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In the domain of computer vision, registration is the process of calculating the transformation between a known object, called a marker, and a camera which is viewing it. Registration is the foundation for a number of applications across a range of disciplines such as augmented reality, medical imaging and robotic navigation. In the set of two dimensional planar markers, there are two classes: (1) fiducial, which are designed to be easily recognisable by computers but have little to no semantic meaning to people, and (2) natural features, which have meaning to people, but can still be registered by a computer. As computers become more powerful, natural feature markers are increasingly the more popular choice; however there are still a number of inherent problems with this class of markers. This thesis examines the most common shortcomings of natural feature markers, and proposes and evaluates solutions to these weaknesses. The work starts with a review of the existing planar registration approaches, both fiducial and natural features, with a focus on the strengths and weaknesses of each. From this review, the theory behind planar registration is discussed, from the different coordinate systems and transformations, to the computation of the registration transformation. With a foundation of planar registration, natural feature registration is decomposed into its main stages, and each stage is described in detail. This leads into a discussion of the complete natural feature registration pipeline, highlighting common issues encountered at each step, and discussing the possible solutions for each issue. A new implementation of natural feature registration called the Optical-flow Perspective Invariant Registration Augmentation (OPIRA) is proposed, which provides vast improvements in robustness to perspective, rotation and changes in scale to popular registration algorithms such as SIFT, SURF, and the Ferns classifier. OPIRA is shown to improve perspective invariance on average by 15% for SIFT, 25% for SURF and 20% for the Ferns Classifier, as well as provide complete rotation invariance for the rotation dependent implementations of these algorithms. From the investigation into problems and potential resolutions at each stage during registration, each proposed solution is evaluated empirically against an external ground truth. The results are discussed and a conclusion on the improvements gained by each proposed solution and the feasibility of use in a real natural feature registration application is drawn. Finally, some applications which use the research contained within this thesis are described, as well as some future directions for the research.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 52-54). Also available via the Internet.

Local descriptors are increasingly used for the task of object recognition because of their perceived robustness with respect to occlusions and to global geometrical deformations. We propose a performance criterion for a local descriptor based on the tradeoff between selectivity and invariance. In this paper, we evaluate several local descriptors with respect to selectivity and invariance. The descriptors that we evaluated are Gaussian derivatives up to the third order, gray image patches, and Laplacian-based descriptors with either three scales or one scale filters. We compare selectivity and invariance to several affine changes such as rotation, scale, brightness, and viewpoint. Comparisons have been made keeping the dimensionality of the descriptors roughly constant. The overall results indicate a good performance by the descriptor based on a set of oriented Gaussian filters. It is interesting that oriented receptive fields similar to the Gaussian derivatives as well as receptive fields similar to the Laplacian are found in primate visual cortex.

abstract: Although the issue of factorial invariance has received increasing attention in the literature, the focus is typically on differences in factor structure across groups that are directly observed, such as those denoted by sex or ethnicity. While establishing factorial invariance across observed groups is a requisite step in making meaningful cross-group comparisons, failure to attend to possible sources of latent class heterogeneity in the form of class-based differences in factor structure has the potential to compromise conclusions with respect to observed groups and may result in misguided attempts at instrument development and theory refinement. The present studies examined the sensitivity of two widely used confirmatory factor analytic model fit indices, the chi-square test of model fit and RMSEA, to latent class differences in factor structure. Two primary questions were addressed. The first of these concerned the impact of latent class differences in factor loadings with respect to model fit in a single sample reflecting a mixture of classes. The second question concerned the impact of latent class differences in configural structure on tests of factorial invariance across observed groups. The results suggest that both indices are highly insensitive to class-based differences in factor loadings. Across sample size conditions, models with medium (0.2) sized loading differences were rejected by the chi-square test of model fit at rates just slightly higher than the nominal .05 rate of rejection that would be expected under a true null hypothesis. While rates of rejection increased somewhat when the magnitude of loading difference increased, even the largest sample size with equal class representation and the most extreme violations of loading invariance only had rejection rates of approximately 60%. RMSEA was also insensitive to class-based differences in factor loadings, with mean values across conditions suggesting a degree of fit that would generally be regarded as exceptionally good in practice. In contrast, both indices were sensitive to class-based differences in configural structure in the context of a multiple group analysis in which each observed group was a mixture of classes. However, preliminary evidence suggests that this sensitivity may contingent on the form of the cross-group model misspecification. / Dissertation/Thesis / Ph.D. Psychology 2011

