Active contours for intensity inhomogeneous image segmentation

La “inhomogeneidad” (falta d'homogeneïtat) d'intensitat és un problema ben conegut en la segmentació d'imatges, la qual cosa afecta la precisió dels mètodes de segmentació basats en la intensitat. En aquesta tesi, es proposen mètodes de contorn actiu basat en fronteres i regions per segmentar imatges inhomogènies. En primer lloc, s'ha proposat un mètode de contorn actiu basat en fronteres mitjançant Diferència de Gaussianes (DoG), que ajuda a segmentar l'estructura global de la imatge. En segon lloc, hem proposat un mètode de contorn actiu basat en regions per corregir i segmentar imatges inhomogènies. S'ha utilitzat un nucli de transformació de fase (phase stretch transform - PST) per calcular noves intensitats mitjanes i camps de polarització, que s'empren per definir una imatge ajustada de polarització. En tercer lloc, s'ha proposat un altre mètode de contorn actiu basat en regions utilitzant un funcional d'energia basat en imatges ajustades locals i globals. El camp de polarització s'aproxima amb una distribució Gaussiana i el biaix de les regions no homogènies es corregeix dividint la imatge original pel camp aproximat de polarització. Finalment, s'ha proposat un mètode híbrid de contorns actius multifàsic (quatre fases) per dividir una imatge de RM cerebral en tres regions diferents: matèria blanca (WM), matèria grisa (GM) i líquid cefaloraquidi (CSF). En aquest treball, també s'ha dissenyat un mètode de post-processat (correcció de píxels) per millorar la precisió de les regions WM, GM i CSF segmentades. S'han utilitzat resultats experimentals tant amb imatges sintètiques com amb imatges reals de RM del cervell per a una comparació quantitativa i qualitativa amb mètodes de contorns actius de l'estat de l'art per mostrar els avantatges de les tècniques de segmentació proposades.La “inhomogeneidad” (falta de homogeneidad) de intensidad es un problema bien conocido en la segmentación de imágenes, lo que afecta la precisión de los métodos de segmentación basados en la intensidad. En esta tesis, se proponen métodos de contorno activo basado en bordes y regiones para segmentar imágenes inhomogéneas. En primer lugar, se ha propuesto un método de contorno activo basado en fronteras mediante Diferencia de Gaussianas (DoG), que ayuda a segmentar la estructura global de la imagen. En segundo lugar, hemos propuesto un método de contorno activo basado en regiones para corregir y segmentar imágenes inhomogéneas. Se ha utilizado un núcleo de transformación de fase (phase stretch transform - PST) para calcular nuevas intensidades medias y campos de polarización, que se emplean para definir una imagen ajustada de polarización. En tercer lugar, se ha propuesto otro método de contorno activo basado en regiones utilizando un funcional de energía basado en imágenes ajustadas locales y globales. El campo de polarización se aproxima con una distribución Gaussiana y el sesgo de las regiones no homogéneas se corrige dividiendo la imagen original por el campo aproximado de polarización. Finalmente, se ha propuesto un método híbrido de contornos activos multifásico (cuatro fases) para dividir una imagen de RM cerebral en tres regiones distintas: materia blanca (WM), materia gris (GM) y líquido cefalorraquídeo (CSF). En este trabajo, también se ha diseñado un método de post-procesado (corrección de píxeles) para mejorar la precisión de las regiones WM, GM y CSF segmentadas. Se han utilizado resultados experimentales tanto con imágenes sintéticas como con imágenes reales de RM del cerebro para una comparación cuantitativa y cualitativa con métodos de contornos activos del estado del arte para mostrar las ventajas de las técnicas de segmentación propuestas.Intensity inhomogeneity is a well-known problem in image segmentation, which affects the accuracy of intensity-based segmentation methods. In this thesis, edge-based and region-based active contour methods are proposed to segment intensity inhomogeneous images. Firstly, we have proposed an edge-based active contour method based on the Difference of Gaussians (DoG), which helps to segment the global structure of the image. Secondly, we have proposed a region-based active contour method to both correct and segment intensity inhomogeneous images. A phase stretch transform (PST) kernel has been used to compute new intensity means and bias field, which are employed to define a bias fitted image. Thirdly, another region-based active contour method has been proposed using an energy functional based on local and global fitted images. Bias field is approximated with a Gaussian distribution and the bias of intensity inhomogeneous regions is corrected by dividing the original image by the approximated bias field. Finally, a hybrid region-based multiphase (four-phase) active contours method has been proposed to partition a brain MR image into three distinct regions: white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). In this work, a post-processing (pixel correction) method has also been devised to improve the accuracy of the segmented WM, GM and CSF regions. Experimental results with both synthetic and real brain MR images have been used for a quantitative and qualitative comparison with state-of-the-art active contour methods to show the advantages of the proposed segmentation techniques

