Feature extraction using MPEG-CDVS and Deep Learning with application to robotic navigation and image classification

The main contributions of this thesis are the evaluation of MPEG Compact Descriptor for Visual Search in the context of indoor robotic navigation and the introduction of a new method for training Convolutional Neural Networks with applications to object classification. The choice for image descriptor in a visual navigation system is not straightforward. Visual descriptors must be distinctive enough to allow for correct localisation while still offering low matching complexity and short descriptor size for real-time applications. MPEG Compact Descriptor for Visual Search is a low complexity image descriptor that offers several levels of compromises between descriptor distinctiveness and size. In this work, we describe how these trade-offs can be used for efficient loop-detection in a typical indoor environment. We first describe a probabilistic approach to loop detection based on the standard’s suggested similarity metric. We then evaluate the performance of CDVS compression modes in terms of matching speed, feature extraction, and storage requirements and compare them with the state of the art SIFT descriptor for five different types of indoor floors. During the second part of this thesis we focus on the new paradigm to machine learning and computer vision called Deep Learning. Under this paradigm visual features are no longer extracted using fine-grained, highly engineered feature extractor, but rather using a Convolutional Neural Networks (CNN) that extracts hierarchical features learned directly from data at the cost of long training periods. In this context, we propose a method for speeding up the training of Convolutional Neural Networks (CNN) by exploiting the spatial scaling property of convolutions. This is done by first training a pre-train CNN of smaller kernel resolutions for a few epochs, followed by properly rescaling its kernels to the target’s original dimensions and continuing training at full resolution. We show that the overall training time of a target CNN architecture can be reduced by exploiting the spatial scaling property of convolutions during early stages of learning. Moreover, by rescaling the kernels at different epochs, we identify a trade-off between total training time and maximum obtainable accuracy. Finally, we propose a method for choosing when to rescale kernels and evaluate our approach on recent architectures showing savings in training times of nearly 20% while test set accuracy is preserved

Content Based Image Retrieval by Preprocessing Image Database

Increase in communication bandwidth, information content and the size of the multimedia databases have given rise to the concept of Content Based Image Retrieval (CBIR). Content based image retrieval is a technique that enables a user to extract similar images based on a query, from a database containing a large amount of images. A basic issue in designing a content based image retrieval system is to select the image features that best represent image content in a database. Current research in this area focuses on improving image retrieval accuracy. In this work, we have presented an ecient system for content based image retrieval. The system exploits the multiple features such as color, edge density, boolean edge density and histogram information features. The existing methods are concentrating on the relevance feedback techniques to improve the count of similar images related to a query from the raw image database. In this thesis, we propose a dierent strategy called preprocessing image database using k means clustering and genetic algorithm so that it will further helps to improve image retrieval accuracy. This can be achieved by taking multiple feature set, clustering algorithm and tness function for the genetic algorithms. Preprocessing image database is to cluster the similar images as homogeneous as possible and separate the dissimilar images as heterogeneous as possible. The main aim of this work is to nd the images that are most similar to the query image and new method is proposed for preprocessing image database via genetic algorithm for improved content based image retrieval system. The accuracy of our approach is presented by using performance metrics called confusion matrix, precison graph and F-measures. The clustering purity in more than half of the clusters has been above 90 percent purity

A window to the past through modern urban environments: Developing a photogrammetric workflow for the orientation parameter estimation of historical images

