Active Contours for Multispectral Images With Non-Homogeneous Sub- Regions

摘要

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.