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Intensity-based edge detectors cannot distinguish whether an edge is caused by material changes, shadows, surface orientation changes or by highlights. Therefore, our aim is to classify the physical cause of an edge using hyperspectra obtained by a spectrograph. Methods are presented to detect edges in hyperspectral images. In theory, the effect of varying imaging conditions is analyzed for raw hyper-spectra, for normalized hyper-spectra, and for hue computed from hyper-spectra. From this analysis, an edge classifier is derived which distinguishes hyper-spectral edges into the following types: (1) a shadow or geometry edge, (2) a highlight edge, (3) a material edge. 1 Introduction Edge information from an image can be used to measure or recognize objects in images. Edges correspond to significant changes in the image, ideally at the boundary between two different regions. However, false edges are often detected, and (parts of) important edges are missing. Thus, after edge d...
Stokman et al. (Fri,) studied this question.