Number Plate Detection (NPD) algorithm.
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Automatic Number Plate Recognition (ANPR) is an important Intelligent Transportation System (ITS) technology, which distinguishes each vehicle as unique by recognising the characters in their number plates via image analysis and pattern recognition techniques. In an ANPR system, the most crucial part is number plate detection. The research presented in this thesis focuses on the detection mechanism and will rely on a third-party Optical Character Recognition (OCR) software for character recognition. Number Plate Detection (NPD) is a well-explored problem with many successful solutions. Although most of these solutions are reasonably fast and robust, they can be further improved to make them even faster and more robust to deal with various complex conditions in real-time. This thesis first presents a region-based NPD algorithm, which provides much more accurate detection results than previous NPD algorithms and is robust against interference characters in images. Then, a fast and robust edge-based NPD algorithm is developed. Tins algorithm can detect various number plates under various conditions in real-time with a high detection rate and a very low false positive rate. Similar work has not been reported elsewhere. Besides character information, the colour information of number plates also plays an important role in identifying each number plate as unique. Hence, this thesis also develops algorithms for classifying number plate colours. Histogram-based image matching methods are investigated, and a Gaussian Weighted Histogram Intersection (GWHI) algorithm is presented. This algorithm is shown to be much more robust against various colour variations than previous methods. Furthermore, a novel Colour Edge Co-occurrence Histogram (CECH) method is presented. It is shown to be particularly applicable for rapidly matching compound objects, such as number plates. Finally, histogram-based image matching technique on a hexagonal image structure is investigated. Gevers' idea of using Colour Ratio Gradient (CRG) for robust object matching is redefined on hexagonal structure, arid a novel Symmetric Colour Ratio Gradient (SCRG) method is developed. Experimental results demonstrate that the proposed SCRG method outperforms the Gevers’ CRG method. More contributions can be found in the appendices. A new virtual hexagonal structure is proposed, on which the time used for mapping a square-based image to hexagon-based image is dramatically reduced. Two basic image transformation operations and a novel edge detection algorithm are performed on the new structure. The results obtained in this thesis can also be applied to many other areas such as Character Detection, Text Detection, and Image/Video Retrieval
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