Denna avhandling beskriver ett projekt för att skapa en prototypapplikation med syftet att förenkla vägmärkesinventering. Istället för att manuellt analysera en tagen bild och jämföra med en databas av vägmärken för inventing så kan man istället starta denna applikation, ladda in bilden och få ut ett svar på vad skylten har för identifieringskod. Idén är att vägmärkesinventerare ska spara in tiden det tar att gå igenom alla bilder tagna under en dag och istället få systemet att automatiskt lägga in vad vägmärket har för identifieringskod. Grundapplikationen är skriven som en WPF-applikation med hjälp av ramverket EmguCV som i sin tur nyttjar .NET ramverket. Den viktiga aspekten i detta projekt är att se ifall detta kan göras med rimlig beräkningskraft, kunna matcha skyltar på en rimlig tid, vilket gjordes möjligt med EmguCVs FLANN-algoritm. Projektet resulterade i en fungerande applikation där användare kan ladda upp cirkulära hastighetsvägmärken där applikationen detekterar och sedan matchar vägmärket mot en databas för att kunna identifiera det. / This dissertation describes a project for creating a prototype application for the purpose of simplifying road sign inventory. Instead of manually analyzing a captured image and comparing it to a database of inventory road signs, you can instead launch this application, load the image and get the identification code of that road sign. The idea is that road sign inventory takers will save the time it takes to review all the pictures taken during a day and instead, the system will automatically generate the identification code of that road sign. The basic application is written as a WPF application using the EmguCV framework, which in turn uses the .NET framework. The important aspect of this project is to see if matching road signs can can be done with reasonable computation and within reasonable time, this was made possible with EmguCVs FLANN-algorithm. The project resulted in a functional application in which users can upload circular velocity road signs and the application detects and identifies the road sign via a database of road signs.

The industrial testing phase of graphical user interfaces and the behaviour of screens, is still involving manual tests with human interaction. This type of testing is particularly difficult and time consuming to manually perform, due to time sensitive messages and information used within these interfaces. This thesis address this issue by introducing an approach to automate this process by utilizing high grade machine vision cameras and existing algorithm implementations from OpenCV 3.2.0. By knowing the expected graphical representation in advance, a comparison between the actual outcome and this expectation can be evaluated by applying image processing algorithms. It is found that this approach presents an Equal Error Rate of 6% while still maintaining a satisfactory time performance, in relation to the timeframe requirement of these time sensitive messages. Accuracy and time performance is profoundly affected by hardware equipment, partially due to the immense amount of image processing involved.

This thesis is about methods of image comparison. Goal is to create a mobile app that allows user to compare images in real time. In the first part there is a theoretical basis, in particular image similarity algorithms. The practical part contains the app implementation including use case analysis, user interface design and functional requirements. It is followed by source code samples a description of frameworks used. Last part is made of testing the implemented algorithms regarding speed and precision.

This master’s thesis is aimed at registration of frames of retinal fundus video using phase corre- lation. An introduction describes general research in topic of retinal fundus, eye movements, diff erent approaches for image registration, phase correlation and examples of phase corre- lation applications. The second, practical part of master’s thesis, is dedicated to description of the proposed algorithm for registration of frames of retinal fundus video. The description of the proposed algorithm is divided into three parts. First two parts describe how frames of retinal fundus video are rated in terms of suitability for registration. Third part describes image registration algorithm itself. In conclusion, the accuracy of algorithm and computational time are evaluated.

The diploma thesis deals with the industrial inspection of correctly made connectors based on computer recognition, and the detection and recognition application is implemented in Python on the Raspberry Pi platform.The work uses empirically known OpenCV library for recognition. The work also deals with the selection of suitable hardware devices, which are a camera with a lens and an illuminator, from which is created one compact device together with the Raspberry Pi microcomputer. The compact device is further mounted on the designed mechanical structure under which is created inspection zone. Finally, Raspberry Pi has a web-based user interface to check the inspection and the interface to write the data to the database.

This thesis focuses on object tracking in real-time. Tracked object is defined by bounding rectangle. The thesis works on issue of image processing and using histogram for real-time object tracking. The main contribution of the work is the extension of the provided program to track object in real-time with changing bounding rectangle. Size of the rectangle is changing as the object moves closer of further from camera. Furthemore the detection behavior in different scenarios is analyzed. In addition, various weight calculations were tested. The program is written in C++ using OpenCV library.

This master thesis deals with the design of a traffic sign detector using the image processing methods. The OpenCV library for working with images in programming language Python is used for this. The first part reports on the using methods. In the next part, these methods were tested on images of traffic signs taken in traffic in different lighting conditions. The results of these tests led to the design of optimal methods and their settings, which were re-verified by verifying on video of driving in traffic. This also revealed the conditions under which they can operate in real-time systems. Finally, an optimization algorithm for compensation of detection errors was proposed from the monitoring of detection waveforms.

The thesis deals with a concept and creation of classifiers of defects found on continuous production lines. The first part presents an overview of methods used for image classification and a analysis of defects. The main part of the thesis consist of a description of created classifier interface and graphical user interface for classifier. The last part sums up reliability of each implemented classifer.

This master thesis deals with the comparison of an industrial system for object recognition and measurement with an affordable camera on the Raspberry Pie platform. The theoretical part briefly describes several methods for measuring dimensions, which are commonly used in industry, along with a general description of technical equipment for image recording. The next section describes 3 industrial systems for image analysis. The practical part of the work deals with the design of the measuring stand, the description of the technical equipment, the implementation of measurements using both platforms and the subsequent evaluation.

This project describes image processing, characterization of scenes, especialy differences of screen captures and method motion objects there. Practical part defines the program, which is finding and tracking similar objects on video files or series of images. The program also computes predictions of next location for each object. This thesis is working with only synthesis scenes. I have chosen this way, because we can understand the synthesis scenes as a simplification of world. If solution of tracking and prediction on this scenes are suscesfull, then could be possible resolve also complicated problems.