Medical image analysis is a ubiquitous and essential part of modem health care. A crucial first step to this is segmentation, which is often complicated by many factors including subject diversity, pathology, noise corruption, and poor image resolution. Traditionally, manual tracing by experts was done. While considered accurate, this process is time consuming and tedious, especially when performed slice-by-slice on three-dimensional (3D) images over large datasets or on two-dimensional (2D) but topologically complicated images such as a retinography. On the other hand, fully-automated methods are typically faster, but work best with data-dependent, carefully tuned parameters and still require user validation and refinement. This thesis contributes to the field of medical image segmentation by proposing a highly-automated, interactive approach that effectively merges user knowledge and efficient computing. To this end, our work focuses on graph-based methods and offer globally optimal solutions. First, we present a novel method for 3D segmentation based on a 3D Livewire approach. This approach is an extension of the 2D Livewire framework, and this method is capable of handling objects with large protrusions, concavities, branching, and complex arbitrary topologies. Second, we propose a method for efficiently segmenting 2D vascular networks, called ‘Live-Vessel’. Live-Vessel simultaneously extracts vessel centrelines and boundary points, and globally optimizes over both spatial variables and vessel radius. Both of our proposed methods are validated on synthetic data, real medical data, and are shown to be highly reproducible, accurate, and efficient. Also, they were shown to be resilient to high amounts of noise and insensitive to internal parameterization. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

The evolution of technology in the textile industry reached the design of embroidery patterns for machine embroidery. In order to create quality designs the shapes to be embroidered need to be segmented into regions that define different parts. One of the objectives of our research is to develop a method to automatically segment the shapes and by doing so making the process faster and easier. Shape analysis is necessary to find a suitable method for this purpose. It includes the study of different ways to represent shapes. In this thesis we focus on shape representation through its skeleton. We make use of a shape's skeleton and the shape's boundary through the so-called feature transform to decide how to segment a shape and where to place the segment boundaries. The direction of stitches is another important specification in an embroidery design. We develop a technique to select the stitch orientation by defining direction lines using the skeleton curves and information from the boundary. We compute the intersections of segment boundaries and direction lines with the shape boundary for the final definition of the direction line segments. We demonstrate that our shape segmentation technique and the automatic placement of direction lines produce sufficient constrains for automated embroidery designs. We show examples for lettering, basic shapes, as well as simple and complex logos.

The segmentation of medical images is a difficult task due to the inhomogeneous intensity variations that occurs during digital image acquisition, the complicated shape of the object, and the medical expert’s lack of semantic knowledge. Automated segmentation algorithms work well for some medical images, but no algorithm has been general enough to work for all medical images. In practice, most of the time the segmentation results are corrected by the experts before the actual use. In this work, we are motivated to determine how to make use of manually segmented data in automatic segmentation. The key idea is to transfer the ground truth segmentation from the database of train images to a given test image. The ground truth segmentation of MR images is done by experts. The process includes a hierarchical image decomposition approach that performs the shape matching of test images at several levels, starting with the image as a whole (i.e. level 0) and then going through a pyramid decomposition (i.e. level 1, level 2, etc.) with the database of the train images and the given test image. The goal of pyramid decomposition is to find the section of the training image that best matches a section of the test image of a different level. After that, a re-composition approach is taken to place the best matched sections of the training image to the original test image space. Finally, the ground truth segmentation is transferred from the best training images to their corresponding location in the test image. We have tested our method on a hip joint MR image database and the experiment shows successful results on level 0, level 1 and level 2 re-compositions. Results improve with deeper level decompositions, which supports our hypotheses.

This thesis is concerned with problems encountered in image-based rendering (IBR) systems. The significance of such systems is increasing as virtual reality as well as augmented reality are finding their way into many applications, from entertainment to military. Particularly, I propose methods that are based on graph theory to address the open problems in the literature of image and video compositing, and scene completion. For a visually plausible compositing, it is first required to separate the object to be composed from the background it was initially captured against, a problem that is known as natural image matting. It aims, using some user interactions, to calculate a map that depicts how much a background color(s) contributes to the color of every other pixel in an image. My contributions to matting increase the accuracy of the map calculation as well as automate the whole process, by eliminating the need for user interactions. I propose several techniques for sampling user interactions which enhance the quality of the calculated maps. They rely on statistics of non-parametric color models as well as graph transduction and iterative graph cut techniques. The presented sampling strategies lead to state-of-the-art separation, and their efficiency was acknowledged by the standard benchmark in the literature. I have adopted the Gestalt laws of visual grouping to formulate a novel cost function to automate the generation of interactions that otherwise have to be provided manually. This frees the matting process from a critical limitation when used in rendering contexts. Scene completion is another task that is often required in IBR systems. This document presents a novel image completion method that overcomes a few drawbacks in the literature. It adopts a binary optimization technique to construct an image summary, which is then shifted according to a map, calculated with combinatorial optimization, to complete the image. I also present the formulation with which the proposed method can be extended to complete scenes, rather than images, in a stereoscopically and temporally-consistent manner.

