An Automated Microbial Detection System for Monitoring Water Pollution D. Bajpai, S. Radhakrishnan and M. Das Introduction Image Sequence From the Microscope Current Frame Previous Frame • This research proposes a technique for detection of various micro-organisms found in polluted water. • High resolution video sequences of microscopic images are acquired and processed to identify water borne micro-organisms and estimate their concentrations. Motion Detection Current Frame Current Difference Image • The concentrations of various micro-organisms at a given instant of time can be related to the quality of the water. Image Denoising System Block Diagram-1 Properties of the Images • Very microscopic non-rigid, irregular shaped organisms which are swimming in static clutter. • The organisms are translucent and change shape while in motion. System Block Diagram-2 System Block Diagram-4 System Block Diagram-3 Position Estimates Current Difference Image Tracking Using IMM • Very strong background noise arising from light scattered by colloidal particles. Feature Extraction Using PCA Block Matching Technique in Hartley Domain Motion Classification using HMM Position Estimation Block Diagram A Previous Difference Image H B G Types of Microorganisms Image Acquisition Image acquired using a data translation frame grabber DT 3155. Legends: A: Tank containing polluted water. B: Tank containing clean water for flushing the tubes. C, D, E: Computer controlled solenoids, and junction box F: Transparent Cuvette to view the polluted sample under the microscope. G: Microscope. H: CCD camera connected to the PC. I: PC containing the frame grabber card. J: Waste tank. • Development of a new technique for classification of micro-organisms based on their motion characteristics. • Development of a new technique for position estimation based on the phase shift property of the discrete Hartley transform (DHT). • Tracking of micro-organisms using the interactive motion models (IMM). • Feature extraction from the track data and estimation of model mixture probabilities using Principal Component Analysis (PCA). • Motion Classification using Hidden Markov Models (HMM). • Automated real-time monitoring. D J Tanks Solenoids Research Objectives F Camera Block size chosen as 16 x 16. E System Setup Captured images size 640 X 480. Frame acquisition rate 10 – 16 frames per second. C I Electronic Controls Microscope v Development of micro-organism detection systems based on either shape or motion characteristics has been completed. On-going research includes: v Development of a detection system using a combination of shape, color and motion characteristics. v A comparative study of various methods. v Development of an automated system for real-time monitoring of water-borne micro-organisms and on-site evaluation. v Extension of the above methods to monitoring other kinds of pollution.
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