This thesis is focused on optical character recognition and its processing. The goal of this application is to make it possible easily track daily expenses. It can be used by an individual or by a company as a monitoring tool. The main principle is to make this tool most as user friendly as it can be. The application gets its input from hardware, such as a scanner or camera, and analyzes the content of the cash voucher for further processing. To analyze the voucher, the application employs different optical character recognition methods. The result is subsequently parsed. Detailed explanations of used methods are inside the document. The application output is a filled database with cash voucher details. Another part of the work is an information system with the main purpose of displaying the collected data.

This thesis deals with segmentation of tennis player´s body parts for analysis its motion. Testing code was written in C++ with use of OpenCV library. Some image processing techniques such as thresholding, background subtraction or finding the largest contours were implemented. There was used a linear triangulation technique for calculating the 3D coordinates of segmented points.

Tato dizertační práce se zaměřuje na použití paralelních souřadnic pro parametrizaci čar a bodů. Paralelní souřadný systém má souřadnicové osy vzájemně rovnoběžné. Bod ve dvourozměrném prostoru je v paralelních souřadnicích zobrazen jako přímka a přímka jako bod. Toho je možné využít pro Houghovu transformaci - metodu, při které body zájmu hlasují v prostoru parametrů pro danou hypotézu. Parametrizace pomocí paralelních souřadnic vyžaduje pouze rasterizaci úseček, a proto je velmi rychlá a přesná. V práci je tato parameterizace demonstrována na detekci maticových kódů a úběžníků.

This thesis deals with curve detection in images. First, current methods used in this area of image processing are summarized and described. Main topic of this thesis is a comparison of methods of parametric curve detection, such as Hough transformation and RANSAC-based methods. These methods are compared according to several criteria which are the most important for precise edge detection.

Tato práce se zabývá Houghovou transformací (HT). HT je nejčastěji používána pro detekci přímek nebo křivek, ale byla zobecněna i pro detekci libovolných tvarů. Hlavní téma této práce jsou parametrizace přímek, speciálně PTLM - zobrazení bodů na přímky. Tyto parametrizace mají tu vlastnost, že bodům v obrázku odpovídají přímky v parametrickém prostoru. Tato práce poskytuje důkazy některých vlastností PTLM. Za zmínku stojí existence páru PTLM vhodného pro detekci a efekt konvoluce v obrázku na obsah parametrického prostoru. V práci jsou prezentovány dvě implementace HT. Obě využívají k akceleraci grafický hardware. Jedna využívá GPGPU API CUDA a druhá zobrazovací API OpenGL. Jako aplikace detekce přímek je uvedena část detekce šachovnicových markerů použitelných pro rozšířenou realitu.

Health is a fundamental human right. To increase global health, research in the medical sector is of great importance. Decreasing time consumption of biomedical testing could accelerate the research and development of new drugs and vaccines. This could be achieved by automation of biomedical analysis, using computerized methods. In order to perform analysis on pictures of biomedical tests, it is important to identify the area of interest (AOI) of the test. For example, cells and bacteria are commonly grown in petri dishes, in this case the AOI is the bottom area of the dish, since this is where the object of analysis is located.This study was performed with the aim to compare a few computerized methods for identifying the AOI in pictures of biomedical tests. In the study, biomedical images from a testing method called ELISpot have been used. ELISpot uses plates with up to 96 circular wells, where pictures of the separate wells were used in order to find the AOI corresponding to the bottom area of each well. The focus has been on comparing the performance of three edge detection methods. More specifically, their ability to accurately detect the edges of the well. Furthermore, a method for identifying a circle based on the detected edges was used to specify the AOI.The study shows that methods using second order derivatives for edge detection, gives the best results regarding to robustness.

yes / In the health care systems, a high security level is required to protect extremely sensitive patient records. The goal is to provide a secure access to the right records at the right time with high patient privacy. As the most accurate biometric system, the iris recognition can play a significant role in healthcare applications for accurate patient identification. In this paper, the corner stone towards building a fast and robust iris recognition system for healthcare applications is addressed, which is known as iris localization. Iris localization is an essential step for efficient iris recognition systems. The presence of extraneous features such as eyelashes, eyelids, pupil and reflection spots make the correct iris localization challenging. In this paper, an efficient and automatic method is presented for the inner and outer iris boundary localization. The inner pupil boundary is detected after eliminating specular reflections using a combination of thresholding and morphological operations. Then, the outer iris boundary is detected using the modified Circular Hough transform. An efficient preprocessing procedure is proposed to enhance the iris boundary by applying 2D Gaussian filter and Histogram equalization processes. In addition, the pupil’s parameters (e.g. radius and center coordinates) are employed to reduce the search time of the Hough transform by discarding the unnecessary edge points within the iris region. Finally, a robust and fast eyelids detection algorithm is developed which employs an anisotropic diffusion filter with Radon transform to fit the upper and lower eyelids boundaries. The performance of the proposed method is tested on two databases: CASIA Version 1.0 and SDUMLA-HMT iris database. The Experimental results demonstrate the efficiency of the proposed method. Moreover, a comparative study with other established methods is also carried out.

