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Visual Communications and Image Processing III 1988

作者： Malowany, M.E. Malowany, A.S. Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University MontréalQC Canada

This paper describes a method for computing Gaussian and mean curvature maps from range data and a modification to this method aimed at facilitating its implementation as a VLSI circuit. The curvature computations constitute a surface characterization step in range-data processing. A comparison of the performance of the original and modified algorithms on artificial range images is presented. Results of applying the modified algorithm to real range data is also presented. © 1988 SPIE. All rights reserved.

关键词： VLSI circuits

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A high performance convolution processor 3

A high performance convolution processor

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Visual Communications and Image Processing III 1988

作者： Côté, J.F. Collet, C. Haule, D.D. Malowany, A.S. Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University 3480 University Street MontrealQC Canada

This paper summarizes the design of a convolution processor card that is very low in cost, easy to use and most importantly, performs a 9 × 9 convolution in less than a second. Its high-performance is attributed to a VLSI systolic convolution cell which has been designed in our laboratories and to an efficient supporting data path architecture. The new Intel 82258 Advanced DMA Controller is used to perform each pixel transfer to and from the host computer's memory. Due to the DMA's software programmability, pictures of any size can be processed. The circuit is assembled on a 36 column MULTIBUS bus card and is installed on an Intel System 310 running iRMX 286 real-time multitasking operating system. © 1988 SPIE. All rights reserved.

关键词： Convolution

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Deriving course 3D models of objects

Deriving course 3D models of objects

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Conference on computer vision and Pattern Recognition (CVPR)

作者： F.P. Ferrie M.D. Levine Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Montreal QUE Canada

Much of the emphasis in computer vision research has been on recognizing objects. However, in order to perform tasks such as manipulating and avoiding collisions with objects, one needs to consider how to derive appropriate three-dimensional descriptions of objects from available sensor data. A description is given of a computational approach that has been successful in deriving renditions of shaded images in terms of volumetric primitives such as ellipsoids and cylinders.< >

关键词： Ellipsoids computer vision Shape Robot vision systems Laboratories Sensor phenomena and characterization Engine cylinders Robustness Eyes Collision avoidance

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EARLY ORIENTATION SELECTION—INFERRING TRACE, TANGENT, AND CURVATURE FIELDS

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International Journal of Pattern Recognition and Artificial Intelligence 1988年第3期2卷 443-457页

作者： STEVEN W. ZUCKER Canadian Institute for Advanced Research Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Montréal Québec Canada

Orientation selection is the inference of orientation information out of images. It is one of the foundations on which other visual structures are built, since it must precede the formation of contours out of pointillist data and surfaces out of surface markings. We take a differential geometric view in defining orientation selection and develop algorithms for actually doing it. The goal of these algorithms is formulated in mathematical terms as the inference of a vector field of tangents (to the contours), and the algorithms are studied in both abstract and computational forms. They are formulated as matching problems, and algorithms for solving them are reduced to biologically plausible terms. We show that two different matching problems are necessary, the first for 1-dimensional contours (which we refer to as Type I processes) and the second for 2-dimensional flows (or Type II processes).

关键词： computer vision Orientation selection Curve detection Relaxation Edge detection Texture

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Convected activation profiles and the measurement of visual motion

Convected activation profiles and the measurement of visual ...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： A.M. Waxman J. Wu F. Bergholm Laboratory for Sensory Robotics College of Engineering Boston University Boston MA USA Computer Vision and Graphics Laboratory Department of Numerical Analysis and Computing Science Royal Institute of Technology Stockholm Sweden

A method is developed for the measurement of short-range visual motion in image sequences, making use of the motion of image features such as edges and points. Each feature generates a Gaussian activation profile in a spatiotemporal neighborhood of specified scale around the feature itself; this profile is then convected with motion of the feature. The authors show that image velocity estimates can be obtained from such dynamic activation profiles using a modification of familiar gradient techniques. The resulting estimators can be formulated in terms of simple ratios of spatiotemporal filters (i.e. receptive fields) convolved with image feature maps. A family of activation profiles of varying scale must be utilized to cover a range of possible image velocities. They suggest a characteristic speed normalization of the estimate obtained from each filter in order to decide which estimate is to be accepted. They formulate the velocity estimators for dynamic edges in 1-D and 2-D image sequences, as well as that for dynamic feature points in 2-D image sequences.< >

关键词： Motion measurement Image sequences Motion analysis Filters Laboratories computer vision Robot sensing systems Educational institutions computer graphics Numerical analysis

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Industrial machine vision: lessons and challenges

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Optics News 1987年第2期13卷 12-17页

作者： Steven W. Holland Robert B. Tilove STEVEN W. HOLLAND is an assistant department head in the Computer Science Dept. of General Motors Research Laboratories (GMR). He is responsible for research projects involving computer vision robotics mobile robotics process planning knowledge systems natural language advanced programming environments parallel processing and statistics. ROBERT B. TILOVE is senior staff research scientist and project leader of the machine perception project at GMR. His research interests include geometric modeling model-based programming and control of intelligent automation systems.

