NDLI: MSTAR image segmentation with multi-phase level set based on probability density model

Content Provider	IEEE Xplore Digital Library
Author	Xiaojin Hou Lin Yan Shuang Wang Biao Hou
Copyright Year	2014
Description	Author affiliation: DFH Satellite Co. Ltd., Beijing, China (Xiaojin Hou; Lin Yan) \|\| Key Lab. of Intell. Perception & Image Understanding, Xidian Univ., Xi'an, China (Shuang Wang; Biao Hou)
Abstract	Radar image segmentation is a fundamental problem in radar image interpretation. Radar images often contain a great deal of noise. Level set method, known as deformable model, is a powerful image segmentation technique. It can get accurate contours of clear-cut objects in image without noise, but has poor performance in getting contours of objects in a noisy image. In this paper, a new multi-phase level set based on probability density model is proposed. We use histogram, a non-parametric density estimation method, to describe the statistical information of each pixel in its neighborhood and the pixels in each subset in the image. The comparability between them computed by inner product function is used as the curve energy in multi-phase level set method. The statistical information is incorporated into the multi-phase level set framework, which can cope with the influence of noise on image segmentation. This new method is particularly well adapted to detection of objects of interesting in a noisy image. We illustrated the performance of the new method on MSTAR images. The experimental results show that incorporating statistical information into the multi-phase level set framework, consistent objects are obtained, and accurate and robust segmentations can be achieved.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	1721
Ending Page	1724
File Size	292859
Page Count	4
File Format	PDF
ISBN	9781479957750
DOI	10.1109/IGARSS.2014.6946783
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-13
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Level set Image segmentation Histograms Active contours Computational modeling Probability Noise MSTAR image segmentation multi-phase level set probability density model inner product function
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Fourier-based multi-references shape prior for region-based active contours

Image segmentation with global active contours

Fast and robust active contours for image segmentation

Active contour based on different mathematic statistics

Multi-Region Active Contours with a Single Level Set Function

A local histogram based Chan-Vese model for segmentation

Efficient active contour model based on Vese-Chan model and split bregman method

Image Segmentation With Cage Active Contours

Ratio- and distribution-metric-based active contours for SAR image segmentation

MSTAR image segmentation with multi-phase level set based on probability density model

Similar Documents

Fourier-based multi-references shape prior for region-based active contours

Image segmentation with global active contours

Fast and robust active contours for image segmentation

Active contour based on different mathematic statistics

Multi-Region Active Contours with a Single Level Set Function

A local histogram based Chan-Vese model for segmentation

Efficient active contour model based on Vese-Chan model and split bregman method

Image Segmentation With Cage Active Contours

Ratio- and distribution-metric-based active contours for SAR image segmentation

MSTAR image segmentation with multi-phase level set based on probability density model