No description available.

<p>In this research, liquid-liquid dispersion of viscous fluids was studied in an SMX static mixer in the laminar regime. Backlighting technique was used for flow visualization, and the Hough transform for circle detection was used in OpenCV to automatically detect and measure drop diameters for obtaining the size distribution. Silicone oil and an aqueous solution of high fructose corn syrup were used for dispersed and continuous phases respectively, and sodium dodecyl sulfate was used as the surfactant to modify the interfacial tension. Experiments were conducted at varying viscosity ratios and flow rates-each at zero, low (~200 ppm) and high (~1000 ppm) surfactant concentrations. The effect of holdup was explored only for a few cases, but it was found to have a minimal effect on the weighted average diameter D₄₃.</p> <p>It was found that the superficial velocity and the continuous phase viscosity had a dominant effect on D₄₃. The tail at the higher end of the droplet size distribution decreased with increasing superficial velocity and continuous phase viscosities. It was also found that D₄₃ decreased with lowering of the interfacial tension. Furthermore, the effect of the dispersed phase viscosity was significant only at non zero surfactant concentrations.</p> <p>An approximate model has been proposed that relates D₄₃ to the capillary number. It is based on an energy analysis of the work done by the viscous and surface forces on a drop of an initial diameter that is largely determined by the gap distance between the cross bars in the element</p> / Master of Applied Science (MASc)

La lecture automatique de plaques d’immatriculation de véhicule est considérée comme une approche de surveillance de masse. Elle permet, grâce à la détection /localisation ainsi que la reconnaissance optique, d’identifier un véhicule dans les images ou les séquences d’images. De nombreuses applications comme le suivi du trafic, la détection de véhicules volés, le télépéage ou la gestion d’entrée / sortie des parkings utilise ce procédé. Or malgré d’important progrès enregistré depuis l’apparition des premiers prototypes en 1979 accompagné d’un taux de reconnaissance parfois impressionnant, notamment grâce aux avancés en recherche scientifique et en technologie des capteurs, les contraintes imposés pour le bon fonctionnement de tels systèmes en limitent les portées. En effet, l’utilisation optimale des techniques de localisation et de reconnaissance de plaque d’immatriculation dans les scénarii opérationnels nécessite des conditions d’éclairage contrôlées ainsi qu’une limitation dans de la pose, de vitesse ou tout simplement de type de plaque. La lecture automatique de plaques d’immatriculation reste alors un problème de recherche ouvert. La contribution majeure de cette thèse est triple. D’abord une nouvelle approche robuste de localisation de plaque d’immatriculation dans des images ou des séquences d’images est proposée. Puis, l’amélioration de la qualité des plaques localisées est traitée par une adaptation de technique de super-résolution. Finalement, un modèle unifié de localisation et de super-résolution est proposé permettant de diminuer la complexité temporelle des deux approches combinées. / Automatic reading of vehicle license plates is considered an approach to mass surveillance. It allows, through the detection / localization and optical recognition to identify a vehicle in the images or video sequences. Many applications such as traffic monitoring, detection of stolen vehicles, the toll or the management of entrance/ exit parking uses this method. Yet in spite of important progress made since the appearance of the first prototype sin 1979, with a recognition rate sometimes impressive thanks to advanced science and sensor technology, the constraints imposed for the operation of such systems limit laid. Indeed, the optimal use of techniques for localizing and recognizing license plates in operational scenarios requiring controlled lighting conditions and a limitation of the pose, velocity, or simply type plate. Automatic reading of vehicle license plates then remains an open research problem. The major contribution of this thesis is threefold. First, a new approach to robust license plate localization in images or image sequences is proposed. Then, improving the quality of the plates is treated with a localized adaptation of super-resolution technique. Finally, a unified model of location and super-resolution is proposed to reduce the time complexity of both approaches combined.