Perceptually relevant browsing environments for large texture databases

This thesis describes the development of a large database of texture stimuli, the production of a similarity matrix re ecting human judgements of similarity about the database, and the development of three browsing models that exploit structure in the perceptual information for navigation. Rigorous psychophysical comparison experiments are carried out and the SOM (Self Organising Map) found to be the fastest of the three browsing models under examination. We investigate scalable methods of augmenting a similarity matrix using the SOM browsing environment to introduce previously unknown textures. Further psychophysical experiments reveal our method produces a data organisation that is as fast to navigate as that derived from the perceptual grouping experiments.Engineering and Physical Sciences Research Council (EPSRC

Stereoscopic viewing, roughness and gloss perception

This thesis presents a novel investigation into the effect stereoscopic vision has upon the strength of perceived gloss on rough surfaces. We demonstrate that in certain cases disparity is necessary for accurate judgements of gloss strength. We first detail the process we used to create a two-level taxonomy of property terms, which helped to inform the early direction of this work, before presenting the eleven words which we found categorised the property space. This shaped careful examination of the relevant literature, leading us to conclude that most studies into roughness, gloss, and stereoscopic vision have been performed with unrealistic surfaces and physically inaccurate lighting models. To improve on the stimuli used in these earlier studies, advanced offline rendering techniques were employed to create images of complex, naturalistic, and realistically glossy 1/fβ noise surfaces. These images were rendered using multi-bounce path tracing to account for interreflections and soft shadows, with a reflectance model which observed all common light phenomena. Using these images in a series of psychophysical experiments, we first show that random phase spectra can alter the strength of perceived gloss. These results are presented alongside pairs of the surfaces tested which have similar levels of perceptual gloss. These surface pairs are then used to conclude that naïve observers consistently underestimate how glossy a surface is without the correct surface and highlight disparity, but only on the rougher surfaces presented

Learning from seismic data to characterize subsurface volumes

The exponential growth of collected data from seismic surveys makes it impossible for interpreters to manually inspect, analyze and annotate all collected data. Deep learning has proved to be a potential mechanism to overcome big data problems in various computer vision tasks such as image classification and semantic segmentation. However, the applications of deep learning are limited in the field of subsurface volume characterization due to the limited availability of consistently-annotated seismic datasets. Obtaining annotations of seismic data is a labor-intensive process that requires field knowledge. Moreover, seismic interpreters rely on the few direct high-resolution measurements of the subsurface from well-logs and core data to confirm their interpretations. Different interpreters might arrive at different valid interpretations of the subsurface, all of which are in agreement with well-logs and core data. Therefore, to successfully utilize deep learning for subsurface characterization, one must address and circumvent the lack or shortage of consistent annotated data. In this dissertation, we introduce a learning-based physics-guided subsurface volume characterization framework that can learn from limited inconsistently-annotated data. The introduced framework integrates seismic data and the limited well-log data to characterize the subsurface at a higher-than-seismic resolution. The introduced framework takes into account the physics that governs seismic data to overcome noise and artifacts that are often present in the data. Integrating a physical model in deep-learning frameworks improves their generalization ability beyond the training data. Furthermore, the physical model enables deep networks to learn from unlabeled data, in addition to a few annotated examples, in a semi-supervised learning scheme. Applications of the introduced framework are not limited to subsurface volume characterization, it can be extended to other domains in which data represent a physical phenomenon and annotated data is limited.Ph.D

