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Faculty of Engineering and Built Environment

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Author: search.filters.author.Moorgas, Kevin EmanuelSubject: search.filters.subject.Artificial neural network

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    Hybrid motion detection system using DSP and ANN ensembles
    (IMECS, 2013-03) Moorgas, Kevin Emanuel; Govender, Poobalan
    Hybrid 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.
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    ANN’s vs. SVM’s for image classification
    (International ASET, 2012-08) Moorgas, Kevin Emanuel; Pillay, Nelendran; Governder, Poobalan
    In 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.