Learnable Descriptors for Visual Search

This work proposes LDVS, a learnable binary local descriptor devised for matching natural images within the MPEG CDVS framework. LDVS descriptors are learned so that they can be sign-quantized and compared using the Hamming distance. The underlying convolutional architecture enjoys a moderate parameters count for operations on mobile devices. Our experiments show that LDVS descriptors perform favorably over comparable learned binary descriptors at patch matching on two different datasets. A complete pair-wise image matching pipeline is then designed around LDVS descriptors, integrating them in the reference CDVS evaluation framework. Experiments show that LDVS descriptors outperform the compressed CDVS SIFT-like descriptors at pair-wise image matching over the challenging CDVS image dataset

Deep Learning-Based Low Complexity and High Efficiency Moving Object Detection Methods

Moving object detection (MOD) is the process of extracting dynamic foreground content from the video frames, such as moving vehicles or pedestrians, while discarding the nonmoving background. It plays an essential role in computer vision field. The traditional methods meet difficulties when applied in complex scenarios, such as videos with illumination changes, shadows, night scenes,and dynamic backgrounds. Deep learning methods have been actively applied to moving object detection in recent years and demonstrated impressive results. However, many existing models render superior detection accuracy at the cost of high computational complexity and slow inference speed. This fact has hindered the development of such models in mobile and embedded vision tasks, which need to be carried out in a timely fashion on a computationally limited platform. The current research aims to use the technique of separable convolution in both 2D and 3D CNN together with our proposed multi-input multi-output strategy and two-branch structure to devise new deep network models that significantly improve inference speed, yet require smaller model size and achieve reduction in floating-point operations as compared to existing deep learning models with competitive detection accuracy. This research devised three deep neural network models, addressing the following main problems in the area of moving object detection: 1. Improving Detection Accuracy by extracting both spatial and temporal information: To improve detection accuracy, the proposed models adopt 3D convolution which is more suitable to extract both spatial and temporal information in video data than 2D convolution. We also put this 3D convolution into two-branch network that extracts both high-level global features and low-level detailed features can further increase the accuracy. 2. Reduce model size and computational complexity by changing network structure: The standard 2D and 3D convolution are further decomposed into depthwise and pointwise convolutions. While existing 3D separable CNN all addressed other problems such as gesture recognition, force prediction, 3D object classification or reconstruction, our work applied it to the moving object detection task for the first time in the literature. 3. Increasing inference speed by changing the input-output relationship: We proposed a multi-input multi-output (MIMO) strategy to increase inference speed, which can take multiple frames as the network input and output multiple frames of detection results. This MIMO embedded in 3Dseparable CNN can further increase model inference speed significantly and maintain high detection accuracy. Compared to state-of-the-art approaches, our proposed methods significantly increases the inference speed, reduces the model size, meanwhile achieving the highest detection accuracy in the scene dependent evaluation (SDE) setup and maintaining a competitive detection accuracy in the scene independent evaluation (SIE) setup. The SDE setup is widely used to tune and test the model on a specific video as the training and test sets are from the same video. The SIE setup is designed to assess the generalization capability of the model on completely unseen videos

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 . . . . . . . . . . . . . . . .

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field

Improving Visual Place Recognition in Changing Environments

For many years, the research community has been highly interested in autonomous robotics and its various applications, from healthcare to manufacturing, transportation to construction, and more. An autonomous robot's key challenge is the ability to determine its location. A fundamental research topic in localization is Visual Place Recognition (VPR), a task of detecting a previously visited location through visual input alone. One specific challenge in VPR is dealing with a place's appearance variation across different visits, which can occur due to viewpoint and environmental changes such as illumination, weather, and seasonal variations. While appearance changes already make VPR challenging, a further difficulty is posed by the resource constraints of many robots employed in real-world applications that limit the usability of learning-based techniques, which enable state-of-the-art performance but are computationally expensive. This thesis aims to combine the need for accurate place recognition in changing environments with low resource usage. The work presented here explores different approaches, from local image feature descriptors to Binary Neural Networks (BNN), to improve the computational and energy efficiency of VPR. The best BNN-based VPR descriptor obtained runs up to one order of magnitude faster than many CNN-based and hand-crafted approaches while maintaining comparable performance and expending a small amount of energy to process an image. Specifically, the proposed BNN can process an image 7 to 14 times faster than AlexNet, spending 13\% of the power at most when deployed on a low-end ARM platform. The results in this manuscript are presented using a new performance metric and an evaluation framework designed explicitly for VPR applications aiming at the two-fold purpose of providing meaningful insights into VPR performance and making results easily comparable across the chapters

Detailed Three-Dimensional Building Façade Reconstruction: A Review on Applications, Data and Technologies

Urban environments are regions of complex and diverse architecture. Their reconstruction and representation as three-dimensional city models have attracted the attention of many researchers and industry specialists, as they increasingly recognise the potential for new applications requiring detailed building models. Nevertheless, despite being investigated for a few decades, the comprehensive reconstruction of buildings remains a challenging task. While there is a considerable body of literature on this topic, including several systematic reviews summarising ways of acquiring and reconstructing coarse building structures, there is a paucity of in-depth research on the detection and reconstruction of façade openings (i.e., windows and doors). In this review, we provide an overview of emerging applications, data acquisition and processing techniques for building façade reconstruction, emphasising building opening detection. The use of traditional technologies from terrestrial and aerial platforms, along with emerging approaches, such as mobile phones and volunteered geography information, is discussed. The current status of approaches for opening detection is then examined in detail, separated into methods for three-dimensional and two-dimensional data. Based on the review, it is clear that a key limitation associated with façade reconstruction is process automation and the need for user intervention. Another limitation is the incompleteness of the data due to occlusion, which can be reduced by data fusion. In addition, the lack of available diverse benchmark datasets and further investigation into deep-learning methods for façade openings extraction present crucial opportunities for future research