Photorealistic rendering focuses on creating images with a computer that imitates pictures of reallife scenes as faithfully as possible. To achieve this, rendering algorithms require incorporating accurate modeling of how light interacts with various types of matter. For most objects, this model needs to account for the scattering of the light rays. However, this model falls short when rendering objects of sizes smaller or comparable to the wavelength of the incident light. In this case, new phenomena such as diffraction or interference are observed and have been characterized in optics. Digital rendering of those phenomena involve different light representations than the approximate light ray optics properties traditionally used in rendering. A first part of this work has been dedicated to creating analytical models to account for appearance phenomena which occur when light is interacting with small objects, namely, hair fibers, thin film coatings and quantum dots. A second part of this work focuses on measured material appearance models and how to find a parametrization over the appearance which can be used for editing.

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The clinical signs that a physician can identify from the appearance of a patient represent an important source of information, upon which the diagnostic decision is nominally based. Most of the research in medical education emphasizes the organization of medical knowledge or the reasoning processes based on these signs. This emphasis carries the implicit assumptions that identifying features is not the major problem and that evaluation of the clinical signs occurs largely independently of consideration of the diagnosis. However, there is accumulating evidence to suggest that the identification of these clinical signs can be influenced by the diagnosis being evaluated. The studies in this thesis contribute to this body of research by investigating the underlying processes by which the diagnosis being considered influences feature identification. Participants in these experiments were asked to identify the clinical signs from photographs of patients or electrocardiogram strips after having been biased towards the correct or an alternate diagnosis. It was found that the availability of a diagnosis served both to change the probability of reporting relevant clinical signs as well as to influence the identification of ambiguous signs. Manipulating the credibility of the suggested diagnosis, subsequently suggesting a second diagnosis, or decreasing the size of the pool of alternatives available to the diagnostician had a large impact on diagnostic conclusions, but produced relatively small effects on the features reported. These results suggest that changing the degree of focus that a clinician places on the suggested diagnosis has a small effect on the identification of the features by comparison to the substantial effect of merely suggesting a diagnosis. Furthermore, it was found that the subsequent suggestion of a competing diagnosis did not lead to a reinterpretation of the data. This indicates that once clinicians have seen the evidence one way, they are unlikely to see and label it differently. The implication of these findings for research on medical decision making, the mental organization of medical categories, as well as medical education are discussed. / Thesis / Doctor of Philosophy (PhD)

This thesis is a result of a survey of elementary school principals at schools with grades kindergarten through 5th in Virginia to determine whether and how attitudes of principals influence vegetation levels on school grounds. The research also looked to find other contributing factors. Principals' educational backgrounds and age proved to be non-influencing factors. However, gender did influence school grounds with women being more likely to administer school grounds with fewer trees and tree plantings than men. Levels of tree vegetation and tree plantings proved to be highly influential in satisfaction with appearance of school grounds. Non-urban schools were more likely to have higher levels of vegetation than urban schools. Parental, service club, and local business involvement are linked together in respondents' satisfaction with each influencing the perception of the others. Analyses of these results can help groups target schools for grounds improvement projects. By understanding what effect principals have on their school grounds steps can be taken at the level of individual schools, school boards, and communities to help improve school grounds. / Master of Science

Yes / This paper seeks to understand leaders as material presences. Leadership theory has traditionally explored leaders as sites of disembodied traits, characteristics and abilities. Our qualitative, mixed method study suggests that managers charged with the tasks of leadership operate within a very different understanding. Their endogenous or lay theory understands leadership as physical, corporeal and visible, and as something made manifest through leaders’ material presence. This theory-in-practice holds that leadership qualities are signified by the leader’s physical appearance: the good leader must look the part. Actors consequently work on their own appearance to present an image of themselves as leader. They thus offer a fundamental challenge to dominant exogenous, or academic, theories of leadership. To understand the unspoken assumptions that underpin the lay theory of leadership as material presence, we interrogate it using the new materialist theory of Karen Barad and the object relations theory of Christopher Bollas. This illuminates the lay theory’s complexities and sophisticated insights. In academic terms it offers a theory of how sentient and non-sentient actors intra-act and performatively constitute leadership through complex entanglements that enact and circulate organizational and leadership norms. The paper’s contribution is thus a theory of leadership micro-dynamics in which the leader is materialised through practices of working on a corporeal self for presentation to both self and others.

