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Bayesian classification of shapes hidden in point cloud data.

Srivastava, A. and Jermyn, I.H. (2009) 'Bayesian classification of shapes hidden in point cloud data.', in IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009 (DSP/SPE 2009) ; proceedings. Piscataway, NJ: IEEE, pp. 359-364.

Abstract

An interesting challenge in image processing is to classify shapes of polygons formed by selecting and ordering points in a 2D cluttered point cloud. This kind of data can result, for example, from a simple preprocessing of images containing objects with prominent boundaries. Taking an analysis-by-synthesis approach, we simulate high-probability configurations of sampled contours using models learnt from the training data to evaluate the given test data. To facilitate simulations, we develop statistical models for sources of (nuisance) variability: (i) shape variations of contours within classes, (ii) variability in sampling continuous curves into points, (iii) pose and scale variability, (iv) observation noise, and (v) points introduced by clutter. Finally, using a Monte Carlo approach, we estimate the posterior probabilities of different classes which leads to a Bayesian classification.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1109/DSP.2009.4785949
Publisher statement:© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:15 April 2016
Date of first online publication:January 2009
Date first made open access:No date available

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