On the generation of high dynamic range images: theory and practice from a statistical perspective

This dissertation studies the problem of high dynamic range (HDR) image generation from a statistical perspective. A thorough analysis of the camera acquisition process leads to a simplified yet realistic statistical model describing raw pixel values. The analysis and methods then proposed are based on this model. First, the theoretical performance bound of the problem is computed for the static case, where the acquisition conditions are controlled. Furthermore, a new method is proposed that, unlike previous methods, improves the reconstructed HDR image by taking into account the information carried by saturated samples. From a more practical perspective, two methods are proposed to generate HDR images in the more realistic and complex case where both objects and camera may exhibit motion. The first one is a multi-image, patch-based method, that simultaneously estimates and denoises the HDR image. The other is a single image approach that makes use of a general restoration method to generate the HDR image. This general restoration method, applicable to a wide range of problems, constitutes the last contribution of this dissertation

Sur la génération d'images à grande gamme dynamique. Théorie et pratique : une perspective statistique

This dissertation studies the problem of high dynamic range (HDR) image generation from a statistical perspective. A thorough analysis of the camera acquisition process leads to a simplified yet realistic statistical model describing raw pixel values. The analysis and methods then proposed are based on this model. First, the theoretical performance bound of the problem is computed for the static case, where the acquisition conditions are controlled. Furthermore, a new method is proposed that, unlike previous methods, improves the reconstructed HDR image by taking into account the information carried by saturated samples. From a more practical perspective, two methods are proposed to generate HDR images in the more realistic and complex case where both objects and camera may exhibit motion. The first one is a multi-image, patch-based method, that simultaneously estimates and denoises the HDR image. The other is a single image approach that makes use of a general restoration method to generate the HDR image. This general restoration method, applicable to a wide range of problems, constitutes the last contribution of this dissertation.Cette thèse porte sur le problème de la génération d'images à grande gamme dynamique (HDR pour l'anglais High Dynamic Range). Une analyse approfondie du processus d'acquisition de la caméra conduit tout d'abord à un modèle statistique simplifié mais réaliste décrivant les valeurs brutes des pixels. Les analyses et méthodes proposées par la suite sont fondées sur ce modèle.Nous posons le problème de l'estimation de l'irradiance comme un problème d'estimation statistique et en calculons la borne de performance. Les performances des estimateurs d'irradiance classiques sont comparées à cette borne. Les résultats obtenus justifient l'introduction d'un nouvel estimateur qui, au contraire des méthodes de la littérature, prend en compte les échantillons saturés.D'un point de vue plus pratique, deux méthodes sont proposées pour générer des images HDR dans le cas plus réaliste et complexe de scènes dynamiques. Nous proposons tout d'abord une méthode multi-image qui utilise des voisinages (patches) pour estimer et débruiter l'image HDR de façon simultanée. Nous proposons également une approche qui repose sur l'acquisition d'une seule image. Cette approche repose sur une méthode générique, par patches, de résolution des problèmes inverses pour génerer l'image HDR. Cette méthode de restauration, d'un point de vue plus général et pour une large gamme d'applications, constitue la dernière contribution de cette thèse

An active vision system for tracking and mosaicking on UAV.

