While executing tasks such as ocean pollution monitoring, maritime rescue, geographic mapping, and automatic
navigation utilizing remote sensing images, the coastline feature should be determined. Traditional methods are not
satisfactory to extract coastline in high-resolution panchromatic remote sensing image. Active contour model, also called snakes, have proven useful for interactive specification of image contours, so it is used as an effective coastlines extraction technique. Firstly, coastlines are detected by water segmentation and boundary tracking, which are considered initial contours to be optimized through active contour model. As better energy functions are developed, the power assist of snakes becomes effective. New internal energy has been done to reduce problems caused by convergence to local minima, and new external energy can greatly enlarge the capture region around features of interest. After normalization processing, energies are iterated using greedy algorithm to accelerate convergence rate. The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.
To extract circular array targets from high-spatial resolution optical satellite images, we propose an efficient two-step detection framework. In the first stage, we propose a multiscale histogram contrast (MHC) saliency evaluation model to detect salient objects. This saliency model is able to highlight all salient areas with well-defined boundaries and effectively suppress the obvious background textures. Moreover, the generated saliency map can be used to find the locations of potential targets most likely causing visual attention, which greatly decreases the computation cost in later processes. In the second stage, we extract task-related targets from these salient objects by removing the irrelevant salient areas according to the special properties of the targets. In this paper, we present a circular array targets-detection method based on the saliency map produced by the proposed saliency detector. First we extract the circular objects from the saliency map using simple shape features, which are then clustered through a graph-based approach to locate the target areas. To evaluate the performance of our saliency model and targets extraction scheme, we carried out many experiments on large-field, high-spatial resolution remote-sensing images and achieved promising results both in efficiency and precision.
Because of the huge computation in 3D medical data visualization, looking into its inner data interactively is always a
problem to be resolved. In this paper, we present a novel approach to explore 3D medical dataset in real time by utilizing
a 3D widget to manipulate the scanning plane. With the help of the 3D texture property in modern graphics card, a
virtual scanning probe is used to explore oblique clipping plane of medical volume data in real time. A 3D model of the
medical dataset is also rendered to illustrate the relationship between the scanning-plane image and the other tissues in
medical data. It will be a valuable tool in anatomy education and understanding of medical images in the medical