Developments Under the Freedom of Information Act—1974

Den här rapporten mäter i vilka miljöer det går att hitta förutbestämda former med OpenCV och en "off-the-shelf" webbkamera.  This project measures in which environments a predetermined shape can be found with OpenCV and "off-the-shelf" webcameras.

The Legal Approach to Crime and Correction

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

Processing Large Amounts of Images on Hadoop with OpenCV

Modern image collections cannot be processed efficiently on one computer due to large collection sizes and high computational costs of modern image processing algorithms. Hence, image processing often requires distributed computing. However, distributed computing is a complicated subject that demands deep technical knowledge and often cannot be used by researches who develop image processing algorithms. The framework is needed that allows the researches to concentrate on image processing tasks and hides from them the complicated details of distributed computing. In addition, the framework should provide the researches with the familiar image processing tools. The paper describes the extension to the MapReduce Image Processing (MIPr) framework that provides the ability to use OpenCV in Hadoop cluster for distributed image processing. The modified MIPr framework allows the development of image processing programs in Java using the OpenCV Java binding. The performance testing of created system on the cloud cluster demonstrated near-linear scalability

openEyeTrack - a high speed multi-threaded eye tracker for head-fixed applications

Published versio

Development of Computer Vision-Enhanced Smart Golf Ball Retriever

An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field