The ongoing process of digitization in archives is providing access to ever-increasing historical image collections. In many of these repositories, images can typically be viewed in a list or gallery view. Due to the growing number of digitized objects, this type of visualization is becoming increasingly complex. Among other things, it is difficult to determine how many photographs show a particular object and spatial information can only be communicated via metadata. Within the scope of this thesis, research is conducted on the automated determination and provision of this spatial data. Enhanced visualization options make this information more eas- ily accessible to scientists as well as citizens. Different types of visualizations can be presented in three-dimensional (3D), Virtual Reality (VR) or Augmented Reality (AR) applications. However, applications of this type require the estimation of the photographer’s point of view. In the photogrammetric context, this is referred to as estimating the interior and exterior orientation parameters of the camera. For determination of orientation parameters for single images, there are the established methods of Direct Linear Transformation (DLT) or photogrammetric space resection. Using these methods requires the assignment of measured object points to their homologue image points. This is feasible for single images, but quickly becomes impractical due to the large amount of images available in archives. Thus, for larger image collections, usually the Structure-from-Motion (SfM) method is chosen, which allows the simultaneous estimation of the interior as well as the exterior orientation of the cameras. While this method yields good results especially for sequential, contemporary image data, its application to unsorted historical photographs poses a major challenge. In the context of this work, which is mainly limited to scenarios of urban terrestrial photographs, the reasons for failure of the SfM process are identified. In contrast to sequential image collections, pairs of images from different points in time or from varying viewpoints show huge differences in terms of scene representation such as deviations in the lighting situation, building state, or seasonal changes. Since homologue image points have to be found automatically in image pairs or image sequences in the feature matching procedure of SfM, these image differences pose the most complex problem. In order to test different feature matching methods, it is necessary to use a pre-oriented historical dataset. Since such a benchmark dataset did not exist yet, eight historical image triples (corresponding to 24 image pairs) are oriented in this work by manual selection of homologue image points. This dataset allows the evaluation of frequently new published methods in feature matching. The initial methods used, which are based on algorithmic procedures for feature matching (e.g., Scale Invariant Feature Transform (SIFT)), provide satisfactory results for only few of the image pairs in this dataset. By introducing methods that use neural networks for feature detection and feature description, homologue features can be reliably found for a large fraction of image pairs in the benchmark dataset. In addition to a successful feature matching strategy, determining camera orientation requires an initial estimate of the principal distance. Hence for historical images, the principal distance cannot be directly determined as the camera information is usually lost during the process of digitizing the analog original. A possible solution to this problem is to use three vanishing points that are automatically detected in the historical image and from which the principal distance can then be determined. The combination of principal distance estimation and robust feature matching is integrated into the SfM process and allows the determination of the interior and exterior camera orientation parameters of historical images. Based on these results, a workflow is designed that allows archives to be directly connected to 3D applications. A search query in archives is usually performed using keywords, which have to be assigned to the corresponding object as metadata. Therefore, a keyword search for a specific building also results in hits on drawings, paintings, events, interior or detailed views directly connected to this building. However, for the successful application of SfM in an urban context, primarily the photographic exterior view of the building is of interest. While the images for a single building can be sorted by hand, this process is too time-consuming for multiple buildings. Therefore, in collaboration with the Competence Center for Scalable Data Services and Solutions (ScaDS), an approach is developed to filter historical photographs by image similarities. This method reliably enables the search for content-similar views via the selection of one or more query images. By linking this content-based