Detection of shapes in 2D point clouds generated from images

Jingyong Su; Zhiqiang Zhu; Anuj Srivastava; Fred Huffer

doi:10.1109/ICPR.2010.647

Detection of shapes in 2D point clouds generated from images

Jingyong Su^*
, Zhiqiang Zhu
, Anuj Srivastava
, Fred Huffer

^*Corresponding author for this work

Florida State University
Colorado State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are in turn extracted from images. In this model-based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson process for points from background clutter, and an additive Gaussian model for noise. Combining these with a past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data.

Original language	English
Title of host publication	Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2640-2643
Number of pages	4
ISBN (Print)	9780769541099
DOIs	https://doi.org/10.1109/ICPR.2010.647
State	Published - 2010
Externally published	Yes

Publication series

Name	Proceedings - International Conference on Pattern Recognition
ISSN (Print)	1051-4651

Keywords

Generalized likelihood ratio test
Poisson process
Shape detection

Access to Document

10.1109/ICPR.2010.647

Cite this

Su, J., Zhu, Z., Srivastava, A., & Huffer, F. (2010). Detection of shapes in 2D point clouds generated from images. In Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010 (pp. 2640-2643). (Proceedings - International Conference on Pattern Recognition). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2010.647

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title = "Detection of shapes in 2D point clouds generated from images",

abstract = "We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are in turn extracted from images. In this model-based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson process for points from background clutter, and an additive Gaussian model for noise. Combining these with a past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data.",

keywords = "Generalized likelihood ratio test, Poisson process, Shape detection",

author = "Jingyong Su and Zhiqiang Zhu and Anuj Srivastava and Fred Huffer",

year = "2010",

doi = "10.1109/ICPR.2010.647",

language = "英语",

isbn = "9780769541099",

series = "Proceedings - International Conference on Pattern Recognition",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Detection of shapes in 2D point clouds generated from images. / Su, Jingyong; Zhu, Zhiqiang; Srivastava, Anuj et al.
Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010. Institute of Electrical and Electronics Engineers Inc., 2010. p. 2640-2643 (Proceedings - International Conference on Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Zhu, Zhiqiang

AU - Srivastava, Anuj

AU - Huffer, Fred

PY - 2010

Y1 - 2010

N2 - We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are in turn extracted from images. In this model-based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson process for points from background clutter, and an additive Gaussian model for noise. Combining these with a past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data.

AB - We present a novel statistical framework for detecting pre-determined shape classes in 2D cluttered point clouds, which are in turn extracted from images. In this model-based approach, we use a 1D Poisson process for sampling points on shapes, a 2D Poisson process for points from background clutter, and an additive Gaussian model for noise. Combining these with a past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data.

KW - Generalized likelihood ratio test

KW - Poisson process

KW - Shape detection

UR - https://www.scopus.com/pages/publications/78149490546

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DO - 10.1109/ICPR.2010.647

M3 - 会议稿件

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BT - Proceedings - 2010 20th International Conference on Pattern Recognition, ICPR 2010

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Detection of shapes in 2D point clouds generated from images

Abstract

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Keywords

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