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In this study, we propose a two-stage method for material segmentation in hyperspectral images. The first stage employs a Convolutional Neural Network (CNN) to predict the material label at individual pixels. The second stage further refines the segmentation by a fully-connected Conditional Random Field (CRF) framework. For the first stage, we experimented with two different network architectures...
We propose a supervised approach to the classification and segmentation of material regions in hyperspectral imagery. Our algorithm is a two-stage process, combining a pixelwise classification step with a segmentation step aiming to minimise the total perimeters of the resulting regions. Our algorithm is distinctive in its ability to ensure label consistency within local homogeneous areas and to generate...
In this paper we present a method to automatically control the exposure of multispectral cameras. To this end, we use an input image taken using camera pre-sets. This pre-set exposure time and the input image are then used to recover an updated exposure time making use of the spectral power. This yields a setting where the spectral power is computed using a photopic function. Moreover, the exposure...
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