No abstract available.

关键词： Integral imaging Machine vision Measurement and metrology Optical systems Sensors Statistics

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TOWARD EFFICIENT TRAJECTORY PLANNING - THE PATH-VELOCITY DECOMPOSITION

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INTERNATIONAL JOURNAL OF robotics RESEARCH 1986年第3期5卷 72-89页

作者： KANT, K ZUCKER, SW Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Montréal Québec Canada H3A2A7

We present a novel approach to solving the trajectory planning problem (TPP) in time-varying environments. The essence of our approach lies in a heuristic but natural decomposition of TPP into two subproblems: (1) planning a path to avoid collision with static obstacles and (2) planning the velocity along the path to avoid collision with moving obstacles. We call the first subproblem the path planning problem (PPP) and the second the velocity planning problem (VPP). Thus, our decomposition is summarized by the equation TPP right arrow PPP + VPP. The symbol right arrow indicates that the decomposition holds under certain assumptions, e.g., when obstacles are moving independently of (i.e., not tracking) the robot. Furthermore, we pose the VPP in path-time space, where time is explicitly represented as an extra dimension, and reduce it to a graph search in this space. In fact, VPP is transformed to a two-dimensional PPP in path-time space with some additional constraints. Algorithms are then presented to solve the VPP with different optimality criteria: minimum length in path-time space, and minimum time. [ABSTRACT FROM AUTHOR]

关键词： robotics

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Fast collision detection scheme by recursive decomposition of a manipulator workspace

Fast collision detection scheme by recursive decomposition o...

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IEEE International Conference on robotics and Automation (ICRA)

作者： V. Hayward Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Quebec Canada

This paper explains a simple method for fast collision detection in manipulator tasks. We show from examples taken in the literature that solutions to this problem can be chosen among a continuum of schemes, according to the method selected for representing the workspace and the robot, and the amount of computations performed before testing a particular trajectory. We then describe a method based on a recursive decomposition of the workspace, also referred to as an octree model, as a good tradeoff for a class of applications.

关键词： Robot programming Manipulators robotics and automation Solids Detectors computer vision Robot vision systems Laboratories Performance evaluation Testing

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EARLY ORIENTATION SELECTION - TANGENT FIELDS AND THE DIMENSIONALITY OF THEIR SUPPORT

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computer vision GRAPHICS AND IMAGE PROCESSING 1985年第1期32卷 74-103页

作者： ZUCKER, SW Computer Vision and Robotics Laboratory Department of Electrical Engineering McGill University Montréal Québec Canada

Orientation selection is the inference of orientation information out of images. It is one of the foundations on which other visual structures are built, since it must precede the formation of contours out of pointillist data and surfaces out of surface markings. We take a differential geometric view in defining orientation selection, and develop algorithms for actually doing it. The goal of these algorithms is formulated in mathematical terms as the inference of a vector field of tangents (to the contours), and the algorithms are studied in both abstract and computational forms. They are formulated as matching problems, and algorithms for solving them are reduced to biologically plausible terms. We show that two different matching problems are necessary, the first for 1-dimensional contours (which we refer to as Type I processes) and second for 2-dimensional flows (or Type II processes). We conjecture that this difference is reflected in the response properties of “simple” and “complex” cells, respectively, and predict several other psychophysical phenomena.

关键词： vision

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Scene Representations for Robotic Spatial Perception

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Control, robotics, and Autonomous Systems 1000年第1期8卷 351-377页

作者： Ruben Mascaro Margarita Chli 1Vision for Robotics Lab ETH Zurich Zurich Switzerland email: rmascaro@ethz.ch chlim@ethz.ch 2Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point cloud might be sufficient for localization, denser scene representations are required for obstacle avoidance. On the other hand, higher-level semantic information is often crucial for breaking down the necessary steps to autonomously complete a complex task, such as cooking. So the looming question is, What is a suitable scene representation for the robotic task at hand? This survey provides a comprehensive review of key approaches and frameworks driving progress in the field of robotic spatial perception, with a particular focus on the historical evolution and current trends in representation. By categorizing scene modeling techniques into three main types—metric, metric–semantic, and metric–semantic–topological—we discuss how spatial perception frameworks are transitioning from building purely geometric models of the world to more advanced data structures incorporating higher-level concepts, such as the notion of object instances and places. Special emphasis is placed on approaches for real-time simultaneous localization and mapping, their integration with deep learning for enhanced robustness and scene understanding, and their ability to handle scene dynamicity as some of the hottest topics of interest driving robotics research today. We conclude with a discussion of ongoing challenges and future research directions in the quest to develop robust and scalable spatial perception systems suitable for long-term autonomy.

关键词： spatial perception simultaneous localization and mapping SLAM semantic SLAM 3D scene graphs scene representations robotics

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