A 3D neural network for moving microorganism extraction

Accurate detection and extraction of moving microorganisms from microscopic video streams is the first important step in biological wastewater treatment system. We propose a novel moving object extraction algorithm based on a 3D self-organizing neural network to overcome the prominent challenges in microorganism video sequences, such as error bootstrapping, dynamic background, variable motion, physical deformation and noise obscured. Firstly, we design a multilayer network topology instead of the traditional single-layer self-organizing map, which significantly improve the discrimination ability of moving objects. Secondly, new designed mechanisms related to background model initialization and adaptively update have effectively weakened the bootstrapping and ghost influences. Thirdly, we create buffer layers in neural network efficiently to resolve the dynamic background and variable motion problems. Finally, a simple Kalman predictor with constant coefficients has been constructed to tackle with the cases of microorganism being obscured or lost. Experimental results on real microscopic video sequences and comparisons with the state-of-the-art methods have demonstrated the accuracy of our proposed microorganism extraction algorithm.