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Showing papers on "3D single-object recognition published in 1983"


Journal ArticleDOI
TL;DR: In this article, the authors describe an approach to the recognition of stacked objects with planar and curved surfaces. But their system works in two phases: in the learning phase, a scene containing a single object is shown one at a time, and then the description is matched to the object models so that the stacked objects are recognized sequentially.
Abstract: This paper describes an approach to the recognition of stacked objects with planar and curved surfaces. The system works in two phases. In the learning phase, a scene containing a single object is shown one at a time. The range data of a scene are obtained by a range finder. The description of each scene is built in terms of properties of regions and relations between them. This description is stored as an object model. In the recognition phase, an unknown scene is described in the same way as in the learning phase. Then the description is matched to the object models so that the stacked objects are recognized sequentially. Efficient matching is achieved by a combination of data-driven and model-driven search processes. Experimental results for blocks and machine parts are shown.

250 citations


Journal ArticleDOI
TL;DR: This paper describes a method for handling this case: a known object is detected by finding changes in orientation, translation, and scale of the object from its canonical description, a Hough technique and has the characteristic insensitivity to occlusion and noise.
Abstract: An important problem in vision is to detect the presence of a known rigid 3-D object. The general 3-D object recognition task can be thought of as building a description of the object that must have at least two parts: 1) the internal description of the object itself (with respect to an object-centered frame); and 2) the transformation of the object-centered frame to the viewer-centered (image) frame. The reason for this decomposition is parsimony: different views of the object should have minimal impact on its description. This is achieved by factoring the object's description into two sets of parameters, one which is view-independent (the object-centered component) and one which is view-varying (the viewing transformation). Often a description of the object is known beforehand and the task reduces to finding the objectframe to viewer-frame transformation. This paper describes a method for handling this case: a known object is detected by finding changes in orientation, translation, and scale of the object from its canonical description. The method is a Hough technique and has the characteristic insensitivity to occlusion and noise.

98 citations


Proceedings ArticleDOI
Jakub Segen1
23 May 1983
TL;DR: A new method for selecting features for object recognition based on training data is proposed, which avoids overspecifying or selecting too many features by using the criterion of minimal representation, which penalizes the representation complexity of features.
Abstract: A new method for selecting features for object recognition based on training data is proposed. This method avoids overspecifying or selecting too many features by using the criterion of minimal representation, which penalizes the representation complexity of features. The presented approach can be used to search for high level structural features such as relations or production rules.© (1983) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

3 citations


Proceedings ArticleDOI
01 Apr 1983
TL;DR: These polynomial fits act as remarkably effective nonlinear lowpass filters for surface-type recognition and object location and orientation estimation in robust real-time 3-D object recognition.
Abstract: In order to realize robust real-time 3-D object recognition and object location and orientation estimation, a 2-D image is partitioned into small square windows which are processed in parallel. It is assumed that a 3-D object can be approximated by chunks of planes, spheres, and cylinders and that one such surface type is seen within a small window. Our solutions are largely for matte surfaces (i.e. diffusely reflecting); we do touch on specular (i.e, mirror) surfaces. Two kinds of results have been obtained in this work. For the purpose of 3-D object type recognition, a simple 2-D polynomial approximation is made to the image data in a window. Based on the relationships between polynomial coefficients and 3-D surface shape-types, a reliable decision can be made as to whether the object surface seen is that of a plane, a cylinder or sphere. In addition to providing the appropriate observables for surface-type recognition, these polynomial fits act as remarkably effective nonlinear lowpass filters.

1 citations