Fingerprint Recognition: Enhancement, Feature Extraction and Automatic Evaluation of Algorithms

The identification of people by measuring some traits of individual anatomy or physiology has led to a specific research area called biometric recognition. This thesis is focused on improving fingerprint recognition systems considering three important problems: fingerprint enhancement, fingerprint orientation extraction and automatic evaluation of fingerprint algorithms. An effective extraction of salient fingerprint features depends on the quality of the input fingerprint. If the fingerprint is very noisy, we are not able to detect a reliable set of features. A new fingerprint enhancement method, which is both iterative and contextual, is proposed. This approach detects high-quality regions in fingerprints, selectively applies contextual filtering and iteratively expands like wildfire toward low-quality ones. A precise estimation of the orientation field would greatly simplify the estimation of other fingerprint features (singular points, minutiae) and improve the performance of a fingerprint recognition system. The fingerprint orientation extraction is improved following two directions. First, after the introduction of a new taxonomy of fingerprint orientation extraction methods, several variants of baseline methods are implemented and, pointing out the role of pre- and post- processing, we show how to improve the extraction. Second, the introduction of a new hybrid orientation extraction method, which follows an adaptive scheme, allows to improve significantly the orientation extraction in noisy fingerprints. Scientific papers typically propose recognition systems that integrate many modules and therefore an automatic evaluation of fingerprint algorithms is needed to isolate the contributions that determine an actual progress in the state-of-the-art. The lack of a publicly available framework to compare fingerprint orientation extraction algorithms, motivates the introduction of a new benchmark area called FOE (including fingerprints and manually-marked orientation ground-truth) along with fingerprint matching benchmarks in the FVC-onGoing framework. The success of such framework is discussed by providing relevant statistics: more than 1450 algorithms submitted and two international competitions.

Computational modelling of human stem cell-derived cardiomyocytes - Texture descriptors for biological image processing

This thesis investigates two distinct research topics. The main topic (Part I) is the computational modelling of cardiomyocytes derived from human stem cells, both embryonic (hESC-CM) and induced-pluripotent (hiPSC-CM). The aim of this research line lies in developing models of the electrophysiology of hESC-CM and hiPSC-CM in order to integrate the available experimental data and getting in-silico models to be used for studying/making new hypotheses/planning experiments on aspects not fully understood yet, such as the maturation process, the functionality of the Ca2+ hangling or why the hESC-CM/hiPSC-CM action potentials (APs) show some differences with respect to APs from adult cardiomyocytes. Chapter I.1 introduces the main concepts about hESC-CMs/hiPSC-CMs, the cardiac AP, and computational modelling. Chapter I.2 presents the hESC-CM AP model, able to simulate the maturation process through two developmental stages, Early and Late, based on experimental and literature data. Chapter I.3 describes the hiPSC-CM AP model, able to simulate the ventricular-like and atrial-like phenotypes. This model was used to assess which currents are responsible for the differences between the ventricular-like AP and the adult ventricular AP. The secondary topic (Part II) consists in the study of texture descriptors for biological image processing. Chapter II.1 provides an overview on important texture descriptors such as Local Binary Pattern or Local Phase Quantization. Moreover the non-binary coding and the multi-threshold approach are here introduced. Chapter II.2 shows that the non-binary coding and the multi-threshold approach improve the classification performance of cellular/sub-cellular part images, taken from six datasets. Chapter II.3 describes the case study of the classification of indirect immunofluorescence images of HEp2 cells, used for the antinuclear antibody clinical test. Finally the general conclusions are reported.

Software tools and efficient algorithms for the feature detection, feature tracking, event localization, and visualization of large sets of atmospheric data