image retrieval with the SfM approach, automatic determination of camera parameters for a large number of historical photographs is possible. The developed method represents a significant improvement over commercial and open-source SfM standard solutions. The result of this work is a complete workflow from archive to application that automatically filters images and calculates the camera parameters. The expected accuracy of a few meters for the camera position is sufficient for the presented applications in this work, but offer further potential for improvement. A connection to archives, which will automatically exchange photographs and positions via interfaces, is currently under development. This makes it possible to retrieve interior and exterior orientation parameters directly from historical photography as metadata which opens up new fields of research.:1 Introduction 1 1.1 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Historical image data and archives . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Structure-from-Motion for historical images . . . . . . . . . . . . . . . . . . . 4 1.3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Selection of images and preprocessing . . . . . . . . . . . . . . . . . . 5 1.3.3 Feature detection, feature description and feature matching . . . . . . 6 1.3.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3.2 Feature description . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.3.3 Feature matching . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.3.4 Geometric verification and robust estimators . . . . . . . . . 13 1.3.3.5 Joint methods . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.3.4 Initial parameterization . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.3.5 Bundle adjustment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.6 Dense reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1.3.7 Georeferencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.4 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2 Generation of a benchmark dataset using historical photographs for the evaluation of feature matching methods 29 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 Image differences based on digitization and image medium . . . . . . . 30 2.1.2 Image differences based on different cameras and acquisition technique 31 2.1.3 Object differences based on different dates of acquisition . . . . . . . . 31 2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 The image dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4 Comparison of different feature detection and description methods . . . . . . 35 2.4.1 Oriented FAST and Rotated BRIEF (ORB) . . . . . . . . . . . . . . . 36 2.4.2 Maximally Stable Extremal Region Detector (MSER) . . . . . . . . . 36 2.4.3 Radiation-invariant Feature Transform (RIFT) . . . . . . . . . . . . . 36 2.4.4 Feature matching and outlier removal . . . . . . . . . . . . . . . . . . 36 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Conclusions and future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3 Photogrammetry as a link between image repository and 4D applications 45 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 IX Contents 3.2 Multimodal access on repositories . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.1 Conventional access . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2.2 Virtual access using online collections . . . . . . . . . . . . . . . . . . 48 3.2.3 Virtual museums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.3 Workflow and access strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.3.2 Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 3.3.3 Photogrammetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3.4 Browser access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3.5 VR and AR access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4 An adapted Structure-from-Motion Workflow for the orientation of historical images 69 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.2 Related Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.2.1 Historical images for 3D reconstruction . . . . . . . . . . . . . . . . . 72 4.2.2 Algorithmic Feature Detection and Matching . . . . . . . . . . . . . . 73 4.2.3 Feature Detection and Matching using Convolutional Neural Networks 74 4.3 Feature Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 4.4 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.4.1 Step 1: Data preparation . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.4.2 Step 2.1: Feature Detection and Matching . . . . . . . . . . . . . . . . 78 4.4.3 Step 2.2: Vanishing Point Detection and Principal Distance Estimation 80 4.4.4 Step 3: Scene Reconstruction . . . . . . . . . . . . . . . . . . . . . . . 80 4.4.5 Comparison with Three Other State-of-the-Art SfM Workflows . . . . 81 4.5 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 4.