Tracking objects using image disparities
TL;DR: A system that finds and tracks known polyhedral objects in 3-space, given a sequence of grey-level images is presented, which integrates three established algorithms in a novel way.
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About: This article is published in Image and Vision Computing. The article was published on 01 Feb 1990. and is currently open access. The article focuses on the topics: 3D projection & Projection (set theory).
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Citations
Fitting parameterized three-dimensional models to images
TL;DR: Current methods of parameter solving are extended to handle objects with arbitrary curved surfaces and with any number of internal parameters representing articulation, variable dimensions, or surface deformations to allow model-based vision to be used for a much wider class of problems than was possible with previous methods.
Motion tracking with an active camera
D. Murray,Anup Basu +1 more
TL;DR: The system successfully extracts moving edges from dynamic images even when the pan/tilt angles between successive frames are as large as 3.5m, and the use of morphological filtering of motion images is explored to desensitize the detection algorithm to inaccuracies in background compensation.
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RAPID - a video rate object tracker.
Chris Harris,Carl Stennett +1 more
- 01 Jan 1990
TL;DR: Three-dimensional (3D) Model-Based Vision enables observed image features to be used to determine the pose (ie. position and attitude) of a known 3D object with respect to the camera (or alternatively, the viewpoint of the camera withrespect to the model).
The North Atlantic Treaty Organization
Hitoshi Suzuki
- 13 Sep 2011
TL;DR: In this paper, the authors present a lecture series on pattern recognition techniques for real-time visual machine perception, principles and applications in G&C real time speech recognition and understanding.
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Model-based tracking of complex articulated objects
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- 01 Feb 2001
TL;DR: Methods for tracking complex, articulated objects are presented, assuming that an appearance model and the kinematic structure of the object to be tracked are given, leading to what is termed a model-based object tracker.
References
A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
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Automatic Synthesis of Fine-Motion Strategies for Robots
TL;DR: A formal approach to the synthesis of compliant-motion strategies from geometric descriptions of assembly operations and explicit estimates of errors in sensing and control is described.
Disparity Analysis of Images
TL;DR: An algorithm for matching images of real world scenes is presented, which quickly converges to good estimates of disparity, which reflect the spatial organization of the scene.
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Automatic synthesis of fine-motion strategies for robots
Tomás Lozano-Pérez,Matthew T. Mason,Russell H. Taylor +2 more
- 01 Jun 1991
TL;DR: In this article, a formal approach to the synthesis of compliant motion strategies from geometric descriptions of assembly operations and explicit estimates of errors in sensing and control is presented, where correctness criteria for compliant motion strategy are provided.
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Model-based recognition and localization from sparse range or tactile data
TL;DR: In this paper, the authors show that inconsistent hypotheses about pairings between sensed points and object surfaces can be discarded efficiently by using local constraints on distances between faces, angles between face normals, and angles (relative to the surface normals) of vectors between the sensed points.