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This thesis addresses analysis, evaluation and simulation of low complexity face detection algorithms and tracking that could be used on mobile phones. Network access control using face recognition increases the user-friendliness in human-computer interaction. In order to realize a real time system implemented on handheld devices with low computing power, low complexity algorithms for face detection and face tracking are implemented. Skin color detection algorithms and face matching have low implementation complexity suitable for authentication of cellular network services. Novel approaches for reducing the complexities of these algorithms and fast implementation are introduced in this thesis. This includes a fast algorithm for face detection in video sequences, using a skin color model in the HSV (Hue-Saturation-Value) color space. It is combined with a Gaussian model of the H and S statistics and adaptive thresholds. These algorithms permit segmentation and detection of multiple faces in thumbnail images. Furthermore we evaluate and compare our results with those of a method implemented in the Chromatic Color space (YCbCr). We also test our test data on face detection method using Convolutional Neural Network architecture to study the suitability of using other approaches besides skin color as the basic feature for face detection. Finally, face tracking is done in 2D color video streams using HSV as the histogram color space. The program is used to compute 3D trajectories for a remote surveillance system.

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This thesis presents a number of modifications to a blind aid, known as the video optophone, which enables a blind user to more readily interpret their local environment for enhanced mobility and navigation. Versions of this form of blind aid are generally both difficult to use and interpret, and are therefore inadequate for safe mobility. The reason for this severe problem lies in the complexity and excessive bandwidth of the optophonic output after the conversion from scene-to-sound. The work herein describes a number of modifications that can be applied to the current optophonic process to make more efficient use of the limited bandwidth provided by the auditory system when converting scene images to sound. Various image processing and stereo techniques have been employed to artificially emulate the human visual system through the use of depth maps that successfully fade out the quantity of relatively unimportant image features, whilst emphasising the more significant regions such as nearby obstacles. A series of experiments were designed to test these various modifications to the optophonic mapping by studying important factors of mobility and subject response whilst going about everyday life. The devised system, labelled DeLIA for the Detection, Location, Identification, and Avoidance (or Action) of obstacles, provided a means for gathering statistical data on users’ interpretation of the optophonic output. An analysis of this data demonstrated a significant improvement when using the stereo cartooning technique, developed as part of this work, over the more conventional plain image as an input to an optophonic mapping from scene-to-sound. Lastly, conclusions were drawn from the results, which indicated that the use of a stereo depth map as an input to a video optophone would improve its usefulness as an aid to general mobility. For the purposes of detecting and determining text or similar detail, either a plain unmodified image or some form of edge (depth) image were found to produce the best results

Due to the rapid growth in information handling and transmission, there is a serious demand for more efficient data compression schemes. compression schemes address themselves to speech, visual and alphanumeric coded data. This thesis is concerned with the compression of visual data given in the form of still or moving pictures. such data is highly correlated spatially and in the context domain. A detailed study of some existing data compression systems is presented, in particular, the performance of DPCM was analysed by computer simulation, and the results examined both subjectively and objectively. The adaptive form of the prediction encoder is discussed and two new algorithms proposed, which increase the definition of the compressed image and reduce the overall mean square error. Two novel systems are proposed for image compression. The first is a bit plane image coding system based on a hierarchic quadtree structure in a transmission domain, using the Hadamard transform as a kernel. Good compression has been achieved from this scheme, particularly for images with low detail. The second scheme uses a learning automata to predict the probability distribution of the grey levels of an image related to its spatial context and position. An optimal reward/punishment function is proposed such that the automata converges to its steady state within 4000 iterations • such a high speed of convergence together with Huffman coding results in efficient compression for images and is shown to be applicable to other types of data. . The performance and evaluation of all the proposed .'systems have been tested by computer simulation and the results presented both quantitatively and qualitatively."The advantages and disadvantages of each system are discussed and suggestions for improvement. given.

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