Range image segmentation based on randomized Hough transform

Authors:
Yihong Ding;Xijian Ping;Min Hu;Dan Wang
Affiliations:
College of Information Engineering, Information Engineering University, Zhengzhou 450002, China;College of Information Engineering, Information Engineering University, Zhengzhou 450002, China;College of Information Engineering, Information Engineering University, Zhengzhou 450002, China;College of Information Engineering, Information Engineering University, Zhengzhou 450002, China
Venue:
Pattern Recognition Letters
Year:
2005

Citing 9
Cited 13

A survey of the Hough transform

Computer Vision, Graphics, and Image Processing
Segmentation through Variable-Order Surface Fitting

IEEE Transactions on Pattern Analysis and Machine Intelligence
Range Image Segmentation Based on Differential Geometry: A Hybrid Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new curve detection method: randomized Hough transform (RHT)

Pattern Recognition Letters
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities

CVGIP: Image Understanding
An Experimental Comparison of Range Image Segmentation Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in range images based on scan line approximation

Computer Vision and Image Understanding
MIR: An Approach to Robust Clustering-Application to Range Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence

Consistency of robust estimators in multi-structural visual data segmentation

Pattern Recognition
A Multi-agent Approach for Range Image Segmentation

CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
An Agent-Based Approach for Range Image Segmentation

Massively Multi-Agent Technology
Embedded planar surface segmentation system for stereo images

Machine Vision and Applications
A multi-agent approach for range image segmentation with Bayesian edge regularization

ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
A new Bayesian method for range image segmentation

EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Bayesian edge regularization in range image segmentation

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Planar background elimination in range images: a practical approach

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Edge detecting for range data using laplacian operators

IEEE Transactions on Image Processing
Multi-scale edge detection on range and intensity images

Pattern Recognition
Optimal consensus set for annulus fitting

DGCI'11 Proceedings of the 16th IAPR international conference on Discrete geometry for computer imagery
A new distributed approach for range image segmentation

CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Efficient robust digital annulus fitting with bounded error

DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery

Quantified Score

Hi-index	0.10

Visualization

Abstract

Range image analysis is one of the most important subjects in the fields of computer vision and pattern recognition, and range image segmentation is the key of the analysis. This paper presents a novel range image segmentation algorithm based on RHT (randomized Hough transform). The algorithm finds planar regions by utilizing RHT and has the advantage of insensitivity to noise. The proposed algorithm has been tested on a popular range image database (ABW image database) with comparisons to four other typical range image segmentation algorithms. The experiment results show that the proposed algorithm has a strong anti-noise ability and is well-performing in range image segmentation.