A disocclusion replacement approach to subjective assessment for depth map quality evaluation

An inherent problem of Depth Image Based Rendering (DIBR) is the visual presence of disocclusions in the rendered views. This poses a significant challenge when the subjective assessment of these views is utilised for evaluating the quality of the depth maps used in the rendering process. Although various techniques are available to address this challenge, they result in concealing distortions, which are directly caused by the depth map imperfections. For the purposes of depth map quality evaluation, there is a need for an approach that deals with the presence of disocclusions without having further impact on other distortions. The aim of this approach is to enable the subjective assessments of rendered views to provide results, which are more representative of the quality of the depth map used in the rendering process

A disocclusion replacement approach to subjective assessment for depth map quality evaluation

An inherent problem of Depth Image Based Rendering (DIBR) is the visual presence of disocclusions in the rendered views. This poses a significant challenge when the subjective assessment of these views is utilised for evaluating the quality of the depth maps used in the rendering process. Although various techniques are available to address this challenge, they result in concealing distortions, which are directly caused by the depth map imperfections. For the purposes of depth map quality evaluation, there is a need for an approach that deals with the presence of disocclusions without having further impact on other distortions. The aim of this approach is to enable the subjective assessments of rendered views to provide results, which are more representative of the quality of the depth map used in the rendering process

No-reference depth map quality evaluation model based on depth map edge confidence measurement in immersive video applications

When it comes to evaluating perceptual quality of digital media for overall quality of experience assessment in immersive video applications, typically two main approaches stand out: Subjective and objective quality evaluation. On one hand, subjective quality evaluation offers the best representation of perceived video quality assessed by the real viewers. On the other hand, it consumes a significant amount of time and effort, due to the involvement of real users with lengthy and laborious assessment procedures. Thus, it is essential that an objective quality evaluation model is developed. The speed-up advantage offered by an objective quality evaluation model, which can predict the quality of rendered virtual views based on the depth maps used in the rendering process, allows for faster quality assessments for immersive video applications. This is particularly important given the lack of a suitable reference or ground truth for comparing the available depth maps, especially when live content services are offered in those applications. This paper presents a no-reference depth map quality evaluation model based on a proposed depth map edge confidence measurement technique to assist with accurately estimating the quality of rendered (virtual) views in immersive multi-view video content. The model is applied for depth image-based rendering in multi-view video format, providing comparable evaluation results to those existing in the literature, and often exceeding their performance

A Novel Inpainting Framework for Virtual View Synthesis

Multi-view imaging has stimulated significant research to enhance the user experience of free viewpoint video, allowing interactive navigation between views and the freedom to select a desired view to watch. This usually involves transmitting both textural and depth information captured from different viewpoints to the receiver, to enable the synthesis of an arbitrary view. In rendering these virtual views, perceptual holes can appear due to certain regions, hidden in the original view by a closer object, becoming visible in the virtual view. To provide a high quality experience these holes must be filled in a visually plausible way, in a process known as inpainting. This is challenging because the missing information is generally unknown and the hole-regions can be large. Recently depth-based inpainting techniques have been proposed to address this challenge and while these generally perform better than non-depth assisted methods, they are not very robust and can produce perceptual artefacts. This thesis presents a new inpainting framework that innovatively exploits depth and textural self-similarity characteristics to construct subjectively enhanced virtual viewpoints. The framework makes three significant contributions to the field: i) the exploitation of view information to jointly inpaint textural and depth hole regions; ii) the introduction of the novel concept of self-similarity characterisation which is combined with relevant depth information; and iii) an advanced self-similarity characterising scheme that automatically determines key spatial transform parameters for effective and flexible inpainting. The presented inpainting framework has been critically analysed and shown to provide superior performance both perceptually and numerically compared to existing techniques, especially in terms of lower visual artefacts. It provides a flexible robust framework to develop new inpainting strategies for the next generation of interactive multi-view technologies