Data sets describing the state of the earth's atmosphere are of great importance in the atmospheric sciences. Over the last decades, the quality and sheer amount of the available data increased significantly, resulting in a rising demand for new tools capable of handling and analysing these large, multidimensional sets of atmospheric data. The interdisciplinary work presented in this thesis covers the development and the application of practical software tools and efficient algorithms from the field of computer science, aiming at the goal of enabling atmospheric scientists to analyse and to gain new insights from these large data sets. For this purpose, our tools combine novel techniques with well-established methods from different areas such as scientific visualization and data segmentation. In this thesis, three practical tools are presented. Two of these tools are software systems (Insight and IWAL) for different types of processing and interactive visualization of data, the third tool is an efficient algorithm for data segmentation implemented as part of Insight.Insight is a toolkit for the interactive, three-dimensional visualization and processing of large sets of atmospheric data, originally developed as a testing environment for the novel segmentation algorithm. It provides a dynamic system for combining at runtime data from different sources, a variety of different data processing algorithms, and several visualization techniques. Its modular architecture and flexible scripting support led to additional applications of the software, from which two examples are presented: the usage of Insight as a WMS (web map service) server, and the automatic production of a sequence of images for the visualization of cyclone simulations. The core application of Insight is the provision of the novel segmentation algorithm for the efficient detection and tracking of 3D features in large sets of atmospheric data, as well as for the precise localization of the occurring genesis, lysis, merging and splitting events. Data segmentation usually leads to a significant reduction of the size of the considered data. This enables a practical visualization of the data, statistical analyses of the features and their events, and the manual or automatic detection of interesting situations for subsequent detailed investigation. The concepts of the novel algorithm, its technical realization, and several extensions for avoiding under- and over-segmentation are discussed. As example applications, this thesis covers the setup and the results of the segmentation of upper-tropospheric jet streams and cyclones as full 3D objects. Finally, IWAL is presented, which is a web application for providing an easy interactive access to meteorological data visualizations, primarily aimed at students. As a web application, the needs to retrieve all input data sets and to install and handle complex visualization tools on a local machine are avoided. The main challenge in the provision of customizable visualizations to large numbers of simultaneous users was to find an acceptable trade-off between the available visualization options and the performance of the application. Besides the implementational details, benchmarks and the results of a user survey are presented.

Effiziente und robuste, feature-basierte Wiedererkennung langer Zeitreihen in großen Datenbanken

Die Materialverfolgung gewinnt in der Metallindustrie immer mehr an Bedeutung:rnEs ist notwendig, dass ein Metallband im Fertigungsprozess ein festgelegtes Programm durchläuft - erst dann ist die Qualität des Endprodukts garantiert. Die bisherige Praxis besteht darin, jedem Metallband eine Nummer zuzuordnen, mit der dieses Band beschriftet wird. Bei einer tagelangen Lagerung der Bänder zwischen zwei Produktionsschritten erweist sich diese Methode als fehleranfällig: Die Beschriftungen können z.B. verloren gehen, verwechselt, falsch ausgelesen oder unleserlich werden. 2007 meldete die iba AG das Patent zur Identifikation der Metallbänder anhand ihres Dickenprofils an (Anhaus [3]) - damit kann die Identität des Metallbandes zweifelsfrei nachgewiesen werden, eine zuverlässige Materialverfolgung wurde möglich.Es stellte sich jedoch heraus, dass die messfehlerbehafteten Dickenprofile, die als lange Zeitreihen aufgefasst werden können, mit Hilfe von bisherigen Verfahren (z.B. L2-Abstandsminimierung oder Dynamic Time Warping) nicht erfolgreich verglichen werden können.Diese Arbeit stellt einen effizienten feature-basierten Algorithmus zum Vergleichrnzweier Zeitreihen vor. Er ist sowohl robust gegenüber Rauschen und Messausfällen als auch invariant gegenüber solchen Koordinatentransformationen der Zeitreihen wie Skalierung und Translation. Des Weiteren sind auch Vergleiche mit Teilzeitreihen möglich. Unser Framework zeichnet sich sowohl durch seine hohe Genauigkeit als auch durch seine hohe Geschwindigkeit aus: Mehr als 99.5% der Anfragen an unsere aus realen Profilen bestehende Testdatenbank werden richtig beantwortet. Mit mehreren hundert Zeitreihen-Vergleichen pro Sekunde ist es etwa um den Faktor 10 schneller als die auf dem Gebiet der Zeitreihenanalyse etablierten Verfahren, die jedoch nicht im Stande sind, mehr als 90% der Anfragen korrekt zu verarbeiten. Der Algorithmus hat sich als industrietauglich erwiesen. Die iba AG setzt ihn in einem weltweit einzigartigen dickenprofilbasierten Überwachungssystemrnzur Materialverfolgung ein, das in ersten Stahl- und Aluminiumwalzwerkenrnbereits erfolgreich zum Einsatz kommt.

Costificazione delle matrici per estrusione mediante approccio "feature-based cost".

In questo elaborato viene presentato un nuovo modello di costo per le matrici per estrusione basato su un approccio feature-based. Nel particolare si è cercato di definire il costo di questi prodotti sulla base delle loro caratteristiche geometriche e tecnologiche.

