Ship detection by salient convex boundaries

Automatic ship detection from remote sensing imagery has many applications, such as maritime security, traffic surveillance, fisheries management. However, it is still a difficult task for noise and distractors. This paper is concerned with perceptual organization, which detect salient convex structures of ships from noisy images. Because the line segments of contour of ships compose a convex set, a local gradient analysis is adopted to filter out the edges which are not on the contour as preprocess. For convexity is the significant feature, we apply the salience as the prior probability to detect. Feature angle constraint helps us compute probability estimate and choose correct contour in many candidate closed line groups. Finally, the experimental results are demonstrated on the satellite imagery from Google earth.