Please use this identifier to cite or link to this item: http://hdl.handle.net/10321/557
Title: Object motion detection, extraction and filtering using ANN ensembles
Authors: Moorgas, Kevin Emanuel
Issue Date: 2009
Abstract: This research is devoted to the development of an intelligent image motion detection system based on artificial neural networks (ANN’s). Object motion detection, non-stationary image isolation and extraction, and image filtering is investigated, with the intention of developing a system that will overcome some of the shortcomings associated with the performance of conventional motion detection systems. Motion detection and image extraction finds popular application in medical imagery and engineering based diagnostics systems. Conventional image processing systems utilise Digital Signal Processing (DSP) to perform the non-stationary image motion detection function. Aliasing and filtering are problematic processes in DSP based image processing systems. The proposed ANN motion detection system overcomes some of these shortcomings. The study compares the performance of conventional DSP systems to that of the proposed ANN based system. The excellent noise immunity, ability to generalise and robustness of the ANN system is exploited in the design of the motion detection system. The ANN’s are arranged as ensembles in order to improve the computation time of the proposed motion detection system. A hybrid system comprising DSP and ANN ensembles is also proposed in the study. The hybrid system exploits the positive characteristics of DSP and ANN’s within a single system. The performance of the pure ANN system and the hybrid system is compared to that of DSP systems, using the image’s signal-to-noise ratio and computation times as a basis for comparison.
Description: Thesis submitted in compliance with the requirements for the Master's Degree of Technology: Electrical Engineering - Light Current, Durban University of Technology, 2009.
URI: http://hdl.handle.net/10321/557
Appears in Collections:Theses and dissertations (Engineering and Built Environment)

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