Background Foreground Based Underwater Image Segmentation

Under water object detection is widely discussed area to detect objects, mining, vehicles and species in oceans and seas To perform a color-based recognition of an object, we develop an algorithm robust with respect to the attenuation which takes into account the light modification during its path between the light source and the camera. Underwater computer vision has to cope with distortion and attenuation due to light propagation in water and with challenging operating condition. In previous method, if any of the objects are in same color and same size we cannot detect the exact object. Moreover, detected image contains salt and pepper effect and also distorted by some background unwanted images. Two algorithms are used to rectify these effects. .In this paper Principal components analysis is used. PC ALGORITHM is used to remove background images and shape recognition, and FUZZY METHOD is used for scene segmentation are designed to achieve robust object detection as it produces a clarity image. SOBEL ALGORITHM is used to eradicate the salt and pepper effect in the image. The robustness of the proposed method results for different captusred images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines.