This thesis discusses matching of 3D surfaces, in particular, their registration in a common coordinate system. This differs from object recognition in the sense that the surfaces are generally close to registration, sometimes so close that the mismatch cannot be detected on visual inspection. The surface matching algorithm, based on least squares theory, is therefore an estimation of the matching parameters, sometimes very small, which provides the most statistically accurate registration. High redundancy is achieved with the algorithm, as each point of one surface can potentially participate in the formation of an observation equation for the least squares adjustment. The algorithm minimises the separation between the surfaces. The surfaces are defined by sets of points represented by their cartesian coordinates in 3D space, without restrictions on the mode of sampling used in the capture of the data. The registration is executed without control points. Modern non-thematic sampling methods, for instance airborne laser scanning, can benefit from such an algorithm. Other applications include processes where permanent control markers cannot be used, for example, medical applications or coastal erosion. Surface matching has been used previously by a small number of people. The particular interest of this thesis, however, has been to test the accuracy and other characteristics of the matching, especially when weighting is used with the surface separations. This thesis presents and compares several weighting techniques including one technique based on the covariance function. In addition, a statistical method to model matching accuracy as a function of the density of the control surface is formulated. The method is useful to ascertain the interpolation component of the matching error. The remaining component of the error can be deducted and analysed according to the project under consideration. Examples of project might be filtering in data fusion assessment, or volume displacement in landslide analysis. The theory is developed using artificial data. This helps to isolate and analyse in turn the various characteristics of the surface matching. The thesis is then illustrated with examples involving real data sampled in Newcastle, NSW, Australia, using methods such as ALS, photogrammetry and GPS. / PhD Doctorate

Weighted graph matching is a good way to align a pair of shapesrepresented by a set of descriptive local features; the set ofcorrespondences produced by the minimum cost of matching features fromone shape to the features of the other often reveals how similar thetwo shapes are. However, due to the complexity of computing the exactminimum cost matching, previous algorithms could only run efficientlywhen using a limited number of features per shape, and could not scaleto perform retrievals from large databases. We present a contourmatching algorithm that quickly computes the minimum weight matchingbetween sets of descriptive local features using a recently introducedlow-distortion embedding of the Earth Mover's Distance (EMD) into anormed space. Given a novel embedded contour, the nearest neighborsin a database of embedded contours are retrieved in sublinear time viaapproximate nearest neighbors search. We demonstrate our shapematching method on databases of 10,000 images of human figures and60,000 images of handwritten digits.

Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television (HDTV), CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences. Motion compensated predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50-80% of the video encoding complexity. This technique has been adopted by all of the existing international video coding standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks; each target macroblock of the current frame is compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm were developed to reduce the computation complexity. This thesis focuses on two classifications: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the full search is decreased while the resolution of the predicted frames is the same as for the full search. The second is called the lossy block matching algorithm process, which reduces the computational complexity effectively but the search result’s quality is not the same as for the full search.

