[Show abstract][Hide abstract] ABSTRACT: 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.
[Show abstract][Hide abstract] ABSTRACT: This paper presents an adaptive tracking algorithm by online features enhancement. To avoid the distraction of the similar background on tracker, Bayes decision rule is applied to calculate the posterior probability of every pixel belonging to the object and generate a set of candidate confidence maps according to the conditional sample densities from object and background on different features. We evaluate the performance of every candidate confidence map using moment of inertia. Then, an optimal confidence map is selected to be fed to Meanshift which is employed to find the location of the object. At last, we update the target model by the confidence map. Experimental validation of the proposed method is performed and presented on challenging image sequences.