Statistical filter for image feature extraction

Edge extraction techniques have become important as a preprocessing step in extraction of image features for the purpose of image segmentation, object identification, and bandwidth compression. The use of conventional edge extractors such as Sobel and Laplacian filters results in images that in many cases have a high degree of clutter due to the natural spatial texture of the scene background. To overcome this difficulty, a statistical filter has been developed that enhances local grey level activity around objects while reducing contributions due to background. The statistical filter is employed in a neighborhood modification process where the central pixel is replaced with the third central moment computed from the surrounding neighborhood.Choice of the third central moment is due in part to the fact that it is a function of the scene within the neighborhood rather than the power spectral density (Wiener spectrum) of the neighborhood. Application of the filter requires no prior knowledge, and pixels within the filter window may be chosen in random order due to the statistical nature of the operation. Results of the filter applied to IR images show performance comparable with, and in some cases superior to, the Sobel and Laplacian filters most commonly used for feature and edge extraction.