Este trabalho apresenta uma análise sobre um novo método de formação de imagem, utilizando aparelhos de ultra-som a elastografia. Esta técnica baseia-se no fato de que quando um meio elástico, como o tecido, é deformado por uma tensão constante e uniaxial, todos os pontos no meio possuem um nível de deformação longitudinal cujo componente principal está ao longo do eixo de deformação. Se elementos do tecido possuem um módulo elástico diferente dos demais, a deformação nestes elementos será relativamente maior ou menor. Elementos mais rígidos geralmente deformam-se menos. Desta forma, pode-se mapear e identificar estruturas com diferentes níveis de dureza. A comparação entre os mapas de RF de pré e pós-deformação foi realizada pela técnica de block matching. Esta técnica consiste em comparar regiões, ou kernels, no mapa de pré-deformação com regiões de mesmo tamanho no mapa de pós-deformação. Esta comparação é feita pela minimização de uma função custo. Nesta técnica, o tamanho do kernel, é um dos principais parâmetros para melhorar a precisão das medidas de deslocamento. O principal objetivo neste trabalho é aperfeiçoar o algoritmo de block matching visando melhorar a precisão da determinação de deslocamento em técnicas de deformação dinâmica e estática, mantendo o custo computacional baixo. Para isto, foram utilizados phantoms com e sem inclusões mais duras que o meio. Os phantoms foram submetidos a deformações estáticas e dinâmicas. Foi possível determinar o comportamento destes phantoms sob estas formas de deformação, e as faixas de kernel e funções custo que forneceram os melhores resultados. Também foram gerados elastogramas do phantom com inclusão. Estas imagens permitiram avaliar a influência dos diferentes kernels sobre a resolução dos elastogramas e a capacidade em diferenciar a lesão do tecido circundante. Comparando os elastogramas obtidos sobre deformação dinâmica, utilizando os kernels que apresentaram o melhor desempenho, com as respectivas imagens em modo B, pôde-se observar que a inclusão estava clara e bem delimitada. / This work provides an analysis about a new method for image formation using ultrasound devices elastography. This technique is based on the fact that when an elastic medium, as the tissue, is deformed under a constant and directional stress, all the points in the medium have a deformation level whose main component is along the deformation axis. If tissues elements have different elastic modules, the deformation in these elements will be higher or lower. Normally harder elements have lower deformations. In this way, one can detect and identify structures with different elastic levels. The comparisons between the pre and post-deformation RF maps were done by the block matching technique. This technique is based on the comparison of regions, or kernels, in the pre-deformation maps with regions of the same size in the post-deformation map. This is done by the minimization of a cost function. In this technique, the kernel size is one of the most important parameters to obtain better resolution and precision in the displacement measurements. The goal of this work is to optimize the block matching algorithm to improve the displacement estimates precision in both dynamic and static deformations, while keeping a low computational cost. To obtain this, we used phantoms with and without inclusions harder than the medium. These phantoms were submitted to both static and dynamic deformations. It was possible to estimate the behavior of these phantoms under these deformations, and the kernel range and cost functions that provided the best results. Also, we generated the elastograms of the phantom with the inclusion. These images allowed us to evaluate the influence of the different kernel sizes under the elastograms resolution and their capability in differentiate the lesion from the embedding tissue. Comparing the elastograms obtained under dynamic deformation that had the best performance, with the B mode images, we could conclude that the inclusion was well delimited and clear.

Name matching plays a vital and crucial role in many applications. They are for example used in information retrieval or deduplication systems to do comparisons among names to match them together or to find the names that refer to identical objects, persons, or companies. Since names in each application are subject to variations and errors that are unavoidable in any system and because of the importance of name matching, so far many algorithms have been developed to handle matching of names. These algorithms consider the name variations that may happen because of spelling, pattern or phonetic modifications. However most existing methods were developed for use with the English language and so cover the characteristics of this language. Up to now no specific one has been designed and implemented for the Persian language. The purpose of this thesis is to present a name matching algorithm for Persian. In this project, after consideration of all major algorithms in this area, we selected one of the basic methods for name matching that we then expanded to make it work particularly well for Persian names. This proposed algorithm, called Persian Edit Distance Algorithm or shortly PEDA, was built based on the characteristics of the Persian language and it compares Persian names with each other on three levels: phonetic similarity, character form similarity and keyboard distance, in order to give more accurate results for Persian names. The algorithm gets Persian names as its input and determines their similarity as a percentage in the output. In this thesis three series of experiments have been accomplished in order to evaluate the proposed algorithm. The f-measure average shows a value of 0.86 for the first series and a value of 0.80 for the second series results. The first series of experiments have been repeated with Levenshtein as well, and have 33.9% false negatives on average while PEDA has a false negative average of 6.4%. The third series of experiments shows that PEDA works well for one edit, two edits and three edits with true positive average values of 99%, 81%, and 69% respectively.

