Purely bottom-up, unsupervised target segmentation is one of the most challenging problems in satellite image interpretation within the computer vision community. In this paper, we focus on the problem of automatically segmenting aircrafts from high-resolution satellite images based on the idea of co-segmentation. First, we selectively segment out the regions of interest (ROIs) from the satellite images. Then, we apply a region based shadow detection and removal approach to remove shadows. Finally, the anisotropic heat diffusion model is employed to fulfill our co-segmentation task. Experimental results on a given dataset have demonstrated the effectiveness of our method.

No abstract

In this paper, we present a novel technique for quickly generating large-scale digital watermarks. Generally, digital watermark can be embedded in any copyright image whose size is not larger than it. But nowadays, more and more high-resolution pictures call for larger and larger watermark templates, while most traditional watermarking algorithms work slowly on templates of large scale. To solve this problem, we firstly produce a pseudo-random number sequence, using the Logistic Function and then convert it to a binary 2D image. Afterwards, cellular automata are used to turn this image into an elementary watermark template. We regard this chaotic featured watermark as an input texture, and apply a multilevel texture synthesis method to it. In this way, large-scale watermarks can be obtained at acceptable cost of time. Keywords-large-scale digital watermark; cellular automata; texture synthesis; fast algorithmI.

No abstract