A Comparative Evaluation of Statistical Image Segmentation Techniques.
Abstract
Statistical image segmentation refers to the computer-oriented procedures that partition the image into meaningful parts by using the statistical pattern recognition techniques. Although most image segmentation works have been nonstatistical in nature, there is now strong interest in the use of the supervised and the unsupervised classification techniques for image segmentation. In this paper, a critical comparison is made on the supervised image segmentation techniques including the Fisher's linear discriminant, the autoregressive moving-average modelling, the maximum likelihood region estimation, and the maximum a posteriori region estimation, as well as on the unsupervised image segmentation techniques including the cluster analysis, the estimation-theory based method, histogram directed segmentation techniques, and the decision-directed method using the conditional population mixture model. Some computer results are presented. The fundamental issues in the statistical image segmentation and the related topics are also reviewed. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 26, 1981
- Accession Number
- ADA094237
Entities
People
- Chia‐Hung Chen
Organizations
- University of Massachusetts Dartmouth