Bayesian classification of shapes hidden in point cloud data

Srivastava, A.; Jermyn, I.H.

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.

Citation

Srivastava, A., & Jermyn, I. (2009, January). Bayesian classification of shapes hidden in point cloud data. Presented at IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009 (DSP/SPE 2009 ), Marco Island, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009 (DSP/SPE 2009 )
• Publication Date: Jan 1, 2009
• Deposit Date: Aug 12, 2011
• Publicly Available Date: Apr 15, 2016
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 359-364
• Book Title: IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009 (DSP/SPE 2009) ; proceedings
• DOI: https://doi.org/10.1109/dsp.2009.4785949
• Public URL: https://durham-repository.worktribe.com/output/1158785