O principal objetivo deste trabalho é estudar a quantização covariante da corda bosônica e da supercorda RNS, explorando as simetrias envolvidas, ou seja, as simetrias BRST e conforme no caso da corda bosônica e as generalizações correspondentes para a corda fermiônica. Em particular, discutimos alguns aspectos perturbativos da teoria bosônica e a construção de operadores de vértice da corda fermiônica. / The main goal of this work is to study the covariant quantization of the bosonic and RNS string theories by exploiting the involved symmetries, namely, the BRST and conformal invariance for the bosonic string and the corresponding supersymmetric generalizations for the fermionic case. In particular, we discuss some perturbative aspects of the bosonic theory and the construction of vertex operators for the fermionic string.

Neste trabalho apresentamos alguns resultados da invariância conforme e da teoria de escala para sistemas finitos. Estudamos, usando tais técnicas, dois modelos estatísticos (modelos 1 e 2). Para cada modelo obtivemos a anomalia conforme e as dimensões dos operadores energia e magnetização bem como seus respectivos descendentes / In this work we show some results of conformal invariance theory and finite-size scaling. We study by using these theories two statistical mechanics models (models 1 and 2). To each model we obtained the conformal anomaly, the dimensions of energy and magnetization operators as well their respective descendents

A new method is developed for treating atoms and molecules in a magnetic field in a gauge-invariant way using the Rayleigh-Ritz energy variational principle. The energy operator depends on the vector potential which must be chosen in some gauge. In order to adapt the trial wave function to the gauge of the vector potential, the trial wave function can be multiplied by a phase factor which depends on the spatial coordinates. When the energy expectation value is minimized with respect to the phase function, the equation for charge conservation for stationary states is obtained. This equation can be solved for the phase function, and the solution used in the energy expectation value to obtain a gauge-invariant energy. The method is applicable to all quantum mechanical systems for which the variational principle can be applied. It ensures satisfaction of the charge conservation condition, a gauge-invariant energy, and the best upper bound to the ground-state energy which can be obtained for the form of trial wave function chosen.

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<p>Previous research has identified impulsive personality traits as significant risk factors for a wide range of risk-taking behavior, substance use, and clinical problems. Most work has been conducted in primarily White samples, leaving it unclear whether these patterns generalize to racial and ethnic minorities, who have higher rates of negative consequences of substance use behavior. The most widely used assessment of impulsive traits is the UPPS-P Impulsive Behavior scale, which has strong psychometric properties across demographic subgroups, such as gender and age; however, data supporting its use in racial and ethnic minorities is less well-developed. The aims of this study are to 1) examine the measurement invariance of the UPPS-P Impulsive Behavior Scale-Short Form (Cyders et al., 2014) across racial minority groups and 2) determine if impulsive personality traits differentially relate to substance use outcomes across racial groups. Participants were 1301young adults (ages 18-35, fluent in English), recruited through an online survey for both college students at a large public university and Mechanical Turk, a crowdsourcing online platform. Measurement invariance was assessed using multigroup confirmatory factor analysis. Differential validity was assessed using a structural equation modeling framework. I established model fit for each racial group (White group: RMSEA= .067, CFI= .94; Black group: RMSEA= .071, 90% CFI= .952; Asian American group: RMSEA= .073, CFI= .94; Hispanic group: RMSEA=.081, CFI=.934). Based on change in CFI/RMSEA indices, I concluded strong measurement invariance of the Short UPPS-P as a valid scale of impulsive behavior across racial groups. In the White group, findings indicated significant relationships between multiple SUPPS-P traits and alcohol and substance use. In the Asian American group, positive relationships were found between sensation and alcohol use (<i>p</i>=.015) and negative urgency and drug use (<i>p</i>=.020). I found that there were no differences in the relationships between the Short UPPS-P traits and substance use outcomes across White and the racial and ethnic groups studied (<i>p</i>’s>.06). </p>