No abstract

The exploration of oceans and sea beds is being made increasingly possible through the development of Autonomous Underwater Vehicles (AUVs). This is an activity that concerns the marine community and it must confront the existence of notable challenges. However, an automatic detecting and tracking system is the first and foremost element for an AUV or an aqueous surveillance network. In this paper a method of Kalman filter was presented to solve the problems of objects track in sonar images. Region of object was extracted by threshold segment and morphology process, and the features of invariant moment and area were analysed. Results show that the method presented has the advantages of good robustness, high accuracy and real-time characteristic, and it is efficient in underwater target track based on sonar images and also suited for the purpose of Obstacle avoidance for the AUV to operate in the constrained underwater environment.

The SAR and SAS images are perturbed by a multiplicative noise called speckle, due to the coherent nature of the scattering phenomenon. If the background of an image is uneven, the fixed thresholding technique is not suitable to segment an image using adaptive thresholding method. In this paper a new Adaptive thresholding method is proposed to reduce the speckle noise, preserving the structural features and textural information of Sector Scan SONAR (Sound Navigation and Ranging) images. Due to the massive proliferation of SONAR images, the proposed method is very appealing in under water environment applications. In fact it is a pre-treatment required in any SONAR