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.7 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 4.8 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5 Fully automated pose estimation of historical images 97 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 5.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.1 Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 5.2.2 Feature Detection and Matching . . . . . . . . . . . . . . . . . . . . . 101 5.3 Data Preparation: Image Retrieval . . . . . . . . . . . . . . . . . . . . . . . . 102 5.3.1 Experiment and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2.1 Layer Extraction Approach (LEA) . . . . . . . . . . . . . . . 104 5.3.2.2 Attentive Deep Local Features (DELF) Approach . . . . . . 105 5.3.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 106 5.4 Camera Pose Estimation of Historical Images Using Photogrammetric Methods 110 5.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 5.4.1.1 Benchmark Datasets . . . . . . . . . . . . . . . . . . . . . . . 111 5.4.1.2 Retrieval Datasets . . . . . . . . . . . . . . . . . . . . . . . . 113 5.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 5.4.2.1 Feature Detection and Matching . . . . . . . . . . . . . . . . 115 5.4.2.2 Geometric Verification and Camera Pose Estimation . . . . . 116 5.4.3 Results and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 117 5.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.A Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6 Related publications 129 6.1 Photogrammetric analysis of historical image repositores for virtual reconstruction in the field of digital humanities . . . . . . . . . . . . . . . . . . . . . . . 130 6.2 Feature matching of historical images based on geometry of quadrilaterals . . 131 6.3 Geo-information technologies for a multimodal access on historical photographs and maps for research and communication in urban history . . . . . . . . . . 132 6.4 An automated pipeline for a browser-based, city-scale mobile 4D VR application based on historical images . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6.5 Software and content design of a browser-based mobile 4D VR application to explore historical city architecture . . . . . . . . . . . . . . . . . . . . . . . . 134 7 Synthesis 135 7.1 Summary of the developed workflows . . . . . . . . . . . . . . . . . . . . . . . 135 7.1.1 Error assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 7.1.2 Accuracy estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 7.1.3 Transfer of the workflow . . . . . . . . . . . . . . . . . . . . . . . . . . 141 7.2 Developments and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 8 Appendix 149 8.1 Setup for the feature matching evaluation . . . . . . . . . . . . . . . . . . . . 149 8.2 Transformation from COLMAP coordinate system to OpenGL . . . . . . . . 150 References 151 List of Figures 165 List of Tables 167 List of Abbreviations 169Der andauernde Prozess der Digitalisierung in Archiven ermöglicht den Zugriff auf immer größer werdende historische Bildbestände. In vielen Repositorien können die Bilder typischerweise in einer Listen- oder Gallerieansicht betrachtet werden. Aufgrund der steigenden Zahl an digitalisierten Objekten wird diese Art der Visualisierung zunehmend unübersichtlicher. Es kann u.a. nur noch schwierig bestimmt werden, wie viele Fotografien ein bestimmtes Motiv zeigen. Des Weiteren können räumliche Informationen bisher nur über Metadaten vermittelt werden. Im Rahmen der Arbeit wird an der automatisierten Ermittlung und Bereitstellung dieser räumlichen Daten geforscht. Erweiterte Visualisierungsmöglichkeiten machen diese Informationen Wissenschaftlern sowie Bürgern einfacher zugänglich. Diese Visualisierungen können u.a. in drei-dimensionalen (3D), Virtual Reality (VR) oder Augmented Reality (AR) Anwendungen präsentiert werden. Allerdings erfordern Anwendungen dieser Art die Schätzung des Standpunktes des Fotografen. Im photogrammetrischen Kontext spricht man dabei von der Schätzung der inneren und äußeren Orientierungsparameter der Kamera. Zur Bestimmung der Orientierungsparameter für Einzelbilder existieren die etablierten Verfahren der direkten linearen Transformation oder des photogrammetrischen Rückwärtsschnittes. Dazu muss eine Zuordnung von gemessenen Objektpunkten zu ihren homologen Bildpunkten erfolgen. Das ist für einzelne Bilder realisierbar, wird aber aufgrund der großen Menge an Bildern in Archiven schnell nicht mehr praktikabel. Für größere Bildverbände wird im photogrammetrischen Kontext somit üblicherweise das Verfahren Structure-from-Motion (SfM) gewählt, das die simultane Schätzung der inneren sowie der äußeren Orientierung der Kameras ermöglicht. Während diese Methode vor allem für sequenzielle, gegenwärtige Bildverbände gute Ergebnisse liefert, stellt die Anwendung auf unsortierten historischen Fotografien eine große Herausforderung dar. Im Rahmen der Arbeit, die sich größtenteils auf Szenarien stadträumlicher