Recognition of Waterborne Microorganisms by their Motion Characteristics Using an IMM Estimator

摘要

Traditional techniques of microbial density estimation involve analysis of microbes from still images of water samples. However, microorganisms are non-rigid objects that swim in polluted water and are surrounded by static clutter and debris. Many of these microorganisms have unique motion characteristics and this paper proposes a technique for microorganisms classification based on their motion characteristics. Motion detection is carried out on pre-processed image sequences obtained from a microscope mounted CCD camera. A block matching technique in the frequency domain is proposed which can detect microorganism motion by using the phase shift property of the Discrete Hartley Transform (DHT). Position estimation of multiple objects can be performed using this technique. Tracking of the various microorganisms is achieved using the Interacting Multiple Models (IMM). The position estimates and the model mixing properties are used as an input to the IMM tracker. Three models are used to define the motion characteristics of the various types of microorganisms. Multiple microorganisms can be tracked using this technique, and is more reliable in recognition of the track characteristics than using a bank of 'non-interacting' single model-based filters. The track characteristic and the mode probabilities are unique properties of a particular organism. The track data obtained from the IMM and the mixing probabilities of the various models used in the track are used as a feature set. Principal Component Analysis (PCA) is used for feature extraction. Classification is to be carried out using the Hidden Markov Models (HMM). The performance of the proposed technique is evaluated using simulated image sequences and actual images obtained from the field.