Interaktive Raumzeitrekonstruktion in der Computergraphik

High-quality dense spatial and/or temporal reconstructions and correspondence maps from camera images, be it optical flow, stereo or scene flow, are an essential prerequisite for a multitude of computer vision and graphics tasks, e.g. scene editing or view interpolation in visual media production. Due to the ill-posed nature of the estimation problem in typical setups (i.e. limited amount of cameras, limited frame rate), automated estimation approaches are prone to erroneous correspondences and subsequent quality degradation in many non-trivial cases such as occlusions, ambiguous movements, long displacements, or low texture. While improving estimation algorithms is one obvious possible direction, this thesis complementarily concerns itself with creating intuitive, high-level user interactions that lead to improved correspondence maps and scene reconstructions. Where visually convincing results are essential, rendering artifacts resulting from estimation errors are usually repaired by hand with image editing tools, which is time consuming and therefore costly. My new user interactions, which integrate human scene recognition capabilities to guide a semi-automatic correspondence or scene reconstruction algorithm, save considerable effort and enable faster and more efficient production of visually convincing rendered images.Raumzeit-Rekonstruktion in Form von dichten räumlichen und/oder zeitlichen Korrespondenzen zwischen Kamerabildern, sei es optischer Fluss, Stereo oder Szenenfluss, ist eine wesentliche Voraussetzung für eine Vielzahl von Aufgaben in der Computergraphik, zum Beispiel zum Editieren von Szenen oder Bildinterpolation. Da sowohl die Anzahl der Kameras als auch die Bildfrequenz begrenzt sind, ist das Rekonstruktionsproblem unterbestimmt, weswegen automatisierte Schätzungen häufig fehlerhafte Korrespondenzen für nichttriviale Fälle wie Verdeckungen, mehrdeutige oder große Bewegungen, oder einheitliche Texturen enthalten; jede Bildsynthese basierend auf den partiell falschen Schätzungen muß daher Qualitätseinbußen in Kauf nehmen. Man kann nun zum einen versuchen, die Schätzungsalgorithmen zu verbessern. Komplementär dazu kann man möglichst effiziente Interaktionsmöglichkeiten entwickeln, die die Qualität der Rekonstruktion drastisch verbessern. Dies ist das Ziel dieser Dissertation. Für visuell überzeugende Resultate müssen Bildsynthesefehler bislang manuell in einem aufwändigen Nachbearbeitungsschritt mit Hilfe von Bildbearbeitungswerkzeugen korrigiert werden. Meine neuen Benutzerinteraktionen, welche menschliches Szenenverständnis in halbautomatische Algorithmen integrieren, verringern den Nachbearbeitungsaufwand beträchtlich und ermöglichen so eine schnellere und effizientere Produktion qualitativ hochwertiger synthetisierter Bilder

Visual Privacy Protection Methods: A Survey

Recent advances in computer vision technologies have made possible the development of intelligent monitoring systems for video surveillance and ambient-assisted living. By using this technology, these systems are able to automatically interpret visual data from the environment and perform tasks that would have been unthinkable years ago. These achievements represent a radical improvement but they also suppose a new threat to individual’s privacy. The new capabilities of such systems give them the ability to collect and index a huge amount of private information about each individual. Next-generation systems have to solve this issue in order to obtain the users’ acceptance. Therefore, there is a need for mechanisms or tools to protect and preserve people’s privacy. This paper seeks to clarify how privacy can be protected in imagery data, so as a main contribution a comprehensive classification of the protection methods for visual privacy as well as an up-to-date review of them are provided. A survey of the existing privacy-aware intelligent monitoring systems and a valuable discussion of important aspects of visual privacy are also provided.This work has been partially supported by the Spanish Ministry of Science and Innovation under project “Sistema de visión para la monitorización de la actividad de la vida diaria en el hogar” (TIN2010-20510-C04-02) and by the European Commission under project “caring4U - A study on people activity in private spaces: towards a multisensor network that meets privacy requirements” (PIEF-GA-2010-274649). José Ramón Padilla López and Alexandros Andre Chaaraoui acknowledge financial support by the Conselleria d'Educació, Formació i Ocupació of the Generalitat Valenciana (fellowship ACIF/2012/064 and ACIF/2011/160 respectively)

