A Scale-Space Medialness Transform Based on Boundary Concordance Voting

The Concordance-based Medial Axis Transform (CMAT) presented in this paper is a multiscale medial axis (MMA) algorithm that computes the medial response from grey-level boundary measures. This non-linear operator responds only to symmetric structures, overcoming the limitations of linear medial operators which create “side-lobe” responses for symmetric structures and respond to edge structures. In addition, the spatial localisation of the medial axis and the identification of object width is improved in the CMAT algorithm compared with linear algorithms. The robustness of linear medial operators to noise is preserved in our algorithm. The effectiveness of the CMAT is accredited to the concordance property described in this paper. We demonstrate the performance of this method with test figures used by other authors and medical images that are relatively complex in structure. In these complex images the benefit of the improved response of our non-linear operator is clearly visible

Edge detection and ridge detection with automatic scale selection

When extracting features from image data, the type of information that can be extracted may be strongly dependent on the scales at which the feature detectors are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. Anovel concept of a scale-space edge is introduced, defined as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. An important property of this definition is that it allows the scale levels to vary along the edge. Two specific measures of edge strength are analysed in detail. It is shown that by expressing these in terms of γ-normalized derivatives, an immediate consequence of this definition is that fine scales are selected for sharp edges (so as to reduce the shape distortions due to scale-space smoothing), whereas coarse scales are selected for diffuse edges, such that an edge model constitutes a valid abstraction of the intensity profile across the edge. With slight modifications, this idea can be used for formulating a ridge detector with automatic scale selection, having the characteristic property that the selected scales on a scale-space ridge instead reflect the width of the ridge

Textural features for fingerprint liveness detection

The main topic ofmy research during these three years concerned biometrics and in particular the Fingerprint Liveness Detection (FLD), namely the recognition of fake fingerprints. Fingerprints spoofing is a topical issue as evidenced by the release of the latest iPhone and Samsung Galaxy models with an embedded fingerprint reader as an alternative to passwords. Several videos posted on YouTube show how to violate these devices by using fake fingerprints which demonstrated how the problemof vulnerability to spoofing constitutes a threat to the existing fingerprint recognition systems. Despite the fact that many algorithms have been proposed so far, none of them showed the ability to clearly discriminate between real and fake fingertips. In my work, after a study of the state-of-the-art I paid a special attention on the so called textural algorithms. I first used the LBP (Local Binary Pattern) algorithm and then I worked on the introduction of the LPQ (Local Phase Quantization) and the BSIF (Binarized Statistical Image Features) algorithms in the FLD field. In the last two years I worked especially on what we called the “user specific” problem. In the extracted features we noticed the presence of characteristic related not only to the liveness but also to the different users. We have been able to improve the obtained results identifying and removing, at least partially, this user specific characteristic. Since 2009 the Department of Electrical and Electronic Engineering of the University of Cagliari and theDepartment of Electrical and Computer Engineering of the ClarksonUniversity have organized the Fingerprint Liveness Detection Competition (LivDet). I have been involved in the organization of both second and third editions of the Fingerprint Liveness Detection Competition (LivDet 2011 and LivDet 2013) and I am currently involved in the acquisition of live and fake fingerprint that will be inserted in three of the LivDet 2015 datasets

Automated characterisation of Deep-sea imagery using Machine Learning: implications for future conservation and mineral extraction

This thesis aimed to develop a methodology using Machine Learning (ML) techniques for the interpretation of deep-sea resources. The deep-sea hosts diverse ecosystems and valuable resources, but potential environmental implications, particularly from mining activities, necessitate effective management strategies. Detailed maps of the sea floor are therefore a necessity, yet such maps have to date only been produced based on manual interpretation which is time consuming and subjective. The study focused on assessing the potential of ML methods to map deep-sea features based on photomosaic and bathymetry data in order to take the first steps in developing an automated, objective, and time-saving technique. This thesis’s method accurately identified and classified features like chimneys at the hydrothermal vent fields, providing insights for resource interpretation and conservation. Integrating ML methods into deep-sea resource management is crucial. The methodology enhances understanding of complex techniques, such as Convolutional Neural Networks (CNN) and Object-Based Image Analysis (OBIA) to overcome a seabed characterization. Simultaneously describing the parameters utilised to achieve a meaningful classification. ML algorithms analyze large data volumes, extract patterns, and predict feature distributions, aiding targeted conservation measures and sustainable resource exploitation. The methodology successfully mapped hydrothermal chimneys in two study areas yet producer accuracies (0,7%) were higher than user accuracies (0,64%), indicating that there were other landforms that shared similar features. The methodology also helps assess potential environmental implications of future mining, supporting informed decision-making and mitigation strategies. It serves also as a foundation for future research to aim at overcoming problems related to incomplete spatial coverage, attempt to better utilize shape and spatial parameters within the OBIA refinement, try to identify more background classes for excluding them from the model, etc.Master's Thesis in Earth ScienceGEOV399MAMN-GEO

Interoperability of fingerprint sensors and matching algorithms

Biometric systems are widely deployed in governmental, military and commercial/civilian applications. There are a multitude of sensors and matching algorithms available from different vendors. This creates a competitive market for these products, which is good for the consumers but emphasizes the importance of interoperability. In fingerprint recognition, interoperability is the ability of a system to work with a diverse set of fingerprint devices. Variations induced by fingerprint sensors include image resolution, scanning area, gray levels, etc. Such variations can impact the quality of the extracted features, and cross-device matching performance. This is true even when dealing with fingerprint sensors of the same sensing technology. In this thesis, we perform a large-scale empirical study of the status of interoperability between fingerprint sensors and assess the performance consequence when interoperability is lacking. Additionally we develop a method to increase interoperability in fingerprint-based recognition systems deploying optical fingerprint sensors. A set of features to measure differences in fingerprint acquisition is designed and evaluated. Finally, different fusion schemes based on machine learning are tested end evaluated in order to exploit the designed set of features. Experimental results show that the proposed approach is able to reduce cross-device match error rates by a significant margin