terrestrischer Fotografien beschränkt, werden zuerst die Gründe für das Scheitern des SfM Prozesses identifiziert. Im Gegensatz zu sequenziellen Bildverbänden zeigen Bildpaare aus unterschiedlichen zeitlichen Epochen oder von unterschiedlichen Standpunkten enorme Differenzen hinsichtlich der Szenendarstellung. Dies können u.a. Unterschiede in der Beleuchtungssituation, des Aufnahmezeitpunktes oder Schäden am originalen analogen Medium sein. Da für die Merkmalszuordnung in SfM automatisiert homologe Bildpunkte in Bildpaaren bzw. Bildsequenzen gefunden werden müssen, stellen diese Bilddifferenzen die größte Schwierigkeit dar. Um verschiedene Verfahren der Merkmalszuordnung testen zu können, ist es notwendig einen vororientierten historischen Datensatz zu verwenden. Da solch ein Benchmark-Datensatz noch nicht existierte, werden im Rahmen der Arbeit durch manuelle Selektion homologer Bildpunkte acht historische Bildtripel (entspricht 24 Bildpaaren) orientiert, die anschließend genutzt werden, um neu publizierte Verfahren bei der Merkmalszuordnung zu evaluieren. Die ersten verwendeten Methoden, die algorithmische Verfahren zur Merkmalszuordnung nutzen (z.B. Scale Invariant Feature Transform (SIFT)), liefern nur für wenige Bildpaare des Datensatzes zufriedenstellende Ergebnisse. Erst durch die Verwendung von Verfahren, die neuronale Netze zur Merkmalsdetektion und Merkmalsbeschreibung einsetzen, können für einen großen Teil der historischen Bilder des Benchmark-Datensatzes zuverlässig homologe Bildpunkte gefunden werden. Die Bestimmung der Kameraorientierung erfordert zusätzlich zur Merkmalszuordnung eine initiale Schätzung der Kamerakonstante, die jedoch im Zuge der Digitalisierung des analogen Bildes nicht mehr direkt zu ermitteln ist. Eine mögliche Lösung dieses Problems ist die Verwendung von drei Fluchtpunkten, die automatisiert im historischen Bild detektiert werden und aus denen dann die Kamerakonstante bestimmt werden kann. Die Kombination aus Schätzung der Kamerakonstante und robuster Merkmalszuordnung wird in den SfM Prozess integriert und erlaubt die Bestimmung der Kameraorientierung historischer Bilder. Auf Grundlage dieser Ergebnisse wird ein Arbeitsablauf konzipiert, der es ermöglicht, Archive mittels dieses photogrammetrischen Verfahrens direkt an 3D-Anwendungen anzubinden. Eine Suchanfrage in Archiven erfolgt üblicherweise über Schlagworte, die dann als Metadaten dem entsprechenden Objekt zugeordnet sein müssen. Eine Suche nach einem bestimmten Gebäude generiert deshalb u.a. Treffer zu Zeichnungen, Gemälden, Veranstaltungen, Innen- oder Detailansichten. Für die erfolgreiche Anwendung von SfM im stadträumlichen Kontext interessiert jedoch v.a. die fotografische Außenansicht des Gebäudes. Während die Bilder für ein einzelnes Gebäude von Hand sortiert werden können, ist dieser Prozess für mehrere Gebäude zu zeitaufwendig. Daher wird in Zusammenarbeit mit dem Competence Center for Scalable Data Services and Solutions (ScaDS) ein Ansatz entwickelt, um historische Fotografien über Bildähnlichkeiten zu filtern. Dieser ermöglicht zuverlässig über die Auswahl eines oder mehrerer Suchbilder die Suche nach inhaltsähnlichen Ansichten. Durch die Verknüpfung der inhaltsbasierten Suche mit dem SfM Ansatz ist es möglich, automatisiert für eine große Anzahl historischer Fotografien die Kameraparameter zu bestimmen. Das entwickelte Verfahren stellt eine deutliche Verbesserung im Vergleich zu kommerziellen und open-source SfM Standardlösungen dar. Das Ergebnis dieser Arbeit ist ein kompletter Arbeitsablauf vom Archiv bis zur Applikation, der automatisch Bilder filtert und diese orientiert. Die zu erwartende Genauigkeit von wenigen Metern für die Kameraposition sind ausreichend für die dargestellten Anwendungen in dieser Arbeit, bieten aber weiteres Verbesserungspotential. Eine Anbindung an Archive, die über Schnittstellen automatisch Fotografien und Positionen austauschen soll, befindet sich bereits in der Entwicklung. Dadurch ist es möglich, innere und äußere Orientierungsparameter direkt von der historischen Fotografie als Metadaten abzurufen, was neue Forschungsfelder eröffnet.:1 Introduction 1 1.1 Thesis structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Historical image data and archives . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Structure-from-Motion for historical images . . . . . . . . . . . . . . . . . . . 4 1.3.1 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3.2 Selection of images and preprocessing . . . . . . . . . . . . . . . . . . 5 1.3.3 Feature detection, feature description and feature matching . . . . . . 6 1.3.3.1 Feature detection . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.3.2 Feature description . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.3.3 Feature matching . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3.3.4 Geometric verification and robust estimators . . . . . . . . . 13 1.3.3.5 Joint methods . . . . . . . . . . . . . . . .