Fundus image analysis for automatic screening of ophthalmic pathologies

En los ultimos años el número de casos de ceguera se ha reducido significativamente. A pesar de este hecho, la Organización Mundial de la Salud estima que un 80% de los casos de pérdida de visión (285 millones en 2010) pueden ser evitados si se diagnostican en sus estadios más tempranos y son tratados de forma efectiva. Para cumplir esta propuesta se pretende que los servicios de atención primaria incluyan un seguimiento oftalmológico de sus pacientes así como fomentar campañas de cribado en centros proclives a reunir personas de alto riesgo. Sin embargo, estas soluciones exigen una alta carga de trabajo de personal experto entrenado en el análisis de los patrones anómalos propios de cada enfermedad. Por lo tanto, el desarrollo de algoritmos para la creación de sistemas de cribado automáticos juga un papel vital en este campo. La presente tesis persigue la identificacion automática del daño retiniano provocado por dos de las patologías más comunes en la sociedad actual: la retinopatía diabética (RD) y la degenaración macular asociada a la edad (DMAE). Concretamente, el objetivo final de este trabajo es el desarrollo de métodos novedosos basados en la extracción de características de la imagen de fondo de ojo y clasificación para discernir entre tejido sano y patológico. Además, en este documento se proponen algoritmos de pre-procesado con el objetivo de normalizar la alta variabilidad existente en las bases de datos publicas de imagen de fondo de ojo y eliminar la contribución de ciertas estructuras retinianas que afectan negativamente en la detección del daño retiniano. A diferencia de la mayoría de los trabajos existentes en el estado del arte sobre detección de patologías en imagen de fondo de ojo, los métodos propuestos a lo largo de este manuscrito evitan la necesidad de segmentación de las lesiones o la generación de un mapa de candidatos antes de la fase de clasificación. En este trabajo, Local binary patterns, perfiles granulométricos y la dimensión fractal se aplican de manera local para extraer información de textura, morfología y tortuosidad de la imagen de fondo de ojo. Posteriormente, esta información se combina de diversos modos formando vectores de características con los que se entrenan avanzados métodos de clasificación formulados para discriminar de manera óptima entre exudados, microaneurismas, hemorragias y tejido sano. Mediante diversos experimentos, se valida la habilidad del sistema propuesto para identificar los signos más comunes de la RD y DMAE. Para ello se emplean bases de datos públicas con un alto grado de variabilidad sin exlcuir ninguna imagen. Además, la presente tesis también cubre aspectos básicos del paradigma de deep learning. Concretamente, se presenta un novedoso método basado en redes neuronales convolucionales (CNNs). La técnica de transferencia de conocimiento se aplica mediante el fine-tuning de las arquitecturas de CNNs más importantes en el estado del arte. La detección y localización de exudados mediante redes neuronales se lleva a cabo en los dos últimos experimentos de esta tesis doctoral. Cabe destacar que los resultados obtenidos mediante la extracción de características "manual" y posterior clasificación se comparan de forma objetiva con las predicciones obtenidas por el mejor modelo basado en CNNs. Los prometedores resultados obtenidos en esta tesis y el bajo coste y portabilidad de las cámaras de adquisión de imagen de retina podrían facilitar la incorporación de los algoritmos desarrollados en este trabajo en un sistema de cribado automático que ayude a los especialistas en la detección de patrones anomálos característicos de las dos enfermedades bajo estudio: RD y DMAE.In last years, the number of blindness cases has been significantly reduced. Despite this promising news, the World Health Organisation estimates that 80% of visual impairment (285 million cases in 2010) could be avoided if diagnosed and treated early. To accomplish this purpose, eye care services need to be established in primary health and screening campaigns should be a common task in centres with people at risk. However, these solutions entail a high workload for trained experts in the analysis of the anomalous patterns of each eye disease. Therefore, the development of algorithms for automatic screening system plays a vital role in this field. This thesis focuses on the automatic identification of the retinal damage provoked by two of the most common pathologies in the current society: diabetic retinopathy (DR) and age-related macular degeneration (AMD). Specifically, the final goal of this work is to develop novel methods, based on fundus image description and classification, to characterise the healthy and abnormal tissue in the retina background. In addition, pre-processing algorithms are proposed with the aim of normalising the high variability of fundus images and removing the contribution of some retinal structures that could hinder in the retinal damage detection. In contrast to the most of the state-of-the-art works in damage detection using fundus images, the methods proposed throughout this manuscript avoid the necessity of lesion segmentation or