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A key problem in model-based object recognition is selection, namely, the problem of isolating regions in an image that are likely to come from a single object. This isolation can be either based solely on image data (data-driven) or can incorporate the knowledge of the model object (model-driven). In this paper we present an approach that exploits the property of closely-spaced parallelism between lines on objects to achieve data and model-driven selection. Specifically, we present a method of identifying groups of closely-spaced parallel lines in images that generates a linear number of small-sized and reliable groups thus meeting several of the desirable requirements of a grouping scheme for recognition. The line groups generated form the basis for data and model-driven selection. Data-driven selection is achieved by selecting salient line groups as judged by a saliency measure that emphasizes the likelihood of the groups coming from single objects. The approach to model-driven selection, on the other hand, uses the description of closely-spaced parallel line groups on the model object to selectively generate line groups in the image that are likely to eb the projections of the model groups under a set of allowable transformations and taking into account the effect of occlusions, illumination changes, and imaging errors. We then discuss the utility of line groups-based selection in the context of reducing the search involved in recognition, both as an independent selection mechanism, and when used in combination with other cues such as color. Finally, we present results that indicate a vast improvement in the performance of a recognition system that is integrated with parallel line groups-based selection.

Quantifying the behavior of complex systems arguably presents the common ¡°hard¡±problem across the physical, biological, social, economic sciences. Individual-based or agent-based models have proved useful in a variety of different real world systems: from the physical, biological, medical domains through to social and even financial domains. There are many different models in each of these fields, each with their own particular assumptions, strengths and weaknesses for particular application areas. However, there is a lack of minimal model analysis in which both numerical and analytic results can be obtained, and hence allowing different application domains to be analyzed on a common footing. This thesis focuses on a few simple, yet highly non-trivial, minimal models of a population of interacting objects (so-called agents) featuring internal dynamical grouping. In addition to analyzing these models, I apply them to a number of distinct real world systems. Both the numerical and analytical results suggest that these simple models could be key factors in explaining the overall collective behavior and emergent properties in a wide range of real world complex systems. In particular, I study variants of a particular model (called the EZ model) in order to explain the attrition time in modern conflicts, and the evolution of contagion phenomena in such a dynamically evolving population. I also study and explain the empirical data obtained for online guilds and offline gangs, leading to a team-based model which captures the common quantitative features of the data. I then move on to develop a resource competition model (i.e. the so-called El Farol model) and apply it to the carbon emissions market, mapping the different market factors into model parameters which enable me to explore the potential market behaviors under a variety of scenarios.

Thesis (Ph. D.)--University of Michigan, 1925. / Without thesis note. Bibliography: p. 93-96.

Thesis--Catholic Univ. of America. / Bibliography: p. 55-57.

Thesis--University of Wisconsin--Madison. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 205-214).

Thesis (M.S.)--University of Wisconsin--Madison, 1984. / Typescript. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaf 51).

The purpose of the present study was to investigate the effect of heterogeneous grouping and homogeneous grouping in the language development of severely retarded students. A further purpose was to determine how beneficial either of the grouping procedures would be for this level of student.

Ullman (1979) has proposed a measurement metric, which he termed “affinity." He described affinity as a certain similarity measure between successively presented surfaces as it affects the perception of apparent motion between the surfaces. Later, the concept of “affinity” has been extended; it entails that how the perception of motion within a surface is affected by its grouping strength with adjacent surfaces (Hock and Nichols, 2012). It has been found that the more attributes, that are shared by the adjacent surfaces, the greater the likelihood of their being grouped together. However, Ullman (1979) suggested that the relative affinities of pairs of surfaces could determine the solutions for the motion correspondence problem (when more than one motion path is possible). However, it has remained unknown whether the effects of affinity on solutions to the correspondence problem are due to its effects on a single surface apparent motion strength or pre-selection biases; i.e., the top-down effects of perceptual grouping favoring the perception of motion in one direction as opposed to other competing directions. In the current study, it has been confirmed that motion within a surface is affected by its affinity with adjacent surfaces. The current study also confirmed that affinity has a small, but significant effect on motion strength when motion surfaces are presented in a single surface apparent motion configuration, evidence for top-down effects in which motion strength can be affected by affinity. In motion correspondence problem, affinity affects the perceived motion direction due to competition is consistent with the solution to the motion correspondence problem being affected by the relative affinity-determined strength of competing motion signals. But it is seen that there is strong affinity is due to preselection identity biases. To conclude, in motion correspondence problem, stronger motion is perceived between the two similar surfaces is due to pre-selection biases resulting from the perceptual grouping of surfaces with the greatest affinity; i.e., the top-down effects favoring the perception of motion in one direction as opposed to other competing directions. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection