摘要：AbstractMaintaining the desired interface level between the top froth layer and the liquid layer plays an important role in achieving high recovery of products in oil sands and related process industries. As varying throughputs and downstream disturbances tend to change the interface level over time, it is an important indicator of the process behavior. In this paper, we propose an approach based on Gaussian mixture model and Markov Random Field (MRF) based unsupervised image segmentation to achieve the real-time accurate measurement of the interface. The image processing problem is solved as a Maximum a Posteriori (MAP) estimation problem employing the MRF framework and the parameters are estimated using the EM algorithm. The proposed approach is validated using the images captured from a laboratory scale equipment designed to simulate the industrial PSV interface.
关键词：KeywordsInterface levelImage segmentationMarkov random fieldEM algorithm
