Results 221 to 230 of about 291,539 (260)

Biomimetic whiskers for shape recognition

Robotics and Autonomous Systems, 2007
Rodents demonstrate an outstanding capability of tactile perception with their whiskers. Mechanoreceptors surrounding the whisker shaft in their follicle structure measure deflection of the whisker. We designed biomimetic whiskers following the basic design of the follicle.
Kim, DaeEun, Möller, Ralf
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Shape Matching and Object Recognition

2006
We address comparing related, but not identical shapes in images following a deformable template strategy. At the heart of this is the notion of an alignment between the shapes to be matched. The transformation necessary for alignment and the remaining differences after alignment are then used to make a comparison.
Alexander C. Berg, Jitendra Malik
openaire   +1 more source

Shape Recognition in Three Dimensions

Environment and Planning B: Planning and Design, 1992
The subshape recognition problem for three-dimensional shapes under linear transformations is considered. The problem is analysed in a series of cases, some that provide a determinate number of solutions and others that have indeterminately many solutions. Procedures for its solution for general shapes are developed.
R Krishnamurti, C F Earl
openaire   +2 more sources

Robust Occluded Shape Recognition

2006
The primary reason for shape characterization and matching is to use it for characterization and recognition of the associated objects. However, the shapes obtained from segmentation and/or edge detection of real world images are, at best, approximations of the actual shapes of objects. Unsupervised segmentations often deviate from object boundaries to
Ronak Shah, Anima Mishra, Subrata Rakshit   +2 more
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Shape based object recognition

2005
This paper examines the problem of shape based object recognition and proposes an approach to it based on certain characteristic planes of an object. It deals with a certain class of 3-D objects and their shapes. A shape distance for such objects is proposed on the basis of which shape discrimination between 3-D objects is possible.
D. K. Banerjee, Swapan K. Parui, D. Dutta Majumder   +2 more
openaire   +1 more source

Generic shape learning and recognition

1996
We address the problem of generic shape recognition, in which exact models are not available. We propose an original approach, in which learning and recognition are intimately linked, as recognition is based on previous observation.
Alexandre R. J. François, Gérard G. Medioni   +1 more
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A framework for shape representation and recognition

Proceedings of 1st International Conference on Image Processing, 2002
Describes a novel framework which represents and recognizes animate objects from their silhouettes. The authors model animate objects at three levels of complexity: (i) primitives, (ii) mid-grained shapes, which are deformations of the primitives, and (iii) objects constructed by using a grammar to join mid-grained shapes together.
Song Chun Zhu, Alan L. Yuille
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Shape recognition on a Riemannian manifold

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012
In this paper, we propose to perform shape recognition on a Riemannian manifold. Shape representation on a manifold have the advantage to be intrinsically invariant to shape preserving transformation, such as scaling and translation. Also, shape distance can be naturally computed because Riemannian manifolds are metric spaces.
Youssouf Chherawala, Mohamed Cheriet
openaire   +1 more source

Shape normalisation for face recognition

1997
This paper presents methods for shape normalisation of face images. Localisation and shape normalisation are prerequisites for face recognition algorithms like the eigenface approach. Other established face recognition methods like labeled graph matching can also be accelerated by providing normalised face image databases.
Fischer, S., Duc, B.
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Bounds on shape recognition performance

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995
The localization and the recognition tasks are analyzed here relying on a probabilistic model, and independently of the recognition method used. Rigorous upper and lower bounds on the probability that a set of measurements is sufficient to localize an object within a certain precision, are derived. The bounds quantify the difficulty of the localization
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