Using Polygons to Recognize and Locate Partially Occluded Objects

Koch, Mark W.; Kashyap, R.L.

doi:10.1109/tpami.1987.4767936

Cited by 119 publications

(21 citation statements)

References 23 publications

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“…However, algorithms for finding Procrustean mean shapes (Kristof and Wingersky 1971;Gower 1975;Berge 1977) were developed long before the topology of shape spaces were well-understood (Kendall et al 1999;Small 1996). In the classical computer vision literature, there has been considerable work on recognizing occluded objects, e.g., Lin and Chellappa (1987); Koch and Kashyap (1987); Grimson and Lozano-Pérez (1987). Recognizing and localizing occluded objects when the objects are rigid is known as the "binof-parts" or "bin-picking" problem.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic Models of Object Geometry with Application to Grasping

Glover

Rus

Roy

2009

The International Journal of Robotics Research

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Abstract-Robot manipulators typically rely on complete knowledge of object geometry in order to plan motions and compute grasps. But when an object is not fully in view it can be difficult to form an accurate estimate of the object's shape and pose, particularly when the object deforms.In this paper we describe a generative model of object geometry based on Mardia and Dryden's "Probabilistic Procrustean Shape" which captures both non-rigid deformations and object variability in a class. We extend their shape model to the setting where point correspondences are unknown using Scott and Nowak's COPAP framework. We use this model to recognize objects in a cluttered image and to infer their complete 2-D boundaries with a novel algorithm called OSIRIS. We show examples of learned models from image data and demonstrate how the models can be used by a manipulation planner to grasp objects in cluttered visual scenes.

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Section: Related Workmentioning

confidence: 99%

Probabilistic Models of Object Geometry with Application to Grasping

Glover

Rus

Roy

2009

The International Journal of Robotics Research

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“…There are 3 main kinds of template matching methods, namely template matching based on gray value [5], geometric features [6][7][8][9] and gradient orientation features [10,11]. The first method can get relatively high pose accuracy when the image edges are fuzzy, but this method requires high illumination stability and uniformity.…”

Section: Introductionmentioning

confidence: 99%

A fast template matching method for LED chip Localization

Zhong

2015

MATEC Web of Conferences

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Abstract. Efficiency determines the profits of the semiconductor producers. So the producers spare no effort to enhance the efficiency of every procedure. The purpose of the paper is to present a method to shorten the time to locate the LED chips on wafer. The method consists of 3 steps. Firstly, image segmentation and blob analyzation are used to predict the positions of potential chips. Then predict the orientations of potential chips based on their dominant orientations. Finally, according to the positions and orientations predicted above, locate the chips precisely based on gradient orientation features. Experiments show that the algorithm is faster than the traditional method we choose to locate the LED chips. Besides, even the orientations of the chips on wafer are of big deviation to the orientation of the template, the efficiency of this method won't be affected.

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“…The typical problems in objects recognition have been widely treated in Literature [3][4][5], and a lot of them take a special importance when the recognition process imply to identify a set of assembled components of an object [6][7][8]. In these cases, difficulties such as occlusions, deformations, highlights or shadows, appear.…”

Section: Introductionmentioning

confidence: 99%

Detection of partial occlusions of assembled components to simplify the disassembly tasks

Gil

Torres

Ortiz

et al. 2005

Int J Adv Manuf Technol

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An automatic disassembly cell requires of a computer vision system for recognition and localization of the products and each of theirs components. The detection of occlusions adds more information to the knowledge base to identify components and products, and generate a trustworthy and precise relational model (generic graph of hierarchic relations among the different components that make up the product). In this paper, a method to detect partial occlusions in assembled components is presented. This method is based on the fusion of regions and edges information, and it offers a certain degree of simplification for the recognition and modelled of the disassembly tasks, of the set of components which compose the product. The proposed approach to detect regions is a hybrid approach between RGB and HSV spaces. The bi-dimensional histogram V/S is employed for the selection of the appropriate thresholds which serve as an aid to diminish the influence of the highlights and shadows in images. The goal of this paper is to present an approach for the detection of occlusions in assembled components from a combination of HSV and RGB spaces, a bi-dimensional histogram and an edge detector.

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Using Polygons to Recognize and Locate Partially Occluded Objects

Cited by 119 publications

References 23 publications

Probabilistic Models of Object Geometry with Application to Grasping

Probabilistic Models of Object Geometry with Application to Grasping

A fast template matching method for LED chip Localization

Detection of partial occlusions of assembled components to simplify the disassembly tasks

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