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On recognizing and positioning curved 3-D objects from image contours

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TLDR
An approach for explicitly relating the shape of image contours to models of curved three-dimensional objects is presented and readily extends to parameterized models.
Abstract
An approach for explicitly relating the shape of image contours to models of curved three-dimensional objects is presented. This relationship is used for object recognition and positioning. Object models consist of collections of parametric surface patches and their intersection curves; this includes nearly all representations used in computer-aided geometric design and computer vision. The image contours considered are the projections of surface discontinuities and occluding contours. Elimination theory provides a method for constructing the implicit equation of these contours for an object observed under orthographic or perspective projection. This equation is parameterized by the object's position and orientation with respect to the observer. Determining these parameters is reduced to a fitting problem between the theoretical contour and the observed data points. The proposed approach readily extends to parameterized models. It has been implemented for a simple world composed of various surfaces of revolution and tested on several real images. >

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Citations
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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.
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Shock graphs and shape matching

TL;DR: A novel tree matching algorithm is introduced which finds the best set of corresponding nodes between two shock trees in polynomial time and is demonstrated under articulation, occlusion, and moderate changes in viewpoint.
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A Survey Of Free-Form Object Representation and Recognition Techniques

TL;DR: This survey reviews recent literature on both the 3D model building process and techniques used to match and identify free-form objects from imagery to offer the computer vision practitioner new ways to recognize and localize free- form objects.
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Invariant descriptors for 3D object recognition and pose

TL;DR: A model-based vision system that recognizes curved plane objects irrespective of their pose is demonstrated and the stability of a range of invariant descriptors to measurement error is treated in detail.
Journal ArticleDOI

On the Repeatability and Quality of Keypoints for Local Feature-based 3D Object Retrieval from Cluttered Scenes

TL;DR: An algorithm for the detection of highly repeatable keypoints on 3D models and partial views of objects and an automatic scale selection technique for extracting multi-scale and scale invariant features to match objects at different unknown scales are presented.
References
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Numerical Recipes in FORTRAN - The Art of Scientific Computing - Second Edition

TL;DR: This paper presents a list of recommended recipes for making CDRom decks and some examples of how these recipes can be modified to suit theommelier's needs.
Book

The complexity of robot motion planning

TL;DR: John Canny resolves long-standing problems concerning the complexity of motion planning and, for the central problem of finding a collision free path for a jointed robot in the presence of obstacles, obtains exponential speedups over existing algorithms by applying high-powered new mathematical techniques.
Journal ArticleDOI

Three-dimensional object recognition

TL;DR: In this paper, a precise definition of the 3D object recognition problem is proposed, and basic concepts associated with this problem are discussed, and a review of relevant literature is provided.
Journal ArticleDOI

Superquadrics and Angle-Preserving Transformations

TL;DR: A new and powerful family of parametric shapes is introduced that extends the basic quadric surfaces and solids, yielding a variety of useful forms.
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