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Survivability is the primary problem in wireless visual sensor networks (WVSNs) when they are utilized for sewage monitoring with limited resources. In this paper, we propose a low-cost data prioritization framework for sewage microorganism microscopic images to overcome this frangibility of WVSNs. Firstly, an improved fast visual background extraction (Vibe) algorithm is proposed to rapidly detect...
Due to the very low contrast between the microorganisms and background in sewage microscopic images, few traditional segmentation algorithms are available to extract the object contours from surrounding environment completely and smoothly. In this paper, we propose a novel contour extraction method for microorganisms in sewage micro-images. Firstly, Sobel operator is utilized to calculate the image...
In this paper, a new image sequence model, obtained by learning in a multilayer self-organizing neural network, is proposed for moving microorganism detection in sewage treatment system. The model is able to handle diverse challenging scenarios accurately, such as dynamic background, gradual illumination variations, shadows cast and so on, which are robust against false detections for different types...
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