2 research outputs found

    Automatic fingerprint classification scheme using template matching with new set of singular point-based features

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    Fingerprint classification is a technique used to assign fingerprints into five established classes namely Whorl, Left loop, Right loop, Arch and Tented Arch based on their ridge structures and singular points’ trait. Although some progresses have been made thus far to improve accuracy rates, problem arises from ambiguous fingerprints is far from over, especially in large intra-class and small inter-class variations. Poor quality images including blur, dry, wet, low-contrast, cut, scarred and smudgy, are equally challenging. Thus, this thesis proposes a new classification technique based on template matching using fingerprint salient features as a matching tool. Basically, the methodology covers five main phases: enhancement, segmentation, orientation field estimation, singular point detection and classification. In the first phase, it begins with greyscale normalization, followed by histogram equalization, binarization, skeletonization and ends with image fusion, which eventually produces high quality images with clear ridge flows. Then, at the beginning of the second phase, the image is partitioned into 16x16 pixels blocks - for each block, local threshold is calculated using its mean, variance and coherence. This threshold is then used to extract a foreground. Later, the foreground is enhanced using a newly developed filling-in-the-gap process. As for the third phase, a new mask called Epicycloid filter is applied on the foreground to create true-angle orientation fields. They are then grouped together to form four distinct homogenous regions using a region growing technique. In the fourth phase, the homogenous areas are first converted into character-based regions. Next, a set of rules is applied on them to extract singular points. Lastly, at the classification phase, basing on singular points’ occurrence and location along to a symmetric axis, a new set of fingerprint features is created. Subsequently, a set of five templates in which each one of them represents a specific true class is generated. Finally, classification is performed by calculating a similarity between the query fingerprint image and the template images using x2 distance measure. The performance of the current method is evaluated in terms of accuracy using all 27,000 fingerprint images acquired from The National Institute of Standard and Technology (NIST) Special Database 14, which is de facto dataset for development and testing of fingerprint classification systems. The experimental results are very encouraging with accuracy rate of 93.05% that markedly outpaced the renowned researchers’ latest works

    Optical Character Recognition Using Morphological Attributes.

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    This dissertation addresses a fundamental computational strategy in image processing hand written English characters using traditional parallel computers. Image acquisition and processing is becoming a thriving industry because of the frequent availability of fax machines, video digitizers, flat-bed scanners, hand scanners, color scanners, and other image input devices that are now accessible to everyone. Optical Character Recognition (OCR) research increased as the technology for a robust OCR system became realistic. There is no commercial effective recognition system that is able to translate raw digital images of hand written text into pure ASCII. The reason is that a digital image comprises of a vast number of pixels. The traditional approach of processing the huge collection of pixel information is quite slow and cumbersome. In this dissertation we developed an approach and theory for a fast robust OCR system for images of hand written characters using morphological attribute features that are expected by the alphabet character set. By extracting specific morphological attributes from the scanned image, the dynamic OCR system is able to generalize and approximate similar images. This generalization is achieved with the usage of fuzzy logic and neural network. Since the main requirement for a commercially effective OCR is a fast and a high recognition rate system, the approach taken in this research is to shift the recognition computation into the system\u27s architecture and its learning phase. The recognition process constituted mainly simple integer computation, a preferred computation on digital computers. In essence, the system maintains the attribute envelope boundary upon which each English character could fall under. This boundary is based on extreme attributes extracted from images introduced to the system beforehand. The theory was implemented both on a SIMD-MC\sp2 and a SISD machine. The resultant system proved to be a fast robust dynamic system, given that a suitable learning had taken place. The principle contributions of this dissertation are: (1) Improving existing thinning algorithms for image preprocessing. (2) Development of an on-line cluster partitioning procedure for region oriented segmentation. (3) Expansion of a fuzzy knowledge base theory to maintain morphological attributes on digital computers. (4) Dynamic Fuzzy learning/recognition technique
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