Performance Evaluation Of Some Algorithms For Acoustic Images Using Image Segmentation Techniqes

摘要

This paper is concerned with Unsupervised sonar image segmentation .We present a new estimation and segmentation procedure on images provided by a high resolution sonar. The sonar image is segmented in to two kinds of regions:Shadow (corresponding to a lack of acoustic reverberation behind each object lying on seabed) and Reverberation (Due the reflection of acoustic wave on the seabed and on the objects).The unsupervised contextual method is defined as a two-step processExpectation Maximization Algorithm and Statistical Region SnakeTheory[1]. The expectation maximization algorithm is very useful for parameter estimation problems in finite mixtures.The stochastic EM algorithm is a widely applicable approach for computing maximum likelihood estimates for the mixture problem[2].During past years the active contour models (Snakes) have been widely used for finding the contours of objects. This segmentation strategy is classically edge-based in the sense that the snake is driven to fit the maximum of an edge map of the scene.This technique has been successfully applied to real sonar images, and is compatible with an automatic processing of massive amounts of data