Faculty of Engineering and Built Environment
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Item Hybrid motion detection system using DSP and ANN ensembles(IMECS, 2013-03) Moorgas, Kevin Emanuel; Govender, PoobalanHybrid systems are used in engineering systems and scientific applications to enhance and to improve their efficiency. This paper presents a hybrid system using digital signal processing (DSP) systems and artificial neural networks (ANN’s) for object motion detection, extraction and filtering. A summary of a DSP motion detection system is first presented and its performance is compared to that of an ANN ensemble system (AES). The two systems are then combined to form a hybrid motion detection system. Preprocessing of the hybrid system uses wavelet image compression to enhance its computational speed. During the testing of the system the efficiency of the (AES) is demonstrated as a powerful parallel processor in handling large amounts of image data.Item ANN’s vs. SVM’s for image classification(International ASET, 2012-08) Moorgas, Kevin Emanuel; Pillay, Nelendran; Governder, PoobalanIn this paper the dynamic performance of the artificial neural network is compared to the performance of a statistical method such as the support vector machine. This comparison is made with respect to an image classification application where the performance is compared with regards to generalization and robustness. Image vectors are compressed in order to reduce the dimensionality and the salient feature vectors are extracted with the principle component algorithm. The artificial neural network and the support vector machine are trained to classify images with feature vectors. A comparative analysis is made between the artificial neural network and the support vector machine with respect to robustness and generalization.Item Object motion detection, extraction and filtering using ANN ensembles(2009) Moorgas, Kevin Emanuel; Govender, PoobalanThis 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.