A novel statistic based algorithm for watermarking 2D vector maps is proposed. Unlike other multimedia data types, 2D vector map data has many special features, which make the selection of the cover data become a crucial issue directly influencing the performance of the watermarking scheme. In our proposed scheme, the selected cover data is a distance sequence extracted from the original map based on a feature point detecting procession. For a certain original map, this distance sequence can be regarded as a random variable with a stable distribution and the watermark is embedded by changing its distribution. Watermark extraction is based on statistic detection. Experiment results show that the proposed method has better shape-preserving performance, which is of great importance for vector maps. Moreover, this scheme is proved robust to certain attacks such as map interpolation, simplification, data reordering and some geometric transformations like rotation, translation, and their combination.
It is very difficult to detect the small target over water in the marine environment because of the complexity of water movement and the complex physical fields produced by the water interact with environment. We mainly study foreground segmentation for small target in visual image based on MRF (Markov Random Fields). A foreground segmentation method is proposed that is based on kernel function and MRF. In this method, the kernel function uses the special and temporal correlation between neighbor pixels. We take the probability distribution of the kernel function method as the observed value of the MRF, and get the energy function of the MRF. Results show our method is applicable to low speed small target segmentation in sea image.
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