The aim of this thesis was to reveal segments of the car customers in the Czech Republic. Firt section of this thesis contains theoretical informations about market segmentation. The second section is dedicated to an analysis and evaluation of the research, which has been conducted in terms of my thesis. At the and I described the discovered segments and suggested options for their approach.

The diploma thesis concerns in two aspects which are the description of internet users and internet media. I show the ways of measuring visits, which is closely related to the operation of web sites and especially its economic aspect, the advertisement. I specify the indicators in view. I concentrate really carefully on the amount of real users, which is one of the most important indices. I apply the usage of these indices to practical examples. I point to sociodemographic user's profile of particular portals and compare them with other chosen servers. Consequently I determine specific recommendations for the advertisement submitters in the term of targeting their campaign accurately. By using many-variable statistical methods, I try to find out suitable groups of internet users based on their relation to advertisement. I consequently describe these groups of people and determine again certain recommendations.

Loyalty, improvement, better understanding of customers -- all these notions attract the interest and striving for improvement of many managers. The segmentation has become an indispensable part of path from a company to customers. Dividing market into distinctive groups allows a company to target customers in a better way. The energy industry in the Czech Republic is an example where the segmentation methodology is not working clearly, so entire organisations become disoriented as an effect of not clear B2B customer division. As there are not so many segmentation tools that would be made exclusively for an energy company, I decided to study this field and design a new one. In the first part, I will look into the energy industry specificities and functioning of the electricity market in order to understand the specifics of this industry on the Czech market. By looking into segmentation methods, particularly in the area of B2B, the important assumptions of the correct and effective segmentation will be discovered. In the second part, I applied the knowledge from the theoretical framework where the basic knowledge about the B2B market and the segmentation in this area were introduced. Moreover, the most appropriate variables of segmentation were grouped in the logical way. This knowledge will be used as a base for the creation of new segmentation methodology and application of the knowledge gained from the extensive research conducted abroad. All in all, the ultimate goal of my work is to create the new advanced segmentation methodology tool and to propose how the most suitable solutions can be designed.

The objective of my thesis is the analysis of GE Money Bank's marketing strategy in the Czech market and proposal of measures to reduce its weaknesses. Theoretic part of the thesis describes concept of strategic management and particular parts of marketing strategy. Analytic part evaluates external environment in which GE Money Bank operates and subsequently describes marketing strategy of GE Money Bank -- goals of the company, segmentation and targeting, marketing objectives, corporate identity and so on. In the final part of the thesis, there is an SWOT analysis of the marketing strategy and proposed measures to reduce weaknesses of the marketing strategy.

The thesis aims to define processes and strategies in pricing as the one of the marketing mix tools, to appraise the customer segmentation as inseparable tool for finding profitable price and to prove its meaning and applicability at the real business project. First chapter is dealing with the definition of price and micro-economical perspective on price and price determination. It observes, what influences the price and price perception. It represents the price as a component of marketing tools and is pointing out at overlooking of this tool at several studies. The second chapter is focusing on pricing itself, explaining its principles, pillars and models with sub-chapter focused on pricing of internet services, regarding to the practical section of the thesis. The basis of effective pricing is segmenting customers and observing their needs and hopes. That is the reason for assessing the segmentation, its types and processes in the last, third chapter. Practical section of the thesis guides the reader through real business project with innovative business goal to create an internet space with real properties. The thesis should prove the advisability and importance of pricing and the proper price communication.

This study examined how market segmentation strategy can assist nonprofit organizations to identify potential corporate partners, establish collaborative alliances, and secure new sources of capital. Recent economic concerns and increases in competition have seriously affected the nonprofit's business environment. Therefore, a proactive approach is essential, so nonprofits can successfully secure desperately needed capital and continue to provide for those less fortunate. The study seeks a better understanding of the construct of alliance formation. Existing literature on alliances mostly focused on the nature of alliances; much less has been written about characteristics of alliance partners. Based on limited empirical evidence, this study integrated partner and partnership characteristics to develop a comprehensive theory of alliances. The research was conducted with the assistance and cooperation of several nonprofit organizations located in the tri-county area of South Florida. Participating nonprofit organizations were surveyed using several scales adapted from extant literature. Multiple regression technique, specifically stepwise regression was utilized to examine the hypothesized relationships between the dependent and independent (4) variables. Results indicated that organizational memory, governance, and firmographics were correlated to alliance formation and significant. However, the correlation between organizational culture and alliance formation was weak and not significant, despite extant literature review to the contrary. Further research is recommended to explicate causes for this lack of strength between organizational culture and alliance formation.