21 February 1996 Method for multispectral images segmentation in case of partially available spectral characteristics of objects
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Abstract
The method presented in this paper aims at finding objects by segmentation of a multi-spectral (color) image in cases of incomplete knowledge of spectral features of objects' types. The method does not require probability densities of all classes to be known, and at the same time does incorporate the information available. The segmentation is performed in 2 stages. First, a non-parametric extended kNN classification algorithm is applied. It provides estimations of the a posteriori probabilities of every class, including the unknown one (which actually consists of all unknown beforehand classes), for every pixel of the image. The second stage results in a segmented image, containing both objects with spectral characteristics known in advance and objects that composed the unknown class at the first stage. It is obtained by an extended region merging algorithm, where the merging criterium combines a posteriori probability estimates from the first stage with similarity/homogeneity of spectral feature vectors. The method is especially useful when the inspection of big and largely 'unknown' streams of objects must be performed. The example considered in the paper concerns segmentation of real images of printed circuit boards in order to find different electronic components.
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Natasha Gorte-Kroupnova, Ben G.H. Gorte, "Method for multispectral images segmentation in case of partially available spectral characteristics of objects", Proc. SPIE 2665, Machine Vision Applications in Industrial Inspection IV, (21 February 1996); doi: 10.1117/12.232242; https://doi.org/10.1117/12.232242
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