Visualization and validation of (Q)SAR models

Analyzing and modeling relationships between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects in chemical datasets is a challenging task for scientific researchers in the field of cheminformatics. Therefore, (Q)SAR model validation is essential to ensure future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to approve its use in real-world scenarios as an alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model is still under discussion. In this work, we empirically compare a k-fold cross-validation with external test set validation. The introduced workflow allows to apply the built and validated models to large amounts of unseen data, and to compare the performance of the different validation approaches. Our experimental results indicate that cross-validation produces (Q)SAR models with higher predictivity than external test set validation and reduces the variance of the results. Statistical validation is important to evaluate the performance of (Q)SAR models, but does not support the user in better understanding the properties of the model or the underlying correlations. We present the 3D molecular viewer CheS-Mapper (Chemical Space Mapper) that arranges compounds in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kinds of features, like structural fragments as well as quantitative chemical descriptors. Comprehensive functionalities including clustering, alignment of compounds according to their 3D structure, and feature highlighting aid the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. Even though visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allows for the investigation of model validation results are still lacking. We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. New functionalities in CheS-Mapper 2.0 facilitate the analysis of (Q)SAR information and allow the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. Our approach reveals if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.

Feature-invariant image registration method for quantification of surgical outcomes in patients with craniosynostosis: a preliminary study

Craniosynostosis consists of a premature fusion of the sutures in an infant skull that restricts skull and brain growth. During the last decades, there has been a rapid increase of fundamentally diverse surgical treatment methods. At present, the surgical outcome has been assessed using global variables such as cephalic index, head circumference, and intracranial volume. However, these variables have failed in describing the local deformations and morphological changes that may have a role in the neurologic disorders observed in the patients. This report describes a rigid image registration-based method to evaluate outcomes of craniosynostosis surgical treatments, local quantification of head growth, and indirect intracranial volume change measurements. The developed semiautomatic analysis method was applied to computed tomography data sets of a 5-month-old boy with sagittal craniosynostosis who underwent expansion of the posterior skull with cranioplasty. Quantification of the local changes between pre- and postoperative images was quantified by mapping the minimum distance of individual points from the preoperative to the postoperative surface meshes, and indirect intracranial volume changes were estimated. The proposed methodology can provide the surgeon a tool for the quantitative evaluation of surgical procedures and detection of abnormalities of the infant skull and its development.

Feature Versus Spatial Strategies by Orangutans (Pongo abelii) and Human Children (Homo sapiens) in a Cross-Dimensional Task

Despite the fact that photographic stimuli are used across experimental contexts with both human and nonhuman subjects, the nature of individuals' perceptions of these stimuli is still not well understood. In the present experiments, we tested whether three orangutans and 36 human children could use photographic information presented on a computer screen to solve a perceptually corresponding problem in the physical domain. Furthermore, we tested the cues that aided in this process by pitting featural information against spatial position in a series of probe trials. We found that many of the children and one orangutan were successfully able to use the information cross-dimensionally; however, the other two orangutans and almost a quarter of the children failed to acquire the task. Species differences emerged with respect to ease of task acquisition. More striking, however, were the differences in cues that participants used to solve the task: Whereas the orangutan used a spatial strategy, the majority of children used a feature one. Possible reasons for these differences are discussed from both evolutionary and developmental perspectives. The novel results found here underscore the need for further testing in this area to design appropriate experimental paradigms in future comparative research settings.

Analyzing Software Evolution through Feature Views

Features encapsulate the domain knowledge of a software system and thus are valuable sources of information for a reverse engineer. When analyzing the evolution of a system, we need to know how and which features were modified to recover both the change intention and its extent, namely which source artifacts are affected. Typically, the implementation of a feature crosscuts a number of source artifacts. To obtain a mapping between features to the source artifacts, we exercise the features and capture their execution traces. However this results in large traces that are difficult to interpret. To tackle this issue we compact the traces into simple sets of source artifacts that participate in a feature's runtime behavior. We refer to these compacted traces as feature views. Within a feature view, we partition the source artifacts into disjoint sets of characterized software entities. The characterization defines the level of participation of a source entity in the features. We then analyze the features over several versions of a system and we plot their evolution to reveal how and hich features were affected by changes in the code. We show the usefulness of our approach by applying it to a case study where we address the problem of merging parallel development tracks of the same system.