A consensus-constrained parsimonious Gaussian mixture model for clustering hyperspectral images

arXiv:2403.03349v1 Announce Type: cross
Abstract: The use of hyperspectral imaging to investigate food samples has grown due to the improved performance and lower cost of spectroscopy instrumentation. Food engineers use hyperspectral images to classify the type and quality of a food sample, typically using classification methods. In order to train these methods, every pixel in each training image needs to be labelled. Typically, computationally cheap threshold-based approaches are used to label the pixels, and classification methods are trained based on …

abstract arxiv classification clustering consensus cost cs.cv eess.iv engineers food images imaging instrumentation performance quality sample samples spectroscopy stat.me train type