The use of Computer Vision in programmable mobile devices could lead to novel and creative applications. However, the computational demands of Computer Vision are ill-suited to low performance mobile processors. Also the evolving algorithms, due to active research in this fi eld, are ill-suited to dedicated digital circuits. This thesis proposes the inclusion of an FPGA co-processor in smartphones as a means of efficiently computing tasks such as Computer Vision. An open source object detection algorithm is run on a mobile device and implemented on an FPGA to motivate this proposal. Our hardware implementation presents a novel memory architecture and a SIMD processing style that achieves both high performance and energy efficiency. The FPGA implementation outperforms a mobile device by 59 times while being 13.5 times more energy efficient.

Converting handwritten or machine printed documents into a computer readable format allows more efficient storage and processing. The recognition of machine printed text is very reliable with today's technology, but the recognition of offline handwriting still remains a problem to the research community due to the high variance in handwriting styles. Modern mobile devices are capable of performing complex tasks such as scanning invoices, reading traffic signs, and online handwriting recognition, but there are only a few applications that treat offline handwriting. This thesis investigates the segmentation of handwritten documents into text lines and words, how the legibility of handwriting can be increased by beautification, as well as implementing it for modern mobile devices. Text line and word segmentation are crucial steps towards implementing a complete handwriting recognition system. The results of this thesis show that text line and word segmentation along with handwriting beautification can be implemented successfully for modern mobile devices and a survey concluding that the writing on processed documents is more legible than their unprocessed counterparts. An application for the operating system iOS is developed for demonstration.

Modern vehicles are very dependent on sensors and especially cameras to analyze different objects and conditions. Single camera systems are frequently used for lane detection and identifying objects in the distance. These systems depend on good conditions to work properly and are easily disrupted by environmental interferences. This project is targeted in developing an image processing algorithm that can detect disturbances applied upon the camera lens. Several focus measure operators are evaluated and compared by expected outcome while still maintaining a satisfying computational time, low resource usage and accuracy with an ERR between 0% - 1%.

I dagens flygplan finns det mycket information som på ett snabbt och pålitligt sätt behöver förmedlas till piloten via instrument på flera skärmar i cockpit. Att verifiera att skärmarna visar korrekt data för olika indata är ett tidskrävande och monotont arbete. Därför undersöker Saab möjligheten att automatisera delar av arbetet. Examensarbetet undersöker genom praktiskt implementation ifall det är möjligt att automatisera bildanalysen med hjälp av programmen OpenCV och Tesseract. Resultatet visade att det går att enkelt konstruera tester för att automatiskt identifiera oönskade förändringar i den implementerade instrumentingen.

The thesis deals with the problem of GPU acceleration of algorithms for traffic sign recognition. Theoretical part of the thesis outlines methods for object detection with emphasis on the traffic sign detection problem. Further, it provides comparison of two well known tools for programming on the GPU: CUDA and OpenCL. On the basis of the review, an architecture of own solution is proposed. Finally, the thesis contains description of the implementation as well as evaluation of the results.

This report documents the research and experiments on evaluating the possibilities of using OpenCV for developing a markerless augmented reality applications using the structure from motion algorithm. It gives a background on what augmented reality is and how it can be used and also theory about camera calibration and the structure from motion algorithm is presented. Based on the theory the algorithm was implemented using OpenCV and evaluated regarding its performance and possibilities when creating markerless augmented reality applications.

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This master's thesis describes design and development of an application for estimation of vehicle speed from both recorded video file and from camera stream.  It explains the procecess of a camera calibration, vehicle detection and tracking and describes the robot operating system as a target platform. The application uses library OpenCV for most of tasks, to access video application uses a FFmpeg library. Results can be printed to the terminal window, they can also be logged to a file or published in ROS. Application is written in C++ language, some parts in Python.

This master's thesis deals with eye movement tracking using the Raspberry Pi platform. The theoretical part describes eye anatomy, eye detection and eyetracking. A system in Python programming language was designed in the practical part. This algorithm is able to perform the eye tracking function using the Raspberry Pi platform and the RPi Camera module. The OpenCV library is used for loading and preprocessing images from the camera. A method that detects and evaluates the direction of view after a calibration is available. The accuracy of the program is tested on three vector methods and two target methods for four screen resolutions.

The aim of this Master's thesis is designing and developing Android application for automatic number plate recognition with external database lookup. In the introduction we discuss possibilities of number plate recognition in general. Android platform fundamentals, necessary developer tools and multi-platform image processing library OpenCV are described in the second section. In the third section different database types and synchronization methods are introduced. In the fourth section we describe basics of image processing and different algorithms necessary for recognition. Application requirements, involving scene and hardware requirements are defined in the fifth section. In the sixth section application development and algorithm implementation is described. In the seventh section we evalute the results of the detection. In the last section results are summarized and goals are set for further improvement.