scholarly journals A Multiphase Level Set Evolution Scheme for Aerial Image Segmentation Using Multi-scale Image Geometric Analysis

2006 ◽  
pp. 56-64 ◽  
Author(s):  
Wang Wei ◽  
Yang Xin ◽  
Cao Guo
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Geometric Analysis ◽  
Aerial Image ◽  
Multi Scale ◽  
Level Set Evolution ◽  
Evolution Scheme

A MODIFIED MULTIPHASE LEVEL SET EVOLUTION SCHEME FOR AERIAL IMAGE SEGMENTATION

2007 ◽  
Vol 21 (07) ◽  
pp. 1195-1212 ◽  
Author(s):  
WANG WEI ◽  
YANG XIN
Keyword(s):  
Level Set ◽  
Geometric Analysis ◽  
Aerial Images ◽  
Contourlet Transform ◽  
Aerial Image ◽  
Global Features ◽  
Precise Shape ◽  
Level Set Evolution ◽  
Weighting Coefficients ◽  
Evolution Scheme

This paper describes an innovative aerial images segmentation algorithm. The algorithm is based upon the knowledge of image multiscale geometric analysis using contourlet transform, which can extract the image's intrinsic geometrical structure efficiently. The contourlet transform is introduced to represent the most distinguished and the rotation invariant features of the image. A modified Mumford–Shah model is applied to segment the aerial image by a multifeature level set evolution. To avoid possible local minima in the level set evolution, we adjust the weighting coefficients of the multiscale features in different evolution periods, i.e. the global features have bigger weighting coefficients at the beginning stages which roughly define the shape of the contour, then bigger weighting coefficients are assigned to the detailed features for segmenting the precise shape. When the algorithm is applied to segment the aerial images with several classes, satisfied experimental results are achieved by the proposed method.

Adaptive Distance Preserving Level Set Evolution for Image Segmentation

2009 ◽  
Vol 19 (12) ◽  
pp. 3161-3169 ◽  
Author(s):  
Chuan-Jiang HE ◽  
Meng LI ◽  
Yi ZHAN
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Level Set Evolution

scholarly journals Image Segmentation using Level Set Evolution Driven by Complex Wavelets

2016 ◽  
Vol 9 (26) ◽  
Author(s):  
G. Raghotham Reddy ◽  
B. Narsimha ◽  
B. Rajender Naik ◽  
Rameshwar Rao
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Complex Wavelets ◽  
Level Set Evolution

scholarly journals Shape Prior Embedded Level Set Model for Image Segmentation

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Wansuo Liu ◽  
Dengwei Wang ◽  
Wenjun Shi
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Geometric Transformation ◽  
Probability Weighting ◽  
Shape Prior ◽  
Factors Affecting ◽  
Sensitivity Problem ◽  
Level Set Evolution ◽  
Region Information ◽  
Level Set Model

This paper presents an optimized level set evolution (LSE) without reinitialization (LSEWR) model and a shape prior embedded level set model (LSM) for robust image segmentation. Firstly, by performing probability weighting and coefficient adaptive processing on the original LSEWR model, the optimized image energy term required by the proposed model is constructed. The purpose of the probability weighting is to introduce region information into the edge stop function to enhance the model’s ability to capture weak edges. The introduction of the adaptive coefficient enables the evolution process to automatically adjust its amplitude and direction according to the current image coordinate and local region information, thus completely solving the initialization sensitivity problem of the original LSEWR model. Secondly, a shape prior term driven by kernel density estimation (KDE) is additionally introduced into the optimized LSEWR model. The role of the KDE-driven shape prior term is to overcome the problem of image segmentation in the presence of geometric transformation and pattern interference. Even if there is obvious affine transformation in the shape prior and the target to be segmented, the target contour can still be reconstructed correctly. The extensive experiments on a large variety of synthetic and real images show that the proposed algorithm achieves excellent performance. In addition, several key factors affecting the performance of the proposed algorithm are analyzed in detail.

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