This thesis focuses on (1) the statistical methodologies for the estimation of spatial data with outliers and (2) classification accuracy of disease diagnosis. Chapter I, Robust Estimation for Spatial Markov Random Field Models: Markov Random Field (MRF) models are useful in analyzing spatial lattice data collected from semiconductor device fabrication and printed circuit board manufacturing processes or agricultural field trials. When outliers are present in the data, classical parameter estimation techniques (e.g., least squares) can be inefficient and potentially mislead the analyst. This chapter extends the MRF model to accommodate outliers and proposes robust parameter estimation methods such as the robust M- and RA-estimates. Asymptotic distributions of the estimates with differentiable and non-differentiable robustifying function are derived. Extensive simulation studies explore robustness properties of the proposed methods in situations with various amounts of outliers in different patterns. Also provided are studies of analysis of grid data with and without the edge information. Three data sets taken from the literature illustrate advantages of the methods. Chapter II, Extending the Skill Test for Disease Diagnosis: For diagnostic tests, we present an extension to the skill plot introduced by Mozer and Briggs (2003). The method is motivated by diagnostic measures for osteoporosis in a study. By restricting the area under the ROC curve (AUC) according to the skill statistic, we have an improved diagnostic test for practical applications by considering the misclassification costs. We also construct relationships, using the Koziol-Green model and mean-shift model, between the diseased group and the healthy group for improving the skill statistic. Asymptotic properties of the skill statistic are provided. Simulation studies compare the theoretical results and the estimates under various disease rates and misclassification costs. We apply the proposed method in classification of osteoporosis data.

The goal of this thesis is the development of a tracking algorithm for populations of unmarked cancer cells that migrate in 3D in vitro gels. The tracking algorithm is intended to be a tool for analysing the motility of large population (i.e. hundreds) of cells in the context of the anti-migratory drug development and more specifically drug screening. In oncology, cancer cell migration plays pivotal roles in the spread of cancer cells from a primary tumor site to neighboring and secondary sites, i.e. the processes of tissue invasion and metastasis. Preventing such processes represents an important therapeutic approach to cancer treatment. Providing tools able to test potential anti-migratory drugs thus constitutes currently a real need in oncology therapy. The goal of drug screening in this context aims to rapidly and efficiently test the anti-migratory effects of many experimental conditions on cancer cell populations.<p>The focus in this thesis lies in two specific aspects that are important in anti-migratory drug screening: tracking cells inside an in vitro 3D environment and doing so using unmarked cells. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

Purpose of this thesis is the quality check of correct button text display on photographed monitors. These photographs contain a variety of image distortions which complicates the following image graphic element recognition. This paper outlines several possibilities to detect buttons on forms and further elaborates on the implemented detection based on contour shapes description. After buttons are found, their defects are detected subsequently. Additionally, this thesis describes an automatic identification of picture with the highest quality for documentation purposes.

This master´s thesis deals with depiction aplication of camera like sensitive element for assisting system of car. It was proposed, how find a road, a lane and a obstacle on roadways. Only one camera was aplication for it. Solution is realized by methods, that are based on color and gradient of image. It applies simple methods and methods with mathematical model. Result is sum of method and its test and comparing. Realization of my program is present at resume of this thesis.