Proceedings PaperEdge extraction from multispectral images and density analysis of superdimensional spectral space
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The edges in an image could be considered as boundary lines between the classes to be analyzed and distinguished. Determining those boundary lines is important for the detection of image edges. As to the edges in the super dimensional spectral image data, the lower density zone in spectral space could be considered as the seeking range for the boundary between different objects. This paper discusses the principles and methods of density analysis for super dimensional spectral image data. One key for that is to determine the statistical unit of super dimensional space. The approaches include the method according the gray level combination in spectral space, the method of statistics starting from first pixel of image, the method of taking as the reference the first component of principal component transformation for spectral space, the method of determining the unit super sphere based on sample set etc. The experiments using one of the methods have shown the effectiveness of spectral space density analysis and have been discussed.