Author:
Xianghua Xie
Affiliation:
University of Wales Swansea, United Kingdom
Keyword(s):
Active contour, Local spectral histogram, Level set method, Texture segmentation, Wasserstein distance.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Feature Extraction
;
Features Extraction
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Filtering
;
Image Formation and Preprocessing
;
Informatics in Control, Automation and Robotics
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Segmentation and Grouping
;
Sensor Networks
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
In this paper, we propose a novel level set based active contour model to segment textured images. The proposed methods is based on the assumption that local histograms of filtering responses between foreground and background regions are statistically separable. In order to be able to handle texture non-uniformities, which often occur in real world images, we use rotation invariant filtering features and local spectral histograms as image feature to drive the snake segmentation. Automatic histogram bin size selection is carried out so that its underlying distribution can be best represented. Experimental results on both synthetic and real data show promising results and significant improvements compared to direct modeling of filtering responses.