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Subject: search.filters.subject.Pattern recognition systems

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    Lens distortion correction by analysing the shape of patterns in Hough transform space : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Electronics and Computer Engineering at Massey University, Manawatu, New Zealand
    (Massey University, 2018) Chang, Yuan
    Many low cost, wide angle lenses suffer from lens distortion, resulting from a radial variation in the lens magnification. As a result, straight lines, particularly those in the periphery, appear curved. The Hough transform is a commonly used linear feature detection technique within an image. In Hough transform space, straight lines and curved lines have different shapes of peaks. This thesis proposes a lens distortion correction method named SLDC based on analysing the shape of patterns in the Hough transform space. It works by reconstructing the distorted line from significant points on the smile-shaped Hough pattern. It then optimises the distortion parameter by mapping the reconstructed curved line into a straight line and minimising the RMSE. From both simulation and correcting real world images, the SLDC provides encouraging results.
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    Wavelet-based birdsong recognition for conservation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand
    (Massey University, 2017) Priyadarshani, Nirosha
    According to the International Union for the Conservation of Nature Red Data List nearly a quarter of the world's bird species are either threatened or at risk of extinction. To be able to protect endangered species, we need accurate survey methods that reliably estimate numbers and hence population trends. Acoustic monitoring is the most commonly-used method to survey birds, particularly cryptic and nocturnal species, not least because it is non-invasive, unbiased, and relatively time-effective. Unfortunately, the resulting data still have to be analysed manually. The current practice, manual spectrogram reading, is tedious, prone to bias due to observer variations, and not reproducible. While there is a large literature on automatic recognition of targeted recordings of small numbers of species, automatic analysis of long field recordings has not been well studied to date. This thesis considers this problem in detail, presenting experiments demonstrating the true efficacy of recorders in natural environments under different conditions, and then working to reduce the noise present in the recording, as well as to segment and recognise a range of New Zealand native bird species. The primary issues with field recordings are that the birds are at variable distances from the recorder, that the recordings are corrupted by many different forms of noise, that the environment affects the quality of the recorded sound, and that birdsong is often relatively rare within a recording. Thus, methods of dealing with faint calls, denoising, and effective segmentation are all needed before individual species can be recognised reliably. Experiments presented in this thesis demonstrate clearly the effects of distance and environment on recorded calls. Some of these results are unsurprising, for example an inverse square relationship with distance is largely true. Perhaps more surprising is that the height from which a call is transmitted has a signifcant effect on the recorded sound. Statistical analyses of the experiments, which demonstrate many significant environmental and sound factors, are presented. Regardless of these factors, the recordings have noise present, and removing this noise is helpful for reliable recognition. A method for denoising based on the wavelet packet decomposition is presented and demonstrated to significantly improve the quality of recordings. Following this, wavelets were also used to implement a call detection algorithm that identifies regions of the recording with calls from a target bird species. This algorithm is validated using four New Zealand native species namely Australasian bittern (Botaurus poiciloptilus), brown kiwi (Apteryx mantelli ), morepork (Ninox novaeseelandiae), and kakapo (Strigops habroptilus), but could be used for any species. The results demonstrate high recall rates and tolerate false positives when compared to human experts.
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    Informatics simulation & exploration of mobile license plate detection employing infrared, canny edge detection, binary threshold and contour detection for submission in limited light conditions : a thesis presented in partial fulfilment of the requirements for the degree of Master of Engineering in Information and Communication at Massey University, Albany, New Zealand
    (Massey University, 2011) Paul, Binny
    In modern societies, vehicle surveillance is considered as an important device that allows law enforcement to govern the society and ensure citizens’ personal safety. Over the past three decades, there have been many submissions of the research into vehicle surveillance. Such research is typically broken down into two parts: license plate recognition and character recognition. This thesis focuses on licence plate recognition. As surveillance operates 24 hours a day, 365 days a year, which includes night time or limited light conditions, there has been no compelling evidence to show a mobile licence plate recognition system with optimum performance under limited light or night time conditions. Thus, the motivation of this thesis is to ascertain evidence of performance under limited light conditions. The CMOS camera, the infrared lens filter, and the infrared light source are included in the hardware design apparatus of the hardware architecture necessary for collecting image samples of real world settings. To locate the license plate, a software algorithm is envisaged as to reduce image noise and locate license plate in the image. To minimise image noise, the pre-processing techniques applied are the Gaussian blur, pyramid decomposition and up-sampling. To locate a license plate, the two unique techniques investigated are Canny edge detection and the binary threshold. Other algorithms included in the software design are dilation used for filling gaps in edge boundaries, followed by contour sets used for identifying unique object boundaries and simplifying boundary edges by means of the Dogual-Peucker algorithm. To sustain optimum performance, the Canny edge hysteresis threshold level was examined together with the iteration level of the binary threshold. The surveillance model was tested in four different environments, but only three were successful. The average accuracy across the three environment settings (1500 images) was 97.53%, which is above the accepted level in this industry.
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    Feature-based rapid object detection : from feature extraction to parallelisation : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Sciences at Massey University, Auckland, New Zealand
    (Massey University, 2007) Barczak, Andre Luis Chautard
    This thesis studies rapid object detection, focusing on feature-based methods. Firstly, modifications of training and detection of the Viola-Jones method are made to improve performance and overcome some of the current limitations such as rotation, occlusion and articulation. New classifiers produced by training and by converting existing classifiers are tested in face detection and hand detection. Secondly, the nature of invariant features in terms of the computational complexity, discrimination power and invariance to rotation and scaling are discussed. A new feature extraction method called Concentric Discs Moment Invariants (CDMI) is developed based on moment invariants and summed-area tables. The dimensionality of this set of features can be increased by using additional concentric discs, rather than using higher order moments. The CDMI set has useful properties, such as speed, rotation invariance, scaling invariance, and rapid contrast stretching can be easily implemented. The results of experiments with face detection shows a clear improvement in accuracy and performance of the CDMI method compared to the standard moment invariants method. Both the CDMI and its variant, using central moments from concentric squares, are used to assess the strength of the method applied to hand-written digits recognition. Finally, the parallelisation of the detection algorithm is discussed. A new model for the specific case of the Viola-Jones method is proposed and tested experimentally. This model takes advantage of the structure of classifiers and of the multi-resolution approach associated with the detection method. The model shows that high speedups can be achieved by broadcasting frames and carrying out the computation of one or more cascades in each node.