the candidate map generation before the classification stage. Local binary patterns, granulometric profiles and fractal dimension are locally computed to extract texture, morphological and roughness information from retinal images. Different combinations of this information feed advanced classification algorithms formulated to optimally discriminate exudates, microaneurysms, haemorrhages and healthy tissues. Through several experiments, the ability of the proposed system to identify DR and AMD signs is validated using different public databases with a large degree of variability and without image exclusion. Moreover, this thesis covers the basics of the deep learning paradigm. In particular, a novel approach based on convolutional neural networks is explored. The transfer learning technique is applied to fine-tune the most important state-of-the-art CNN architectures. Exudate detection and localisation tasks using neural networks are carried out in the last two experiments of this thesis. An objective comparison between the hand-crafted feature extraction and classification process and the prediction models based on CNNs is established. The promising results of this PhD thesis and the affordable cost and portability of retinal cameras could facilitate the further incorporation of the developed algorithms in a computer-aided diagnosis (CAD) system to help specialists in the accurate detection of anomalous patterns characteristic of the two diseases under study: DR and AMD.En els últims anys el nombre de casos de ceguera s'ha reduït significativament. A pesar d'este fet, l'Organització Mundial de la Salut estima que un 80% dels casos de pèrdua de visió (285 milions en 2010) poden ser evitats si es diagnostiquen en els seus estadis més primerencs i són tractats de forma efectiva. Per a complir esta proposta es pretén que els servicis d'atenció primària incloguen un seguiment oftalmològic dels seus pacients així com fomentar campanyes de garbellament en centres regentats per persones d'alt risc. No obstant això, estes solucions exigixen una alta càrrega de treball de personal expert entrenat en l'anàlisi dels patrons anòmals propis de cada malaltia. Per tant, el desenrotllament d'algoritmes per a la creació de sistemes de garbellament automàtics juga un paper vital en este camp. La present tesi perseguix la identificació automàtica del dany retiniano provocat per dos de les patologies més comunes en la societat actual: la retinopatia diabètica (RD) i la degenaración macular associada a l'edat (DMAE) . Concretament, l'objectiu final d'este treball és el desenrotllament de mètodes novedodos basats en l'extracció de característiques de la imatge de fons d'ull i classificació per a discernir entre teixit sa i patològic. A més, en este document es proposen algoritmes de pre- processat amb l'objectiu de normalitzar l'alta variabilitat existent en les bases de dades publiques d'imatge de fons d'ull i eliminar la contribució de certes estructures retinianas que afecten negativament en la detecció del dany retiniano. A diferència de la majoria dels treballs existents en l'estat de l'art sobre detecció de patologies en imatge de fons d'ull, els mètodes proposats al llarg d'este manuscrit eviten la necessitat de segmentació de les lesions o la generació d'un mapa de candidats abans de la fase de classificació. En este treball, Local binary patterns, perfils granulometrics i la dimensió fractal s'apliquen de manera local per a extraure informació de textura, morfologia i tortuositat de la imatge de fons d'ull. Posteriorment, esta informació es combina de diversos modes formant vectors de característiques amb els que s'entrenen avançats mètodes de classificació formulats per a discriminar de manera òptima entre exsudats, microaneurismes, hemorràgies i teixit sa. Per mitjà de diversos experiments, es valida l'habilitat del sistema proposat per a identificar els signes més comuns de la RD i DMAE. Per a això s'empren bases de dades públiques amb un alt grau de variabilitat sense exlcuir cap imatge. A més, la present tesi també cobrix aspectes bàsics del paradigma de deep learning. Concretament, es presenta un nou mètode basat en xarxes neuronals convolucionales (CNNs) . La tècnica de transferencia de coneixement s'aplica per mitjà del fine-tuning de les arquitectures de CNNs més importants en l'estat de l'art. La detecció i localització d'exudats per mitjà de xarxes neuronals es du a terme en els dos últims experiments d'esta tesi doctoral. Cal destacar que els resultats obtinguts per mitjà de l'extracció de característiques "manual" i posterior classificació es comparen de forma objectiva amb les prediccions obtingudes pel millor model basat en CNNs. Els prometedors resultats obtinguts en esta tesi i el baix cost i portabilitat de les cambres d'adquisión d'imatge de retina podrien facilitar la incorporació dels algoritmes desenrotllats en este treball en un sistema de garbellament automàtic que ajude als especialistes en la detecció de patrons anomálos característics de les dos malalties baix estudi: RD i DMAE.Colomer Granero, A. (2018). Fundus image analysis for automatic screening of ophthalmic pathologies [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/99745TESI

