Active Contours for Multispectral Images With Non-Homogeneous Sub-Regions
Abstract
In this work, we develop a framework for image segmentation which partitions an image based on the statistics of image intensity where the statistical information is represented as a mixture of probability density functions defined in a multi-dimensional image intensity space. Depending on the method to estimate the mixture density functions, three active contour models are proposed: unsupervised multi-dimensional histogram method, half-supervised multivariate Gaussian mixture density method, and supervised multivariate Gaussian mixture density method. The implementation of active contours is done using level sets. The proposed active contour models show robust segmentation capabilities on images where traditional segmentation methods show poor performance. Also, the proposed methods provide a means of autonomous pattern classification by integrating image segmentation and statistical pattern classification.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 16, 2005
- Accession Number
- ADA440479
Entities
People
- Wesley E. Snyder
Organizations
- North Carolina State University