Lin, Kai Wun.Thesis (M.Phil.)--Chinese University of Hong Kong, 2011.Includes bibliographical references (leaves 120-127).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Overview of the UAV Project --- p.1Chapter 1.2 --- Challenges on Vision System for UAV --- p.2Chapter 1.3 --- Contributions of this Work --- p.4Chapter 1.4 --- Organization of Thesis --- p.6Chapter 2 --- Image Sensor Selection and Evaluation --- p.8Chapter 2.1 --- Image Sensor Overview --- p.8Chapter 2.1.1 --- Comparing Sensor Features and Performance --- p.9Chapter 2.1.2 --- Rolling Shutter vsGlobal Shutter --- p.10Chapter 2.2 --- Sensor Evaluation through USB Peripheral --- p.11Chapter 2.2.1 --- Interfacing Image Sensor and USB Controller --- p.12Chapter 2.2.2 --- Image Sensor Configuration --- p.14Chapter 2.3 --- Image Data Transmitting and Processing --- p.17Chapter 2.3.1 --- Data Transfer Mode and Buffering on USB Controller --- p.18Chapter 2.3.2 --- Demosaicking of Bayer Image Data --- p.20Chapter 2.4 --- Splitting Images and Exposure Problem --- p.22Chapter 2.4.1 --- Buffer Overflow on USB Controller --- p.22Chapter 2.4.2 --- Image Luminance and Exposure Adjustment --- p.24Chapter 3 --- Embedded System for Vision Processing --- p.26Chapter 3.1 --- Overview of the Embedded System --- p.26Chapter 3.1.1 --- TI OMAP3530 Processor --- p.27Chapter 3.1.2 --- Gumstix Overo Fire Computer-on-Module --- p.27Chapter 3.2 --- Interfacing Camera Module to the Embedded System --- p.28Chapter 3.2.1 --- Image Signal Processing Subsystem --- p.29Chapter 3.2.2 --- Camera Module Adapting Board --- p.30Chapter 3.2.3 --- Image Sensor Driver and Program Development --- p.31Chapter 3.3 --- View-stabilizing Biaxial Camera Platform --- p.34Chapter 3.3.1 --- The New Camera System iv --- p.35Chapter 3.3.2 --- View-stabilizing Pan-tilt Platform --- p.41Chapter 3.4 --- Overall System Architecture and UAV Integration --- p.46Chapter 4 --- Target Tracking and Geo-locating --- p.50Chapter 4.1 --- Camera Calibration --- p.51Chapter 4.1.1 --- The Perspective Camera Model --- p.51Chapter 4.1.2 --- Camera Lens Distortions --- p.53Chapter 4.1.3 --- Calibration Toolbox and Results --- p.54Chapter 4.2 --- Selection of Object Features and Trackers --- p.56Chapter 4.2.1 --- Harris Corner Detection --- p.58Chapter 4.2.2 --- Color Histogram --- p.59Chapter 4.2.3 --- KLT and Mean-shift Tracker --- p.59Chapter 4.3 --- Target Auto-centering --- p.64Chapter 4.3.1 --- Formulation of the PID Controller --- p.65Chapter 4.3.2 --- Control Gain Settings and Tuning --- p.69Chapter 4.4 --- Geo-locating of Tracked Target --- p.69Chapter 4.4.1 --- Coordinate Frame Transformation --- p.70Chapter 4.4.2 --- Depth Estimation and Target Locating --- p.74Chapter 4.5 --- Results and Discussion --- p.77Chapter 5 --- Real-time Aerial Mosaic Building --- p.89Chapter 5.1 --- Motion Model Selection --- p.90Chapter 5.1.1 --- Planar Perspective Motion Model --- p.90Chapter 5.2 --- Feature-based Image Alignment --- p.91Chapter 5.2.1 --- Image Preprocessing --- p.91Chapter 5.2.2 --- Feature Extraction and Matching --- p.92Chapter 5.2.3 --- Image Alignment using RANSAC Algorithm --- p.94Chapter 5.3 --- Image Composition --- p.95Chapter 5.3.1 --- Image Blending with Distance Map --- p.96Chapter 5.3.2 --- Overall Stitching Process --- p.98Chapter 5.4 --- Mosaic Simulation using Google Earth --- p.99Chapter 5.5 --- Results and Discussion --- p.100Chapter 6 --- Conclusion and Further Work --- p.108Chapter A --- System Schematics --- p.111Chapter B --- Image Sensor Sensitivity --- p.118Bibliography --- p.12

Pixel level data-dependent triangulation with its applications

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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

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

Reconstruction and rendering of time-varying natural phenomena

While computer performance increases and computer generated images get ever more realistic, the need for modeling computer graphics content is becoming stronger. To achieve photo-realism detailed scenes have to be modeled often with a significant amount of manual labour. Interdisciplinary research combining the fields of Computer Graphics, Computer Vision and Scientific Computing has led to the development of (semi-)automatic modeling tools freeing the user of labour-intensive modeling tasks. The modeling of animated content is especially challenging. Realistic motion is necessary to convince the audience of computer games, movies with mixed reality content and augmented reality applications. The goal of this thesis is to investigate automated modeling techniques for time-varying natural phenomena. The results of the presented methods are animated, three-dimensional computer models of fire, smoke and fluid flows.Durch die steigende Rechenkapazität moderner Computer besteht die Möglichkeit immer realistischere Bilder virtuell zu erzeugen. Dadurch entsteht ein größerer Bedarf an Modellierungsarbeit um die nötigen Objekte virtuell zu beschreiben. Um photorealistische Bilder erzeugen zu können müssen sehr detaillierte Szenen, oft in mühsamer Handarbeit, modelliert werden. Ein interdisziplinärer Forschungszweig, der Computergrafik, Bildverarbeitung und Wissenschaftliches Rechnen verbindet, hat in den letzten Jahren die Entwicklung von (semi-)automatischen Methoden zur Modellierung von Computergrafikinhalten vorangetrieben. Die Modellierung dynamischer Inhalte ist dabei eine besonders anspruchsvolle Aufgabe, da realistische Bewegungsabläufe sehr wichtig für eine überzeugende Darstellung von Computergrafikinhalten in Filmen, Computerspielen oder Augmented-Reality Anwendungen sind. Das Ziel dieser Arbeit ist es automatische Modellierungsmethoden für dynamische Naturerscheinungen wie Wasserfluss, Feuer, Rauch und die Bewegung erhitzter Luft zu entwickeln. Das Resultat der entwickelten Methoden sind dabei dynamische, dreidimensionale Computergrafikmodelle