Communication of Digital Material Appearance Based on Human Perception

Im alltägliche Leben begegnen wir digitalen Materialien in einer Vielzahl von Situationen wie beispielsweise bei Computerspielen, Filmen, Reklamewänden in zB U-Bahn Stationen oder beim Online-Kauf von Kleidungen. Während einige dieser Materialien durch digitale Modelle repräsentiert werden, welche das Aussehen einer bestimmten Oberfläche in Abhängigkeit des Materials der Fläche sowie den Beleuchtungsbedingungen beschreiben, basieren andere digitale Darstellungen auf der simplen Verwendung von Fotos der realen Materialien, was zB bei Online-Shopping häufig verwendet wird. Die Verwendung von computer-generierten Materialien ist im Vergleich zu einzelnen Fotos besonders vorteilhaft, da diese realistische Erfahrungen im Rahmen von virtuellen Szenarien, kooperativem Produkt-Design, Marketing während der prototypischen Entwicklungsphase oder der Ausstellung von Möbeln oder Accesoires in spezifischen Umgebungen erlauben. Während mittels aktueller Digitalisierungsmethoden bereits eine beeindruckende Reproduktionsqualität erzielt wird, wird eine hochpräzise photorealistische digitale Reproduktion von Materialien für die große Vielfalt von Materialtypen nicht erreicht. Daher verwenden viele Materialkataloge immer noch Fotos oder sogar physikalische Materialproben um ihre Kollektionen zu repräsentieren. Ein wichtiger Grund für diese Lücke in der Genauigkeit des Aussehens von digitalen zu echten Materialien liegt darin, dass die Zusammenhänge zwischen physikalischen Materialeigenschaften und der vom Menschen wahrgenommenen visuellen Qualität noch weitgehend unbekannt sind. Die im Rahmen dieser Arbeit durchgeführten Untersuchungen adressieren diesen Aspekt. Zu diesem Zweck werden etablierte digitalie Materialmodellen bezüglich ihrer Eignung zur Kommunikation von physikalischen und sujektiven Materialeigenschaften untersucht, wobei Beobachtungen darauf hinweisen, dass ein Teil der fühlbaren/haptischen Informationen wie z.B. Materialstärke oder Härtegrad aufgrund der dem Modell anhaftenden geometrische Abstraktion verloren gehen. Folglich wird im Rahmen der Arbeit das Zusammenspiel der verschiedenen Sinneswahrnehmungen (mit Fokus auf die visuellen und akustischen Modalitäten) untersucht um festzustellen, welche Informationen während des Digitalisierungsprozesses verloren gehen. Es zeigt sich, dass insbesondere akustische Informationen in Kombination mit der visuellen Wahrnehmung die Einschätzung fühlbarer Materialeigenschaften erleichtert. Eines der Defizite bei der Analyse des Aussehens von Materialien ist der Mangel bezüglich sich an der Wahnehmung richtenden Metriken die eine Beantwortung von Fragen wie z.B. "Sind die Materialien A und B sich ähnlicher als die Materialien C und D?" erlauben, wie sie in vielen Anwendungen der Computergrafik auftreten. Daher widmen sich die im Rahmen dieser Arbeit durchgeführten Studien auch dem Vergleich von unterschiedlichen Materialrepräsentationen im Hinblick auf. Zu diesem Zweck wird eine Methodik zur Berechnung der wahrgenommenen paarweisen Ähnlichkeit von Material-Texturen eingeführt, welche auf der Verwendung von Textursyntheseverfahren beruht und sich an der Idee/dem Begriff der geradenoch-wahrnehmbaren Unterschiede orientiert. Der vorgeschlagene Ansatz erlaubt das Überwinden einiger Probleme zuvor veröffentlichter Methoden zur Bestimmung der Änhlichkeit von Texturen und führt zu sinnvollen/plausiblen Distanzen von Materialprobem. Zusammenfassend führen die im Rahmen dieser Dissertation dargestellten Inhalte/Verfahren zu einem tieferen Verständnis bezüglich der menschlichen Wahnehmung von digitalen bzw. realen Materialien über unterschiedliche Sinne, einem besseren Verständnis bzgl. der Bewertung der Ähnlichkeit von Texturen durch die Entwicklung einer neuen perzeptuellen Metrik und liefern grundlegende Einsichten für zukünftige Untersuchungen im Bereich der Perzeption von digitalen Materialien.In daily life, we encounter digital materials and interact with them in numerous situations, for instance when we play computer games, watch a movie, see billboard in the metro station or buy new clothes online. While some of these virtual materials are given by computational models that describe the appearance of a particular surface based on its material and the illumination conditions, some others are presented as simple digital photographs of real materials, as is usually the case for material samples from online retailing stores. The utilization of computer-generated materials entails significant advantages over plain images as they allow realistic experiences in virtual scenarios, cooperative product design, advertising in prototype phase or exhibition of furniture and wearables in specific environments. However, even though exceptional material reproduction quality has been achieved in the domain of computer graphics, current technology is still far away from highly accurate photo-realistic virtual material reproductions for the wide range of existing categories and, for this reason, many material catalogs still use pictures or even physical material samples to illustrate their collections. An important reason for this gap between digital and real material appearance is that the connections between physical material characteristics and the visual quality perceived by humans are far from well-understood. Our investigations intend to shed some light in this direction. Concretely, we explore the ability of state-of-the-art digital material models in communicating physical and subjective material qualities, observing that part of the tactile/haptic information (eg thickness, hardness) is missing due to the geometric abstractions intrinsic to the model. Consequently, in order to account for the information deteriorated during the digitization process, we investigate the interplay between different sensing modalities (vision and hearing) and discover that particular sound cues, in combination with visual information, facilitate the estimation of such tactile material qualities. One of the shortcomings when studying material appearance is the lack of perceptually-derived metrics able to answer questions like "are materials A and B more similar than C and D?", which arise in many computer graphics applications. In the absence of such metrics, our studies compare different appearance models in terms of how capable are they to depict/transmit a collection of meaningful perceptual qualities. To address this problem, we introduce a methodology to compute the perceived pairwise similarity between textures from material samples that makes use of patch-based texture synthesis algorithms and is inspired on the notion of Just-Noticeable Differences. Our technique is able to overcome some of the issues posed by previous texture similarity collection methods and produces meaningful distances between samples. In summary, with the contents presented in this thesis we are able to delve deeply in how humans perceive digital and real materials through different senses, acquire a better understanding of texture similarity by developing a perceptually-based metric and provide a groundwork for further investigations in the perception of digital materials

Perceptual texture similarity estimation

This thesis evaluates the ability of computational features to estimate perceptual texture similarity. In the first part of this thesis, we conducted two evaluation experiments on the ability of 51 computational feature sets to estimate perceptual texture similarity using two differ-ent evaluation methods, namely, pair-of-pairs based and retrieval based evaluations. These experiments compared the computational features to two sets of human derived ground-truth data, both of which are higher resolution than those commonly used. The first was obtained by free-grouping and the second by pair-of-pairs experiments. Using these higher resolution data, we found that the feature sets do not perform well when compared to human judgements. Our analysis shows that these computational feature sets either (1) only exploit power spectrum information or (2) only compute higher order statistics (HoS) on, at most, small local neighbourhoods. In other words, they cannot capture aperiodic, long-range spatial relationships. As we hypothesise that these long-range interactions are important for the human perception of texture similarity we carried out two more pair-of-pairs ex-periments, the results of which indicate that long-range interactions do provide humans with important cues for the perception of texture similarity. In the second part of this thesis we develop new texture features that can encode such data. We first examine the importance of three different types of visual information for human perception of texture. Our results show that contours are the most critical type of information for human discrimination of textures. Finally, we report the development of a new set of contour-based features which performed well on the free-grouping data and outperformed the 51 feature sets and another contour type feature set with the pair-of-pairs data