Design and Development of Data Collection Framework for Shop Floor Data

The rapid pace of technological advancements in the industrial world has transformed the traditional factory floor into a smart, digitalized environment. The advent of advanced and innovative manufacturing techniques, coupled with the Internet of Things (IoT), has made shop floor data an essential source of information for industries to monitor and optimize their operations. The ability to collect, process, and analyse shop floor data has become critical to facilitating the digitalization of factories, which can ultimately improve the competitiveness of industries in the global market. To address this need, the SHOP4CF project aims to develop a comprehensive platform that can facilitate the digitalization of factories. A key module of this project is the Data Collection Framework (DCF), which collects data from various shop floor devices and sensors, analyses, and process data streams, stores the Event Processing in a database and posts the context data to the FIWARE context broker. In line with this, the proposed master's thesis seeks to design and develop a data collection framework that can effectively collect and process shop floor data and can facilitate the digitalization of factories, which has become essential for industries to monitor and optimize their processes in the era of Industry 4.0. This framework is scalable, flexible, and modular, accommodating various manufacturing processes’ requirements and integrating with different devices and sensors used on the shop floor. Additionally, the framework is equipped with real-time data analysis capabilities, allowing manufacturing managers and engineers to monitor event processing information and optimize them in real-time as well as handle large volumes of data generated by shop floor devices and store it in a centralized database for further analysis. The thesis aims to contribute to the development of Industry 4.0 and improve the competitiveness of industries in the global market. To achieve this, it proposes research questions to identify the suitable architecture for collecting real-time shop floor data, how the data collection framework and event processing can be utilized to create real-time alerts for a safe shop floor environment, and which industrial communication protocols have been tested for accomplishing data acquisition for industrial shop floors. Structured into six chapters, this thesis provides a comprehensive analysis of the proposed data collection framework's design, implementation, and testing. The literature review and theoretical background in Chapter two provide an in-depth analysis of vital concepts, such as automation pyramid, ISA-95, communication protocols, MQTT, OPC UA, FIWARE, and MongoDB. The thesis's innovative framework is presented in Chapter three, and Chapter four discusses its implementation. Chapter five examines the testing process and the results obtained from the proposed data collection framework, while Chapter six concludes the thesis and proposes future work to improve the framework

Venture Capital Sector in Pakistan: Ratio Analysis Approach for Financial Performance Assessment

The purpose of study is to rank the venture capital companies operating in Pakistan during the period of 2006-2009 on the base of their financial performance. Ratio analysis technique was used to rank the venture capital companies using profitability / efficiency ratios and total assets as proxies of financial performance. This study concludes that TRG Pakistan Limited is at first in ranking on the bases of return on assets (ROA), return on equity (ROE), and total assets, and at second on the base of earnings per share (EPS). AMZ Ventures Limited is at first on the base of earnings per share (EPS), at second in ranking on the bases of return on assets (ROA), return on equity (ROE), and total assets. TMT Ventures Limited is third on the bases of all ratios, and total assets. This is the first attempt that was made with an objective to facilitate the students, investors and management of company with useful information regarding financial performance of all venture capital companies operating in Pakistan

Unit Interval Time and Magnitude Monitoring Using Beta and Unit Gamma Distributions

Quick detection of an assignable cause is necessary for process accuracy with respect to the specifications. The aim of this study is to monitor the time and magnitude processes based on unit-interval data. To this end, maximum exponentially weighted moving average (Max-EWMA) control chart for simultaneous monitoring time and magnitude of an event is proposed. To be precise, beta and unit gamma distributions are considered to develop the Max-EWMA chart. The chart’s performance is accessed using average run length (ARL), the standard deviation of run length (SDRL), and different quantiles of the run length distribution through extensive Monte Carlo simulations. Besides a comprehensive simulation study, the proposed charting methodology is applied to a real data set. The results show that the proposed chart is more efficient in detecting small to medium-sized shifts. The results also indicate that simultaneous shifts are detected more quickly as compared to the pure shift