Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology

Abstract

Tracking is a fundamental task in Augmented Reality (AR) technology which requires robust real-time to properly adjust real and virtual objects in a single alignment, so that, both objects appear to coexist in the same world. Marker-less tracking has been explored to overcome the limitations of conventional marker-based tracking in AR. By capturing real surroundings to produce the features, the marker-less tracking will recognize these features to overlay the virtual objects on the top of the captured features. The features have been tracked in real-time by the display device, based on the real environment. Therefore, this article aimed to explain the features detection using Features Accelerated Segment Test (FAST) to detect corner features. Related works were reviewed and the features extraction for AR framework using Grayscale Image Generation (GIG) were presented. In addition, to enhance details of grayscale images, a comprehensive study was performed on the three techniques of Contrast Enhancement (CE), namely, Colormap, HE and CLAHE to determine the best method for robust features detection. The findings showed Colormap to be the best technique, compared to HE and CLAHE, in terms of noise, the accuracy of the corner, distributed histogram and amount of features.