Yes / N/A

Although humanoid robots are being designed to assist people in various tasks, there remain gaps in our understanding of the perceptions that humanoid faces evoke in the user. Understanding user perceptions would help design robots that are better suited for the target user group. Younger and older adults’ preferences for robot appearance were assessed out of three levels of human-likeness. In general, people perceived a mixed human-robot appearance less favorably compared to highly human and highly robotic appearances. Additionally the nature of task also influenced people’s overall perceptions of robots. Robots were most positively evaluated for assistance with chores and less positively for personal care and decision-making. Moreover, task and robot humanness had an interactive effect on people’s likability, trust, and perceived usefulness toward robots. Age-related differences in preferences of robot humanness were also observed. Older adults showed a higher inclination toward human-looking appearance of robots whereas younger adults’ preferences were more distributed across the levels of humanness. An appearance with mixed human-robot features was more likely to be rejected by older adults than by younger adults, and the difference was most striking for a decision-making task. Besides the humanness of the robot face, perceptions of robot appearances were also influenced by factors such as robot gender, specific facial features/aesthetics, expressiveness, perceived personality, and perceived capability. Future studies should measure the relative weight of these different factors in the formation of perceptions, both at a global level and at a task-specific level.

Physically-based rendering in Computer Graphics requires the knowledge of material properties other than 3D shapes, textures and colors, in order to solve the rendering equation. A number of material models have been developed, since no model is currently able to reproduce the full range of available materials. Although only few material models have been widely adopted in current rendering systems, the lack of standardisation causes several issues in the 3D modelling workflow, leading to a heavy tool dependency of material appearance. In industry, final decisions about products are often based on a virtual prototype, a crucial step for the production pipeline, usually developed by a collaborations among several departments, which exchange data. Unfortunately, exchanged data often tends to differ from the original, when imported into a different application. As a result, delivering consistent visual results requires time, labour and computational cost. This thesis begins with an examination of the current state of the art in material appearance representation and capture, in order to identify a suitable strategy to tackle material appearance consistency. Automatic solutions to this problem are suggested in this work, accounting for the constraints of real-world scenarios, where the only available information is a reference rendering and the renderer used to obtain it, with no access to the implementation of the shaders. In particular, two image-based frameworks are proposed, working under these constraints. The first one, validated by means of perceptual studies, is aimed to the remapping of BRDF parameters and useful when the parameters used for the reference rendering are available. The second one provides consistent material appearance across different renderers, even when the parameters used for the reference are unknown. It allows the selection of an arbitrary reference rendering tool, and manipulates the output of other renderers in order to be consistent with the reference.

In the modern automotive industry, more and more manufacturers recognise that vehicle paint appearance makes an important contribution to customer satisfaction. Attractive appearance has become one of the important factors for customers in making a decision to purchase a car. Objective measurement of the quality of autobody paint appearance, as perceived by the customer, in a repeatable, reproducible, continuous scale manner is an important requirement for improving the paint appearance. It can provide car manufacturers a standard reference to evaluate the quality of the paint appearance. This thesis mainly deals with the measurement of paint appearance quality in the automotive industry by investigating, identifying and developing measurement methods in this area. First of all, the 'state of the art' in the area of paint appearance measurement was presented, which summarised the concept of appearance, models, attributes and definitions. To further identify the parameters and instruments used in the automotive industry, a round robin test was launched to perform visual assessment and instrument measurements on a set of panels in some European car manufacturers. A summary of the correlation found between measurable parameters and visual assessment provided the basis of the further work. Based on the literature survey and round robin test results, the next work is mainly concentrated on the two most important parameters, 'orange peel' and 'metal texture effect', how to separate and evaluate them. Digital signal processing technique, FFT and Filtering, have been employed to separate them and a set of measures have been provided for evaluation. At the same time, the technique for texture pattern recognition was introduced to evaluate the texture effect when a fine texture comparison was needed. A set of computable textural parameters based on grey-tone spatial-dependence matrices gives good correlation directly corresponding to visual perception. To resolve the overall appearance modelling problem, two novel and more powerful modelling tools, artificial neural networks and fuzzy logic, are introduced to model the overall appearance. The test results showed that both of them are able to reflect the correlation between overall appearance and the major parameters measured from a painted surface. Finally, an integrated measurement system, 'Smart Appearance', was developed using the image processing techniques and the artificial neural network model. The implement results show that this system can measure the major attributes of paint appearance and provide an overall appearance index corresponding to human visual perception. This system is helpful to product quality control on car body paint. It also could be used on the paint production line for dynamic measurement.

Of the many systems of social organization which members of US society use to categorize other members, gender is one of the most important. The gender system operates to place members of US society into categories, and then allocate labor and resources to those members on the basis of their category membership. In order to better understand the gender system, this study examines the methods by which members of US society use the gender system to place other members into a gender category. First, full facial photographs were taken of a group of participants of varying gender, race, ethnicity, and age. Then, parts of each participant's face were isolated digitally and shown to a second group of participants. This second group was asked to identify the sex and/or gender of the individual in the image, indicate how confident they were in this identification, and then write a brief explanation for why they identified the individual in the image as they did. The analysis conducted by this study supports three findings. First, though the gender categories "male" and "female" are still widely predominant, other categories such as "genderqueer" are seeing use as well. Second, the mouth and lips tend to be seen as more important indicators of gender than other facial features. Finally, while the race and gender category membership of the member doing the categorizing has little or no interaction with the gender categorization process, the race and gender category membership of the member being categorized does have a significant interaction.