Open AccessMETHODS OF DETERMINING THE THRESHOLD VALUE OF IMAGE AS A WAY TO IMPROVE AUGMENTED REALITY
Author(s) -
Oleksandr S. Bezpalko
Publication year - 2021
Publication title -
kpi science news
Language(s) - English
Resource type - Journals
eISSN - 2663-7472
pISSN - 2617-5509
DOI - 10.20535/kpisn.2020.4.226964
Subject(s) - threshold limit value , segmentation , computer science , set (abstract data type) , rank (graph theory) , augmented reality , image quality , image (mathematics) , artificial intelligence , pattern recognition (psychology) , quality (philosophy) , algorithm , mathematics , medicine , philosophy , environmental health , epistemology , combinatorics , programming language
Background. Augmented reality technology is at the initial stage of its development and requires further development of new and improvement of existing methods and algorithms. The most common and reliable approach to the implementation of augmented reality is the marker approach. The definition of the marker is divided into several stages, one of the most important of which is to determine the threshold of the image. Properly chosen algorithm for determining this threshold can significantly increase the efficiency of the algorithm for finding the marker.Objective. The aim of the study is a comparative analysis of threshold algorithms based on the type of information used and evaluation of their effectiveness based on a set of objective indicators of segmentation quality. Comparison is made for images and documents. For an objective comparison of efficiency, we used a combination of 4 quality criteria for form segmentation.Methods. The following performance criteria were used to determine the performance of the threshold determination methods: classification error, boundary determination error, relative foreground error, and non-uniformity region. Two approaches were considered to obtain an average performance score according to the previous criteria. The first approach was the arithmetic averaging of normalized scores obtained by all criteria. Using a specific threshold algorithm for the image, the average value of all criteria was determined, which was an indicator of its segmentation quality. In turn, the sum of these quality indicators for all images was determined as the performance indicator of the algorithm. The second approach used rank averaging, so that for each test image there was a ranking of algorithms of the threshold value from 1 to 16 according to each criterion separately. Then the ranks (not the actual scores) were averaged both by images and by all criteria.Results. A general unified comparative analysis of the performance of 16 algorithms for determining the threshold value of the image was conducted, which makes it possible to improve the functioning of technologies that depend on determining the threshold of images, including the mechanism of augmented reality marker recognition.Conclusions. It has been observed that the clustering-based method, namely the Kittler method, and the entropy-based method, namely the Kapoor method, are the best algorithms for determining thresholds for specialized images. Similarly, the Kittler clustering method and the Sauwol and White local variable methods are the best working algorithms for binarization of text documents.