Browsing by Author "Fick, Carlien"

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    Modifying and generalising the Radon transform for improved curve-sensitive feature extraction
    (Stellenbosch : Stellenbosch University, 2017-12-01) Fick, Carlien; Coetzer, Johannes; Swanepoel, Jacques Philip; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    ENGLISH ABSTRACT : In this thesis a novel and generic feature extraction protocol that is based on the well-known standard discrete Radon transform (SDRT) is presented. The SDRT is traditionally associated with computerised tomography and involves the calculation of projection profiles of an image from a finite set of angles. Although the SDRT has already been successfully employed for the purpose of feature extraction, it is limited to the detection of straight lines. The proposed feature extraction protocol is based on modifications to the SDRT that facilitate the detection of not only straight lines, but also curved lines (with various curvatures), as well as textural information. This is made possible by first constructing a novel appropriately normalised multiresolution polar transform (MPT) of the image in question. The origin of said MPT may be adjusted according to the type of features targeted. The SDRT, or the novel modified discrete Radon transform (MDRT) conceptualised in this thesis, is subsequently applied to the MPT. The extraction of textural information based on different textural periodicities is facilitated by considering different projection angles associated with the MDRT, while the extraction of textural information based on different textural orientations is facilitated by specifying different origins for the MPT. The extraction of information pertaining to curved lines is made possible by specifying origins for the MPT that are located at different distances from the edge of the image in question – the SDRT is subsequently applied to a given MPT from a specific angle of 90 . An existing system that only employs SDRT-based features constitutes a benchmark. Two novel texture-based systems, that target different textural periodicities and orientations respectively, are developed. A novel system, that constitutes a generalisation of the SDRT-based benchmark, and is geared towards the detection of different curved lines, is also developed. The proficiency of the proposed systems is gauged by considering a data set that contains authentic handwritten signature images and skilled forgeries associated with 51 writers. All of the proposed systems outperform the SDRTbased benchmark. The improvement in proficiency associated with each individual texture-based system is statistically significant. The proficiency of the proposed systems also compares favourably with that of existing state-of-theart systems within the context of offline signature verification.