Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Digital Image Processing

This book presents several recent advances that are related or fall under the umbrella of 'digital image processing', with the purpose of providing an insight into the possibilities offered by digital image processing algorithms in various fields. The presented mathematical algorithms are accompanied by graphical representations and illustrative examples for an enhanced readability. The chapters are written in a manner that allows even a reader with basic experience and knowledge in the digital image processing field to properly understand the presented algorithms. Concurrently, the structure of the information in this book is such that fellow scientists will be able to use it to push the development of the presented subjects even further

The rectification and recognition of document images with perspective and geometric distortions

Ph.DDOCTOR OF PHILOSOPH

Application of Radial Distribution Functions to Diffraction and Imaging Data: Interfacial Structures, Amorphous, Disordered Materials

The central theme of this thesis is the application of radial and pair distribution function analysis to materials characterisation problems for nanotechnology. These concepts are introduced in Chapter 1, and the associated methods are described in Chapter 2. Chapter 3 details the first of the results which discusses the design and development of a software tool called ImageDataExtractor. This auto-extracts microscopy images and then analyses them to afford quantitative information regarding particles in a sample, such as shape, size and distribution. It realises an opportunity for data-mining the ubiquity of readily available images in the literature. Chapter 4 presents results of the development and execution of a novel experimental technique, called glancing-angle pair distribution function (gaPDF) analysis, applied to the structure of the working electrode in dye-sensitised solar cells (DSSCs). This structure was successfully observed, validating this novel method. The investigation also suggested preferred binding modes of the carboxylic acid anchoring groups present in this interfacial structure. Chapters 5 and 6 demonstrate the application of PDF analysis to synchrotron-based powder diffraction data of two material case studies: the rare earth phosphate glass (REPG) (Gd2O3)0.230(P2O5)0.770, and four Ru based photo-isomers. The closest R…R rare earth separation, which governs optical properties of REPGs, was determined to be 4.2(1) Å, aided by various statistical techniques. Analysis on four Ru-based photo-isomers confirmed: the existence of local structure in such compounds, their ability to be photo-isomerised in powder form, the theoretical models constructed using computational techniques, and the lack of heterogeneity in photo-isomerisation throughout a given light-induced sample. Chapter 7 concludes the work and offers a future outlook

Detection and identification of elliptical structure arrangements in images: theory and algorithms

Cette thèse porte sur différentes problématiques liées à la détection, l'ajustement et l'identification de structures elliptiques en images. Nous plaçons la détection de primitives géométriques dans le cadre statistique des méthodes a contrario afin d'obtenir un détecteur de segments de droites et d'arcs circulaires/elliptiques sans paramètres et capable de contrôler le nombre de fausses détections. Pour améliorer la précision des primitives détectées, une technique analytique simple d'ajustement de coniques est proposée ; elle combine la distance algébrique et l'orientation du gradient. L'identification d'une configuration de cercles coplanaires en images par une signature discriminante demande normalement la rectification Euclidienne du plan contenant les cercles. Nous proposons une technique efficace de calcul de la signature qui s'affranchit de l'étape de rectification ; elle est fondée exclusivement sur des propriétés invariantes du plan projectif, devenant elle même projectivement invariante. ABSTRACT : This thesis deals with different aspects concerning the detection, fitting, and identification of elliptical features in digital images. We put the geometric feature detection in the a contrario statistical framework in order to obtain a combined parameter-free line segment, circular/elliptical arc detector, which controls the number of false detections. To improve the accuracy of the detected features, especially in cases of occluded circles/ellipses, a simple closed-form technique for conic fitting is introduced, which merges efficiently the algebraic distance with the gradient orientation. Identifying a configuration of coplanar circles in images through a discriminant signature usually requires the Euclidean reconstruction of the plane containing the circles. We propose an efficient signature computation method that bypasses the Euclidean reconstruction; it relies exclusively on invariant properties of the projective plane, being thus itself invariant under perspective

Driver traffic violation detection and driver risk calculation through real-time image processing

