Paper Info
Title
Detection, classification and estimation of individual shapes in 2D and 3D point clouds
Abstract
The problems of detecting, classifying, and estimating shapes in point cloud data are important due to their general applicability in image analysis, computer vision, and graphics. They are challenging because the data is typically noisy, cluttered, and unordered. We study these problems using a fully statistical model where the data is modeled using a Poisson process on the object's boundary (curves or surfaces), corrupted by additive noise and a clutter process. Using likelihood functions dictated by the model, we develop a generalized likelihood ratio test for detecting a shape in a point cloud. This ratio test is based on optimizing over some unknown parameters, including the pose and scale associated with hypothesized objects, and an empirical evaluation of the log-likelihood ratio distribution. Additionally, we develop a procedure for estimating most likely shapes in observed point clouds under given shape hypotheses. We demonstrate this framework using examples of 2D and 3D shape detection and estimation in both real and simulated data, and a usage of this framework in shape retrieval from a 3D shape database.
Year
DOI
Venue
2013
10.1016/j.csda.2012.09.008
Computational Statistics & Data Analysis
Keywords
Field
DocType
shape detection,log-likelihood ratio distribution,shape hypothesis,shape retrieval,shape database,likely shape,observed point cloud,simulated data,point cloud data,individual shape,generalized likelihood ratio test,poisson process,point cloud
Active shape model,Point distribution model,Likelihood-ratio test,Pattern recognition,Clutter,Statistical model,Artificial intelligence,Statistics,Point cloud,Ratio test,Mathematics,Shape analysis (digital geometry)
Journal
Volume
ISSN
Citations 
58,
0167-9473
7
PageRank 
References 
Authors
0.54
18
3
Name
Order
Citations
PageRank
Jing-yong Su115610.93
Anuj Srivastava22853199.47
Fred Huffer392.95