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) 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. Semi-automated and fully automated systems are proposed for segmenting questioned dental implants from the background in actual X-ray images. Within the context of the semi-automated system, suitable regions of interest (ROIs), which contain the dental implants, are manually specified. However, as part of the fully automated system, suitable ROIs are automatically detected. It is demonstrated that a segmentation/detection accuracy of 94.0% and a classification/recognition accuracy of 71.7% are attainable within the context of the proposed fully automated system. Since the proposed systems utilise an ensemble of techniques that has not been employed for the purpose of dental implant classification/recognition on any previous occasion, the above-mentioned results are very encouraging. Graphical abstract

In this paper novel semi-automated and fully automated ear-based biometric authentication systems are proposed. The region of interest (ROI) is manually specified and automatically detected within the context of the semi-automated and fully automated systems, respectively. The automatic detection of the ROI is facilitated by a convolutional neural network (CNN) and morphological postprocessing. The CNN classifies sub-images of the ear in question as either foreground (part of the ear shell) or background (homogeneous skin, hair or jewellery). Prominent contours associated with the folds of the ear shell are detected within the ROI. The discrete Radon transform (DRT) is subsequently applied to the resulting binary contour image for the purpose of feature extraction. Feature matching is achieved by implementing an Euclidean distance measure. A ranking verifier is constructed for the purpose of authentication. In this study experiments are conducted on two independent ear databases, that is (1) the Mathematical Analysis of Images (AMI) ear database and (2) the Indian Institute of Technology (IIT) Delhi ear database. The results are encouraging. Within the context of the proposed semi-automated system, accuracies of 99.20% and 96.06% are reported for the AMI and IIT Delhi ear databases respectively.

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