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.

This study has put three different object detecting techniques to the test. The goal was to investigate small and fast-moving objects to see which technique’s performance is most suitable within the sports of Padel. The study aims to cover and explain different affecting conditions that could cause better but also worse performance for small and fast object detection. The three techniques use different approaches for detecting one or multiple objects and could be a guideline for future object detection development. The proposed techniques utilize background histogram calculation, HSV masking with edge detection and DNN frameworks together with the COCO dataset. The process is tested through outdoor video footage across all techniques to generate data, which indicates that Canny edge detection is a prominent suggestion for further research given its high detection rate. However, YOLO shows excellent potential for multiple object detection at a very high confidence grade, which provides reliable and accurate detection of a targeted object. This study’s conclusion is that depending on what the end purpose aims to achieve, Canny and YOLO have potential for future small and fast object detection.

This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations. Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method. The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.

<p>This thesis presents a 3D semi-automatic segmentation technique for extracting the lumen surface of the Carotid arteries including the bifurcation from 3D and 4D ultrasound examinations. </p><p>Ultrasound images are inherently noisy. Therefore, to aid the inspection of the acquired data an adaptive edge preserving filtering technique is used to reduce the general high noise level. The segmentation process starts with edge detection with a recursive and separable 3D Monga-Deriche-Canny operator. To reduce the computation time needed for the segmentation process, a seeded region growing technique is used to make an initial model of the artery. The final segmentation is based on the inflatable balloon model, which deforms the initial model to fit the ultrasound data. The balloon model is implemented with the finite element method. </p><p>The segmentation technique produces 3D models that are intended as pre-planning tools for surgeons. The results from a healthy person are satisfactory and the results from a patient with stenosis seem rather promising. A novel 4D model of wall motion of the Carotid vessels has also been obtained. From this model, 3D compliance measures can easily be obtained.</p>

This work outlines a method of detection of the papillary line width in fingerprints. This method is one of the possible methods of liveness detection. The first part of the work with deals defining of the fingerprint, attacks on today's systems and possibilities to improve security. The next section detection describes of the papillary line width. During the process of resolving, the first thing to do was to start operation of the scanning device and to read the database for tests and experiments. An independent application was created on this purpose. Further, there were projected methods for detection and measuring of the papillary line width. Use of the Canny edge detector with the Sobel operator and the Gaussian filter proved the best. Then, there is described implementation of individual methods. The next part of the work describes and assesses the results of the tests. The last chapter summarizes the work and proposes further possibilities of development.

This paper concerns methods of verification of person's signature and handwriting. Some of commonly used techniques are resumed and described with related literature being referred. Next aim of this work is design and implementation of a simple handwriting verification application. Application is based on edge detection and comparison of a set of structural and statistical features. As a support classification tool a SVM classifier of the LIBSVM software is employed. The Application is written in C language using OpenCV graphics library. Testing and training set was extracted from samples found in the IAM Handwriting Database. Application was created and tested in the Windows XP operating system.

This thesis investigates the problem of detecting and tracking sticky notes on Kanban boards using classical computer vision techniques. Currently, there exists some alternatives for digitizing sticky notes, but none keep track of notes that have already been digitized, allowing for duplicate notes to be created when scanning multiple images of the same Kanban board. Kanban boards are widely used in various industries, and being able to recognize, and possibly in the future even digitize entire Kanban boards could provide users with extended functionality. The implementation presented in this thesis is able to, given two images, detect the Kanban boards in each image and rectify them. The rectified images are then sent to the Google Cloud Vision API for text detection. Then, the rectified images are used to detect all the sticky notes. The positional information of the notes and columns of the Kanban boards are then used to filter the text detection to find the text inside each note as well as the header text for each column. Between the two images, the columns are compared and matched, as well as notes of the same color. If columns or notes in one image do not have a match in the second image, it is concluded that the boards are different, and the user is informed of why. If all columns and notes in one image have matches in the second image but some notes have moved, the user is informed of which notes that have moved, and how they have moved as well. The different experiments conducted in this thesis on the implementation show that it works well, but it is very confined to strict requirements, making it unsuitable for commercial use. The biggest problem to solve is to make the implementation more general, i.e. the Kanban board layout, sticky note shapes and colors as well as their actual content. / Denna avhandling undersöker problemet med att upptäcka och spåra klisterlappar och Kanban-tavlor med hjälp av klassiska datorseendetekniker. För närvarande finns det några alternativ för att digitalisera klisterlappar, men ingen håller reda på anteckningar som redan har digitaliserats, vilket gör att duplicerade anteckningar kan skapas när du skannar flera bilder av samma Kanban-kort. Kanban-kort används flitigt i olika branscher och att kunna känna igen, och eventuellt i framtiden även digitalisera hela Kanban-tavlor, skulle kunna ge användarna utökad funktionalitet. Implementeringen som presenteras i denna avhandling kan, givet två bilder, upptäcka Kanban-brädorna i varje bild och korrigera dem. De korrigerade bilderna skickas sedan till Google Cloud Vision API för textidentifiering. Sedan används de korrigerade bilderna för att upptäcka alla klisterlappar. Positionsinformationen för anteckningarna och kolumnerna på Kanban-tavlan används sedan för att filtrera textdetekteringen för att hitta texten i varje anteckning såväl som rubriktexten för varje kolumn. Mellan de två bilderna jämförs och matchas kolumnerna, samt anteckningar av samma färg. Om kolumner eller anteckningar i en bild inte har en matchning i den andra bilden dras slutsatsen att brädorna är olika och användaren informeras om varför. Om alla kolumner och anteckningar i en bild har matchningar i den andra bilden men några anteckningar har flyttats, informeras användaren om vilka anteckningar som har flyttats och hur de har flyttats. De olika experiment som genomförs i denna avhandling om implementering visar att den fungerar bra, men den är mycket begränsad till strikta krav, vilket gör den olämplig för kommersiellt bruk. Det största problemet att lösa är att göra implementeringen mer generell, d.v.s. Kanban-tavlans layout, klisterlapparnas former och färger samt deras faktiska innehåll.

In this work is describe anatomy and physiology of the cornea. The following are the primary non-inflamatory degeneration of the cornea. Then describe the physical principles diagnostic devices for cornea – keratometer, pachymeter, Michelson interferometr and optical coherence tomography (OCT). At the end of the theoretical introduction is describes the principle of laser correction surgery – LASIK. The practical part is divided into two main objectives. The first task is propose an algorithm for automatic detection of corneal surface and then calculation of corneal thickness and size of the chamber angle in Matlab. The aim of the second task is image flap analysis for boundary detection.

Aim of this thesis is description of neural network based edge detection methods that are substitute for classic methods of detection using edge operators. First chapters generally discussed the issues of image processing, edge detection and neural networks. The objective of the main part is to show process of generating synthetic images, extracting training datasets and discussing variants of suitable topologies of neural networks for purpose of edge detection. The last part of the thesis is dedicated to evaluating and measuring accuracy values of neural network.