Road safety is a serious problem in many countries and affects the lives of many people. Improving road safety starts with the drivers, and the best way to make them change their habits is to offer incentives for better, safer driving styles. This project aims to make that possible by offering a means to calculate a quantified indicator of how safe a driver's habits are. This is done by developing an on-board, visual road-sign recognition system that can be coupled with a vehicle tracking system to determine how often a driver violates the rules of the road. The system detects stop signs, red traffic lights and speed limit signs, and outputs this data in a format that can be read by a vehicle tracking system, where it can be combined with speed information and sent to a central database where the driver safety rating can be calculated. Input to the system comes from a simple, standard dashboard mounted camera within the vehicle, which generates a continuous stream of images of the scene directly in front of the vehicle. The images are subjected to a number of cascaded detection sub-systems to determine if any of the target objects (road signs) appear within that video frame. The detection system software had to be optimized for minimum false positive detections, since those will unfairly punish the driver, and it also had to be optimized for speed to run on small hardware that can be installed in the vehicle. The first stage of the cascaded system consists of an image detector that detects circles within the image, since traffic lights and speed signs are circular and a stop sign can be approximated by a circle when the image is blurred or the resolution is lowered. The second stage is a neural network that is trained to recognize the target road sign in order to determine which road sign was found, or to eliminate other circular objects found in the image frame. The output of the neural network is then sent through an iterative filter with a majority voted output to eliminate detection 'jitter' and the occasional incorrect classifier output. Object tracking is applied to the 'good' detection outputs and used as an additional input for the detection phase on the next frame. In this way the continuity and robustness of the image detector are improved, since the object tracker indicates to it where the target object is most likely to appear in the next frame, based on the track it has been following through previous frames. In the final stage the detection system output is written to the chosen pins of the hardware output port, from where the detection output can be indicated to the user and also used as an input to the vehicle tracking system. To find the best detection approach, some methods found in literature were studied and the most likely candidates compared. The scale invariant feature transform (SIFT) and speeded up robust features (SURF) algorithms are too slow compared to the cascaded approach to be used for real-time detection on an in-vehicle hardware platform. In the cascaded approach used, different detection stage algorithms are tested and compared. The Hough circle transform is measured against blob detection on stop signs and speed limit signs. On traffic light state detection two approaches are tested and compared, one based on colour information and the other on direct neural network classification. To run the software in the user's vehicle, an appropriate hardware platform is chosen. A number of promising hardware platforms were studied and their specifications compared before the best candidate was selected and purchased for the project. The developed software was tested on the selected hardware in a vehicle during real public road driving for extended periods and under various conditions.Dissertation (MEng)--University of Pretoria, 2017.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Structure-property relationships in laser assisted and mechanically deformed advanced materials

This research concentrates on the structural crystallographic properties in laser assisted and mechanically deformed shapes within hexagonal closed packed (HCP) material. This thesis explores a new ‘challenge and opportunity’ for lasers in the field of manufacturing processing, i.e. using a laser as an innovative tool for materials forming purposes. Indeed the laser have been used before as a sophisticated heat source for manufacturing products but the processing-structure-property relationships have been largely neglected. Here, we present a detailed and in-depth study of the structure-performance relationships in laser assisted and mechanically deformed advanced materials

Adaptive Algorithms for Automated Processing of Document Images

Large scale document digitization projects continue to motivate interesting document understanding technologies such as script and language identification, page classification, segmentation and enhancement. Typically, however, solutions are still limited to narrow domains or regular formats such as books, forms, articles or letters and operate best on clean documents scanned in a controlled environment. More general collections of heterogeneous documents challenge the basic assumptions of state-of-the-art technology regarding quality, script, content and layout. Our work explores the use of adaptive algorithms for the automated analysis of noisy and complex document collections. We first propose, implement and evaluate an adaptive clutter detection and removal technique for complex binary documents. Our distance transform based technique aims to remove irregular and independent unwanted foreground content while leaving text content untouched. The novelty of this approach is in its determination of best approximation to clutter-content boundary with text like structures. Second, we describe a page segmentation technique called Voronoi++ for complex layouts which builds upon the state-of-the-art method proposed by Kise [Kise1999]. Our approach does not assume structured text zones and is designed to handle multi-lingual text in both handwritten and printed form. Voronoi++ is a dynamically adaptive and contextually aware approach that considers components' separation features combined with Docstrum [O'Gorman1993] based angular and neighborhood features to form provisional zone hypotheses. These provisional zones are then verified based on the context built from local separation and high-level content features. Finally, our research proposes a generic model to segment and to recognize characters for any complex syllabic or non-syllabic script, using font-models. This concept is based on the fact that font files contain all the information necessary to render text and thus a model for how to decompose them. Instead of script-specific routines, this work is a step towards a generic character and recognition scheme for both Latin and non-Latin scripts

Experimental and Data-driven Workflows for Microstructure-based Damage Prediction