Low Complexity Image Recognition Algorithms for Handheld devices

Content Based Image Retrieval (CBIR) has gained a lot of interest over the last two decades. The need to search and retrieve images from databases, based on information (“features”) extracted from the image itself, is becoming increasingly important. CBIR can be useful for handheld image recognition devices in which the image to be recognized is acquired with a camera, and thus there is no additional metadata associated to it. However, most CBIR systems require large computations, preventing their use in handheld devices. In this PhD work, we have developed low-complexity algorithms for content based image retrieval in handheld devices for camera acquired images. Two novel algorithms, ‘Color Density Circular Crop’ (CDCC) and ‘DCT-Phase Match’ (DCTPM), to perform image retrieval along with a two-stage image retrieval algorithm that combines CDCC and DCTPM, to achieve the low complexity required in handheld devices are presented. The image recognition algorithms run on a handheld device over a large database with fast retrieval time besides having high accuracy, precision and robustness to environment variations. Three algorithms for Rotation, Scale, and Translation (RST) compensation for images were also developed in this PhD work to be used in conjunction with the two-stage image retrieval algorithm. The developed algorithms are implemented, using a commercial fixed-point Digital Signal Processor (DSP), into a device, called ‘PictoBar’, in the domain of Alternative and Augmentative Communication (AAC). The PictoBar is intended to be used in the field of electronic aid for disabled people, in areas like speech rehabilitation therapy, education etc. The PictoBar is able to recognize pictograms and pictures contained in a database. Once an image is found in the database, a corresponding associated speech message is played. A methodology for optimal implementation and systematic testing of the developed image retrieval algorithms on a fixed point DSP is also established as part of this PhD work

AUTOMATED FEATURE EXTRACTION AND CONTENT-BASED RETRIEVAL OFPATHOLOGY MICROSCOPIC IMAGES USING K-MEANS CLUSTERING AND CODE RUN-LENGTH PROBABILITY DISTRIBUTION

The dissertation starts with an extensive literature survey on the current issues in content-based image retrieval (CBIR) research, the state-of-the-art theories, methodologies, and implementations, covering topics such as general information retrieval theories, imaging, image feature identification and extraction, feature indexing and multimedia database search, user-system interaction, relevance feedback, and performance evaluation. A general CBIR framework has been proposed with three layers: image document space, feature space, and concept space. The framework emphasizes that while the projection from the image document space to the feature space is algorithmic and unrestricted, the connection between the feature space and the concept space is based on statistics instead of semantics. The scheme favors image features that do not rely on excessive assumptions about image contentAs an attempt to design a new CBIR methodology following the above framework, k-means clustering color quantization is applied to pathology microscopic images, followed by code run-length probability distribution feature extraction. Kulback-Liebler divergence is used as distance measure for feature comparison. For content-based retrieval, the distance between two images is defined as a function of all individual features. The process is highly automated and the system is capable of working effectively across different tissues without human interference. Possible improvements and future directions have been discussed

Color image quality measures and retrieval

The focus of this dissertation is mainly on color image, especially on the images with lossy compression. Issues related to color quantization, color correction, color image retrieval and color image quality evaluation are addressed. A no-reference color image quality index is proposed. A novel color correction method applied to low bit-rate JPEG image is developed. A novel method for content-based image retrieval based upon combined feature vectors of shape, texture, and color similarities has been suggested. In addition, an image specific color reduction method has been introduced, which allows a 24-bit JPEG image to be shown in the 8-bit color monitor with 256-color display. The reduction in download and decode time mainly comes from the smart encoder incorporating with the proposed color reduction method after color space conversion stage. To summarize, the methods that have been developed can be divided into two categories: one is visual representation, and the other is image quality measure. Three algorithms are designed for visual representation: (1) An image-based visual representation for color correction on low bit-rate JPEG images. Previous studies on color correction are mainly on color image calibration among devices. Little attention was paid to the compressed image whose color distortion is evident in low bit-rate JPEG images. In this dissertation, a lookup table algorithm is designed based on the loss of PSNR in different compression ratio. (2) A feature-based representation for content-based image retrieval. It is a concatenated vector of color, shape, and texture features from region of interest (ROI). (3) An image-specific 256 colors (8 bits) reproduction for color reduction from 16 millions colors (24 bits). By inserting the proposed color reduction method into a JPEG encoder, the image size could be further reduced and the transmission time is also reduced. This smart encoder enables its decoder using less time in decoding. Three algorithms are designed for image quality measure (IQM): (1) A referenced IQM based upon image representation in very low-dimension. Previous studies on IQMs are based on high-dimensional domain including spatial and frequency domains. In this dissertation, a low-dimensional domain IQM based on random projection is designed, with preservation of the IQM accuracy in high-dimensional domain. (2) A no-reference image blurring metric. Based on the edge gradient, the degree of image blur can be measured. (3) A no-reference color IQM based upon colorfulness, contrast and sharpness

