Browsing by Author "Kohlakala, Aviwe"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Dental implant recognition
    (Stellenbosch : Stellenbosch University, 2023-09) Kohlakala, Aviwe; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Applied Mathematics Division.
    ENGLISH ABSTRACT: Deep learning-based frameworks have recently been steadily outperforming existing state-of-the-art systems in a number of computer vision applications, but these models require a large number of training samples in order to effectively train the model parameters. Within the medical field the limited availability of training data is one of the main challenges faced when using deep learning to create practical clinical applications in medical imaging. In this dissertation a novel algorithm for generating artificial training samples from triangulated three-dimensional (3D) surface models within the context of dental implant recognition is proposed. The proposed algorithm is based on the calculation of two-dimensional (2D) parallel projections from a number of different angles of 3D volumetric representations of computer-aided design (CAD) surface models. A fully convolutional network (FCN) is subsequently trained on the artificially generated X-ray images for the purpose of automatically identifying the connection type associated with a specific dental implant in an actual X-ray image. An ensemble of image processing and deep learning-based techniques capable of distinguishing between pixels that belong to an implant from those belonging to the background in an actual X-ray image is developed. Normalisation and preprocessing techniques are subsequently applied to the segmented dental implants within the questioned actual X-ray image. The normalised dental implants are presented to the trained FCN for classification purposes. Experiments are conducted on two data sets that contain the simulated and actual X-ray images in order to gauge the proficiency of the proposed systems. Given the fact that the novel systems proposed in this study utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the results achieved in this study are encouraging and constitute a significant contribution to the current state of the art, especially in scenarios where the proposed systems are combined with existing systems.
  • Thumbnail Image
    Item
    Ear-based biometric authentication
    (Stellenbosch : Stellenbosch University, 2019-04) Kohlakala, Aviwe; Coetzer, Johannes; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Applied Mathematics.
    ENGLISH ABSTRACT : In this thesis novel semi-automated and fully automated ear-based biometric authentication systems are proposed. Within the context of the semiautomated system, a region of interest (ROI) that contains the entire ear shell is manually speci ed by a human operator. However, in the case of the fully automated system the ROI is automatically detected using a suitable convolutional neural network (CNN), followed by morphological post-processing. The purpose of the CNN is to classify sub-images as either foreground (part of the ear shell) or background (homogeneous skin, jewellery, or hair). Independent of the ROI-detection procedure, each grey-scale input image, in its entirety, is subjected to Gaussian smoothing, followed by edge detection through an appropriate Canny- lter, and morphological edge dilation. The detected ROI serves as a mask for retaining only those edges associated with prominent contours of the ear shell. Features are subsequently extracted from each binary contour image using the discrete Radon transform (DRT). The aforementioned features are normalised in such a way that they are translation, rotation and scale invariant. A Euclidean distance measure is employed for the purpose of feature matching. Ear-based authentication is nally achieved by constructing a ranking veri er. Exhaustive experiments are conducted on two large international datasets. It is assumed that only one reference ear is available for each individual enrolled into the system. An experimental protocol is adopted that appropriately partitions the respective datasets based on ears that belong to training, validation, ranking and evaluation individuals. It is demonstrated that the pro ciency of the novel systems developed in this thesis compares favourably to those of existing systems.