Materialermüdung ist die häufigste Ursache für mechanisches Versagen. Die Degradationsmechanismen, welche die Lebensdauer von Bauteilen bei vergleichsweise ausgeprägten zyklischen Belastungen bestimmen, sind gut bekannt. Bei Belastungen im makroskopisch elastischen Bereich hingegen, der (sehr) hochzyklischen Ermüdung, bestimmen die innere Struktur eines Werkstoffs und die Wechselwirkung kristallografischer Defekte die Lebensdauer. Unter diesen Umständen sind die inneren Degradationsphänomene auf der mikroskopischen Skala weitgehend reversibel und führen nicht zur Bildung kritischer Schädigungen, die kontinuierlich wachsen können. Allerdings sind einige Kornensembles in polykristallinen Metallen, je nach den lokalen mikrostrukturellen Gegebenheiten, anfällig für Schädigungsinitiierung, Rissbildung und -wachstum und wirken daher als Schwachstellen. Daher weisen Bauteile, die solchen Belastungen ausgesetzt sind, oft eine ausgeprägte Lebensdauerstreuung auf. Die Tatsache, dass ein umfassendes mechanistisches Verständnis für diese Degradationsprozesse in verschiedenen Werkstoffen nicht vorliegt, hat zur Folge, dass die derzeitigen Modellierungsbemühungen die mittlere Lebensdauer und ihre Varianz in der Regel nur mit unbefriedigender Genauigkeit vorhersagen. Dies wiederum erschwert die Bauteilauslegung und macht die Nutzung von Sicherheitsfaktoren während des Dimensionierungsprozesses erforderlich. Abhilfe kann geschaffen werden, indem umfangreiche Daten zu Einflussfaktoren und deren Wirkung auf die Bildung initialer Ermüdungsschädigungen erhoben werden. Die Datenknappheit wirkt sich nach wie vor negativ auf Datenwissenschaftler und Modellierungsexperten aus, die versuchen, trotz geringer Stichprobengröße und unvollständigen Merkmalsräumen, mikrostrukturelle Abhängigkeiten abzuleiten, datengetriebene Vorhersagemodelle zu trainieren oder physikalische, regelbasierte Modelle zu parametrisieren. Die Tatsache, dass nur wenige kritische Schädigungen bezogen auf das gesamte Probenvolumen auftreten und die hochzyklische Ermüdung eine Vielzahl unterschiedlicher Abhängigkeiten aufweist, impliziert einige Anforderungen an die Datenerfassung und -verarbeitung. Am wichtigsten ist, dass die Messtechniken so empfindlich sind, dass nuancierte Schwankungen im Probenzustand erfasst werden können, dass die gesamte Routine effizient ist und dass die korrelative Mikroskopie räumliche Informationen aus verschiedenen Messungen miteinander verbindet. Das Hauptziel dieser Arbeit besteht darin, einen Workflow zu etablieren, der den Datenmangel behebt, so dass die zukünftige virtuelle Auslegung von Komponenten effizienter, zuverlässiger und nachhaltiger gestaltet werden kann. Zu diesem Zweck wird in dieser Arbeit ein kombinierter experimenteller und datenverarbeitender Workflow vorgeschlagen, um multimodale Datensätze zu Ermüdungsschädigungen zu erzeugen. Der Schwerpunkt liegt dabei auf dem Auftreten von lokalen Gleitbändern, der Rissinitiierung und dem Wachstum mikrostrukturell kurzer Risse. Der Workflow vereint die Ermüdungsprüfung von mesoskaligen Proben, um die Empfindlichkeit der Schädigungsdetektion zu erhöhen, die ergänzende Charakterisierung, die multimodale Registrierung und Datenfusion der heterogenen Daten, sowie die bildverarbeitungsbasierte Schädigungslokalisierung und -bewertung. Mesoskalige Biegeresonanzprüfung ermöglicht das Erreichen des hochzyklischen Ermüdungszustands in vergleichsweise kurzen Zeitspannen bei gleichzeitig verbessertem Auflösungsvermögen der Schädigungsentwicklung. Je nach Komplexität der einzelnen Bildverarbeitungsaufgaben und Datenverfügbarkeit werden entweder regelbasierte Bildverarbeitungsverfahren oder Repräsentationslernen gezielt eingesetzt. So sorgt beispielsweise die semantische Segmentierung von Schädigungsstellen dafür, dass wichtige Ermüdungsmerkmale aus mikroskopischen Abbildungen extrahiert werden können. Entlang des Workflows wird auf einen hohen Automatisierungsgrad Wert gelegt. Wann immer möglich, wurde die Generalisierbarkeit einzelner Workflow-Elemente untersucht. Dieser Workflow wird auf einen ferritischen Stahl (EN 1.4003) angewendet. Der resultierende Datensatz verknüpft unter anderem große verzerrungskorrigierte Mikrostrukturdaten mit der Schädigungslokalisierung und deren zyklischer Entwicklung. Im Zuge der Arbeit wird der Datensatz wird im Hinblick auf seinen Informationsgehalt untersucht, indem detaillierte, analytische Studien zur einzelnen Schädigungsbildung durchgeführt werden. Auf diese Weise konnten unter anderem neuartige, quantitative Erkenntnisse über mikrostrukturinduzierte plastische Verformungs- und Rissstopmechanismen gewonnen werden. Darüber hinaus werden aus dem Datensatz abgeleitete kornweise Merkmalsvektoren und binäre Schädigungskategorien verwendet, um einen Random-Forest-Klassifikator zu trainieren und dessen Vorhersagegüte zu bewerten. Der vorgeschlagene Workflow hat das Potenzial, die Grundlage für künftiges Data Mining und datengetriebene Modellierung mikrostrukturempfindlicher Ermüdung zu legen. Er erlaubt die effiziente Erhebung statistisch repräsentativer Datensätze mit gleichzeitig hohem Informationsgehalt und kann auf eine Vielzahl von Werkstoffen ausgeweitet werden