitive processes, such as the calculation of the fundamental matrix, that are required in the factorization for the conventional perspective projection image, and a stable reconstruction is realized. Furthermore, the metric constraint in the conventional affine model is extended, and the metric constraint in the perspective projection condition is derived. It is shown that the reconstruction in Euclidean space is realized if the internal parameters of the camera are given.

关键词： Image reconstruction

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Face model adaptation with active tracking

Face model adaptation with active tracking

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IEEE International Conference on Image Processing

作者： Lijun Yin A. Basu Computer Vision and Robotics Research Laboratory Department of Computing Science University of Alberta Edmonton AB Canada

A system for face model adaptation combining active tracking is presented. Input from an active camera is used for MPEG4 model based coding. First, the background is compensated considering a moving camera (tilt or pan). Second, the talking face is segmented from the compensated background using fusion of frame differences. A morphological filter is then applied to make the system less sensitive to noise. Third, Hough transform and deformable template coupled with color information are exploited to detect the facial features, e.g., eyes, mouth. Fourth, a wireframe model is adapted to the extracted face by an extended dynamic mesh. The feasibility of the proposed system is demonstrated using several real active video sequences.

关键词： Adaptation model Face detection Cameras MPEG 4 Standard Filters Colored noise Facial features Eyes Mouth Data mining

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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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Coupled lighting direction and shape estimation from single images

Coupled lighting direction and shape estimation from single ...

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International Conference on computer vision (ICCV)

作者： D. Samaras D. Metaxas Vision Analysis and Simulation Technologies Laboratory Department of Computer & Information Science University of Pennsylvania Philadelphia PA USA

This paper presents a new method for the simultaneous estimation of lighting direction and shape from shading. The method estimates the shape and the lighting direction using a two step iterative process. We assume an initial (possibly incorrect) estimate of the lighting position. A stiff deformable model is then fitted to the image, assuming this lighting position. Next, a least-squares estimate of the lighting position is derived from the model using the Levenberg-Marquart method. The two steps-model fitting and lighting-position estimation-are iterated. Once the light direction has converged to a stable solution the deformable model stiffness is lowered and the model fits accurately given the lighting model. In addition, we show how the method can be used with either orthographic or perspective projection assumptions. In a variety of experiments on real and synthetic data, the method is robust to errors both to the initial light position and shape estimates.

关键词： Optical coupling Shape Deformable models Light sources Fitting information analysis Computational modeling Analytical models computer simulation Laboratories

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