AbstractsComputer Science

Implementing Object and Feature Detection Without Compromising the Performance

by Jonas Gerling

Institution: Linköping University
Year: 2016
Keywords: Natural Sciences; Computer and Information Science; Computer Science; Naturvetenskap; Data- och informationsvetenskap; Datavetenskap (datalogi); Computer science; Datavetenskap
Posted: 02/05/2017
Record ID: 2091681
Full text PDF: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-129276


This thesis will cover how some computationally heavy algorithms used in digital image processing and computer vision are implemented with WebGL and computed on the graphics processing unit by utilizing GLSL-shaders. This thesis is based on an already implemented motion detection plug-in used in web based games. This plug-in is enhanced with new features and some already implemented algorithms are improved. The motion detection is based on image subtraction and uses the delta image from previous frames to determine motion. The plug-in is used in web based games so the performance is of utmost importance since bad performance leads to frustration and less immersion for the players Techniques brought up are edge detection, Gaussian filter, features from accelerated segment test(FAST) and Harris corner detection. These techniques will be implemented by utilizing the parallel structure of the GPU. Both Harris corner detection and features from accelerated segment test can be run in real time but the result of the Harris corner detection is the better of the two. The thesis will also cover different color spaces, how they are implemented and why they were implemented