Towards Predictive Rendering in Virtual Reality

The strive for generating predictive images, i.e., images representing radiometrically correct renditions of reality, has been a longstanding problem in computer graphics. The exactness of such images is extremely important for Virtual Reality applications like Virtual Prototyping, where users need to make decisions impacting large investments based on the simulated images. Unfortunately, generation of predictive imagery is still an unsolved problem due to manifold reasons, especially if real-time restrictions apply. First, existing scenes used for rendering are not modeled accurately enough to create predictive images. Second, even with huge computational efforts existing rendering algorithms are not able to produce radiometrically correct images. Third, current display devices need to convert rendered images into some low-dimensional color space, which prohibits display of radiometrically correct images. Overcoming these limitations is the focus of current state-of-the-art research. This thesis also contributes to this task. First, it briefly introduces the necessary background and identifies the steps required for real-time predictive image generation. Then, existing techniques targeting these steps are presented and their limitations are pointed out. To solve some of the remaining problems, novel techniques are proposed. They cover various steps in the predictive image generation process, ranging from accurate scene modeling over efficient data representation to high-quality, real-time rendering. A special focus of this thesis lays on real-time generation of predictive images using bidirectional texture functions (BTFs), i.e., very accurate representations for spatially varying surface materials. The techniques proposed by this thesis enable efficient handling of BTFs by compressing the huge amount of data contained in this material representation, applying them to geometric surfaces using texture and BTF synthesis techniques, and rendering BTF covered objects in real-time. Further approaches proposed in this thesis target inclusion of real-time global illumination effects or more efficient rendering using novel level-of-detail representations for geometric objects. Finally, this thesis assesses the rendering quality achievable with BTF materials, indicating a significant increase in realism but also confirming the remainder of problems to be solved to achieve truly predictive image generation

Doctor of Philosophy

dissertationBalancing the trade off between the spatial and temporal quality of interactive computer graphics imagery is one of the fundamental design challenges in the construction of rendering systems. Inexpensive interactive rendering hardware may deliver a high level of temporal performance if the level of spatial image quality is sufficiently constrained. In these cases, the spatial fidelity level is an independent parameter of the system and temporal performance is a dependent variable. The spatial quality parameter is selected for the system by the designer based on the anticipated graphics workload. Interactive ray tracing is one example; the algorithm is often selected due to its ability to deliver a high level of spatial fidelity, and the relatively lower level of temporal performance isreadily accepted. This dissertation proposes an algorithm to perform fine-grained adjustments to the trade off between the spatial quality of images produced by an interactive renderer, and the temporal performance or quality of the rendered image sequence. The approach first determines the minimum amount of sampling work necessary to achieve a certain fidelity level, and then allows the surplus capacity to be directed towards spatial or temporal fidelity improvement. The algorithm consists of an efficient parallel spatial and temporal adaptive rendering mechanism and a control optimization problem which adjusts the sampling rate based on a characterization of the rendered imagery and constraints on the capacity of the rendering system

Efficient streaming for high fidelity imaging

Researchers and practitioners of graphics, visualisation and imaging have an ever-expanding list of technologies to account for, including (but not limited to) HDR, VR, 4K, 360°, light field and wide colour gamut. As these technologies move from theory to practice, the methods of encoding and transmitting this information need to become more advanced and capable year on year, placing greater demands on latency, bandwidth, and encoding performance. High dynamic range (HDR) video is still in its infancy; the tools for capture, transmission and display of true HDR content are still restricted to professional technicians. Meanwhile, computer graphics are nowadays near-ubiquitous, but to achieve the highest fidelity in real or even reasonable time a user must be located at or near a supercomputer or other specialist workstation. These physical requirements mean that it is not always possible to demonstrate these graphics in any given place at any time, and when the graphics in question are intended to provide a virtual reality experience, the constrains on performance and latency are even tighter. This thesis presents an overall framework for adapting upcoming imaging technologies for efficient streaming, constituting novel work across three areas of imaging technology. Over the course of the thesis, high dynamic range capture, transmission and display is considered, before specifically focusing on the transmission and display of high fidelity rendered graphics, including HDR graphics. Finally, this thesis considers the technical challenges posed by incoming head-mounted displays (HMDs). In addition, a full literature review is presented across all three of these areas, detailing state-of-the-art methods for approaching all three problem sets. In the area of high dynamic range capture, transmission and display, a framework is presented and evaluated for efficient processing, streaming and encoding of high dynamic range video using general-purpose graphics processing unit (GPGPU) technologies. For remote rendering, state-of-the-art methods of augmenting a streamed graphical render are adapted to incorporate HDR video and high fidelity graphics rendering, specifically with regards to path tracing. Finally, a novel method is proposed for streaming graphics to a HMD for virtual reality (VR). This method utilises 360° projections to transmit and reproject stereo imagery to a HMD with minimal latency, with an adaptation for the rapid local production of depth maps