Research On Underwater Sonar Image Objects Detection And Contours Extraction Based Respectively On MRF And Level Set

With the rapid development of exploitation of marine fields, sonar technology plays an important role, in underwater navigation and positioning, object tracking and recognition, communications and so on. It has already become a significant issue in digital image processing field that the technology of object recognition of sonar image. The objects detection and feature extraction are key steps in underwater objects recognition processing. Owing that the detection method based on MRF (Markov Random Field, MRF) can precisely describe the dependent relationship between the classes of every pixel and its neighboring pixels, and the contour extraction algorithm based on level set gave a complete consideration on topological property, and it is effective even the underwater objects with hole, overlapping, and irregular shapes. This paper is committed to research on underwater objects detection based on MRF and contour extraction based on level set of the sonar image. The main research contents of this paper are shown as following:The grey distribution model of sonar image has great influence on underwater objects detection performance based on the model. On the base of analyzing sonar imaging principle and sonar image features, sonar image grey distribution model is researched. The object-highlight region grey distribution satisfying linear function which is applicable in certain range. According to current object-highlight region distribution model depicted by normalized linear equation, a proportional distribution model is proposed to accurately describe object-highlight region grey distribution.In the part of underwater sonar image objects detection based on MRF, regarding to the set of plane MRF model parameters complex problems in three-class detection ways, the new three-class plane MRF model parameters set is proposed which is high accuracy and can decrease the calculation work of parameters estimation. According to the structure property of incompletely hierarchical MRF of two-class, we propose to apply the new three-class plane MRF model parameters and model parameters of the level interaction into incompletely hierarchical MRF of three-class detection. Then a final accuracy three-class detection result will be obtained. Furthermore, in order to further improve detection precision, the Gamma distribution model is used to describe the background region gray distribution. Then an improved underwater sonar image objects detection method based on MRF is put forward.In the part of underwater sonar image contours extraction based on level set, the application of level set model to the full sonar image may cause background noise being mis-extracted as object-highlight region or shadow region due to topology of the model, so object evolution sub-region, which is determined by the MRF underwater objects detection results, is proposed to dwindle the search region. Besides the accuracy of contours extraction are also relative to the initial position of closed curves. To exactly extract the contours of object-highlight region and shadow region, the location of initial closed curves in each object evolution sub-region should be selected appropriately. In this paper, the centers coordinates are determined by the location of object-highlight and shadow regions.The contours of object-highlight and shadow regions are extracted by the four-phase piecewise constant Vese-Chan level set evolution functions. The paper summarized the research results and innovations, and look forward to the next work.