abstract: In this paper, I study many-to-one matching markets in a dynamic framework with the following features: Matching is irreversible, participants exogenously join the market over time, each agent is restricted by a quota, and agents are perfectly patient. A form of strategic behavior in such markets emerges: The side with many slots can manipulate the subsequent matching market in their favor via earlier matchings. In such a setting, a natural question arises: Is it possible to analyze a dynamic many-to-one matching market as if it were either a static many-to-one or a dynamic one-to-one market? First, I provide sufficient conditions under which the answer is yes. Second, I show that if these conditions are not met, then the early matchings are "inferior" to the subsequent matchings. Lastly, I extend the model to allow agents on one side to endogenously decide when to join the market. Using this extension, I provide a rationale for the small amount of unraveling observed in the United States (US) medical residency matching market compared to the US college-admissions system. Micro Finance Institutions (MFIs) are designed to improve the welfare of the poor. Group lending with joint liability is the standard contract used by these institutions. Such a contract performs two roles: it affects the composition of the groups that form, and determines the properties of risk-sharing among their members. Even though the literature suggests that groups consist of members with similar characteristics, there is evidence also of groups with heterogeneous agents. The underlying reason is that the literature lacked the risk-sharing behavior of the agents within a group. This paper develops a model of group lending where agents form groups, obtain capital from the MFI, and share risks among themselves. First, I show that joint liability introduces inefficiency for risk-averse agents. Moreover, the composition of the groups is not always homogeneous once risk-sharing is on the table. / Dissertation/Thesis / Doctoral Dissertation Economics 2020

abstract: In the first chapter, I study the two-sided, dynamic matching problem that occurs in the United States (US) foster care system. In this market, foster parents and foster children can form reversible foster matches, which may disrupt, continue in a reversible state, or transition into permanency via adoption. I first present an empirical analysis that yields four new stylized facts related to match transitions of children in foster care and their exit through adoption. Thereafter, I develop a two-sided dynamic matching model with five key features: (a) children are heterogeneous (with and without a disability), (b) children must be foster matched before being adopted, (c) children search for parents while foster matched to another parent, (d) parents receive a smaller per-period payoff when adopting than fostering (capturing the presence of a financial penalty on adoption), and (e) matches differ in their quality. I use the model to derive conditions for the stylized facts to arise in equilibrium and carry out predictions regarding match quality. The main insight is that the intrinsic disadvantage (being less preferred by foster parents) faced by children with a disability exacerbates due to the penalty. Moreover, I show that foster parents in high-quality matches (relative to foster parents in low-quality matches) might have fewer incentives to adopt. In the second chapter, I study the Minnesota's 2015 Northstar Care Program which eliminated the adoption penalty (i.e., the decrease in fostering-based financial transfers associated with adoption) for children aged six and older, while maintaining it for children under age six. Using a differences-in-differences estimation strategy that controls for a rich set of covariates, I find that parents were responsive to the change in direct financial payments; the annual adoption rate of older foster children (aged six to eleven) increased by approximately 8 percentage points (24% at the mean) as a result of the program. I additionally find evidence of strategic adoption behavior as the adoption rate of younger children temporarily increased by 9 percentage points (23% at the mean) while the adoption rate of the oldest children (aged fifteen) temporarily decreased by 9 percentage points (65% at the mean) in the year prior to the program's implementation. / Dissertation/Thesis / Doctoral Dissertation Economics 2019

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The Generalized Matching Equation (GME) has been utilized by many behavioral researchers as a model of operant choice, especially in elite sports. In this instance, the GME was used to analyze the penalty-kick selections (e.g., shoot right or shoot left) of nine elite international soccer players. The analysis of archival data from in-game decisions demonstrated the presence of matching across participants and within a naturalistic setting. Undermatching consistent with previous laboratory and non-laboratory research on the matching law was also observed. The GME accounted for a majority of variance in penalty-kick selection, with minimal bias in responding. The players’ choices did not demonstrate clear preferences for shooting at either side of the goal, but did expose trends in individual responding worth noting. While the results extend the research literature regarding the applicability of GME to professional sports, future research should investigate matching relationships through analyzing larger sample sizes, team-wide responses, and team outcomes. / Applied Behavioral Analysis

This study applied the generalized matching equation (GME) to pitch selection in MLB during the 2016 regular season. The GME was used to evaluate the pitch selection of 21 groups of pitchers as well as 144 individual pitchers. The GME described pitch selection well for four of the 21 pitching groups and 32 of the 144 individual pitchers. Of the remaining groups and individual pitchers, behavior may be explained by rule following behavior or be impacted by distant reinforcers such as salary. All 21 groups demonstrated a bias for fastballs as well as 119 of the 144 individual pitchers. The results extend the use of the GME to natural contexts and suggest an alternative view to evaluating pitchers. / Applied Behavioral Analysis