Digital Image Users and Reuse: Enhancing practitioner discoverability of digital library reuse based on user file naming behavior

Diese Dissertation untersucht Geräte, die Praktiker verwenden, um die Wiederverwendung von digitalen Bibliotheksmaterialien zu entdecken. Der Autor führt zwei Verifikationsstudien durch, in denen zwei zuvor angewandte Strategien untersucht werden, die Praktiker verwenden, um die Wiederverwendung digitaler Objekte zu identifizieren, insbesondere Google Images Reverse Image Lookup (RIL) und eingebettete Metadaten. Es beschreibt diese Strategiebeschränkungen und bietet einen neuen, einzigartigen Ansatz zur Verfolgung der Wiederverwendung, indem der Suchansatz des Autors basierend auf dem Benennungsverhalten von Benutzerdateien verwendet wird. Bei der Untersuchung des Nutzens und der Einschränkungen von Google Images und eingebetteten Metadaten beobachtet und dokumentiert der Autor ein Muster des Benennungsverhaltens von Benutzerdateien, das vielversprechend ist, die Wiederverwendung durch den Praktiker zu verbessern. Der Autor führt eine Untersuchung zur Bewertung der Dateibenennung durch, um dieses Muster des Verhaltens der Benutzerdateibenennung und die Auswirkungen der Dateibenennung auf die Suchmaschinenoptimierung zu untersuchen. Der Autor leitet mehrere signifikante Ergebnisse ab, während er diese Studie fertigstellt. Der Autor stellt fest, dass Google Bilder aufgrund der Änderung des Algorithmus kein brauchbares Werkzeug mehr ist, um die Wiederverwendung durch die breite Öffentlichkeit oder andere Benutzer zu entdecken, mit Ausnahme von Benutzern aus der Industrie. Eingebettete Metadaten sind aufgrund der nicht persistenten Natur eingebetteter Metadaten kein zuverlässiges Bewertungsinstrument. Der Autor stellt fest, dass viele Benutzer ihre eigenen Dateinamen generieren, die beim Speichern und Teilen von digitalen Bildern fast ausschließlich für Menschen lesbar sind. Der Autor argumentiert, dass, wenn Praktiker Suchbegriffe nach den "aggregierten Dateinamen" modellieren, sie ihre Entdeckung wiederverwendeter digitaler Objekte erhöhen.This dissertation explores devices practitioners utilize to discover the reuse of digital library materials. The author performs two verification studies investigating two previously employed strategies that practitioners use to identify digital object reuse, specifically Google Images reverse image lookup (RIL) and embedded metadata. It describes these strategy limitations and offers a new, unique approach for tracking reuse by employing the author's search approach based on user file naming behavior. While exploring the utility and limitations of Google Images and embedded metadata, the author observes and documents a pattern of user file naming behavior that exhibits promise for improving practitioner's discoverability of reuse. The author conducts a file naming assessment investigation to examine this pattern of user file naming behavior and the impact of file naming on search engine optimization. The author derives several significant findings while completing this study. The author establishes that Google Images is no longer a viable tool to discover reuse by the general public or other users except for industry users because of its algorithm change. Embedded metadata is not a reliable assessment tool because of the non-persistent nature of embedded metadata. The author finds that many users generate their own file names, almost exclusively human-readable when saving and sharing digital images. The author argues that when practitioners model search terms after the "aggregated file names" they increase their discovery of reused digital objects