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 diploma thesis deals with object detection using mathematical technique called Hough transform. Hough transform technique is conceived in general terms from the de facto simplest use for the detection of elementary analytically describable shapes such as lines, ellipses, circles or simple analytically definable elements to sophisticated use for the detection of complex - analytically virtually indescribable - objects. These include cars or pedestrians who are detected on the basis of the photographic records of these objects and entities. The document thus maps the definition and use of the respective Hough transform subtechniques along with their basic classification on probabilistic and non-probabilistic methods. The work subsequently culminates in describing the general state-of-the-art technique called Class-Specific Hough Forests for Object Detection, introduces its definition, training procedure on a provided dataset and the detection of test patterns. In conclusion of this work,there is designed and implemented generally trainable object detector using this technique. And there is experimental evaluation of its quality.

La représentation visuelle est un problème fondamental en vision par ordinateur. Le but est de réduire l'information au strict nécessaire pour une tâche désirée. Plusieurs types de représentation existent, comme les caractéristiques de couleur (histogrammes, attributs de couleurs...), de forme (dérivées, points d'intérêt...) ou d'autres, comme les bancs de filtres.Les caractéristiques bas-niveau (locales) sont rapides à calculer. Elles ont un pouvoir de représentation limité, mais leur généricité présente un intérêt pour des systèmes autonomes et multi-tâches, puisque les caractéristiques haut-niveau découlent d'elles.Le but de cette thèse est de construire puis d'étudier l'impact de représentations fondées seulement sur des caractéristiques locales de bas-niveau (couleurs, dérivées spatiales) pour deux tâches : la poursuite d'objets génériques, nécessitant des caractéristiques robustes aux variations d'aspect de l'objet et du contexte au cours du temps; la détection d'objets, où la représentation doit décrire une classe d'objets en tenant compte des variations intra-classe. Plutôt que de construire des descripteurs d'objets globaux dédiés, nous nous appuyons entièrement sur les caractéristiques locales et sur des mécanismes statistiques flexibles visant à estimer leur distribution (histogrammes) et leurs co-occurrences (Transformée de Hough Généralisée). La Transformée de Hough Généralisée (THG), créée pour la détection de formes quelconques, consiste à créer une structure de données représentant un objet, une classe... Cette structure, d'abord indexée par l'orientation du gradient, a été étendue à d'autres caractéristiques. Travaillant sur des caractéristiques locales, nous voulons rester proche de la THG originale.En poursuite d'objets, après avoir présenté nos premiers travaux, combinant la THG avec un filtre particulaire (utilisant un histogramme de couleurs), nous présentons un algorithme plus léger et rapide (100fps), plus précis et robuste. Nous présentons une évaluation qualitative et étudierons l'impact des caractéristiques utilisées (espace de couleur, formulation des dérivées partielles...). En détection, nous avons utilisé l'algorithme de Gall appelé forêts de Hough. Notre but est de réduire l'espace de caractéristiques utilisé par Gall, en supprimant celles de type HOG, pour ne garder que les dérivées partielles et les caractéristiques de couleur. Pour compenser cette réduction, nous avons amélioré deux étapes de l'entraînement : le support des descripteurs locaux (patchs) est partiellement produit selon une mesure géométrique, et l'entraînement des nœuds se fait en générant une carte de probabilité spécifique prenant en compte les patchs utilisés pour cette étape. Avec l'espace de caractéristiques réduit, le détecteur n'est pas plus précis. Avec les mêmes caractéristiques que Gall, sur une même durée d'entraînement, nos travaux ont permis d'avoir des résultats identiques, mais avec une variance plus faible et donc une meilleure répétabilité. / Visual representation is a fundamental problem in computer vision. The aim is to reduce the information to the strict necessary for a query task. Many types of representation exist, like color features (histograms, color attributes...), shape ones (derivatives, keypoints...) or filterbanks.Low-level (and local) features are fast to compute. Their power of representation are limited, but their genericity have an interest for autonomous or multi-task systems, as higher level ones derivate from them. We aim to build, then study impact of low-level and local feature spaces (color and derivatives only) for two tasks: generic object tracking, requiring features robust to object and environment's aspect changes over the time; object detection, for which the representation should describe object class and cope with intra-class variations.Then, rather than using global object descriptors, we use entirely local features and statisticals mecanisms to estimate their distribution (histograms) and their co-occurrences (Generalized Hough Transform).The Generalized Hough Transform (GHT), created for detection of any shape, consists in building a codebook, originally indexed by gradient orientation, then to diverse features, modeling an object, a class. As we work on local features, we aim to remain close to the original GHT.In tracking, after presenting preliminary works combining the GHT with a particle filter (using color histograms), we present a lighter and fast (100 fps) tracker, more accurate and robust.We present a qualitative evaluation and study the impact of used features (color space, spatial derivative formulation).In detection, we used Gall's Hough Forest. We aim to reduce Gall's feature space and discard HOG features, to keep only derivatives and color ones.To compensate the reduction, we enhanced two steps: the support of local descriptors (patches) are partially chosen using a geometrical measure, and node training is done by using a specific probability map based on patches used at this step.With reduced feature space, the detector is less accurate than with Gall's feature space, but for the same training time, our works lead to identical results, but with higher stability and then better repeatability.

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.

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)

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.