DOI : https://doi.org/10.18517/ijaseit.11.5.10990

The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality

- Dahliyusmanto (1), Devi Willieam Anggara (2), Mohd Shafry Mohd Rahim (3), Ajune Wanis Ismail (4)
(1) Faculty of Engineering, University of Riau, 28293 Pekanbaru, Riau, Indonesia
(2) Faculty of Computing, Universiti Teknologi Malaysia, 8130 Johor Bahru, Johor, Malaysia
(3) Institute of Human Centred Engineering (iHumEn),Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
(4) Institute of Human Centred Engineering (iHumEn),Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
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How to cite (IJASEIT) :
Dahliyusmanto, -, et al. “The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, Oct. 2021, pp. 2104-11, doi:10.18517/ijaseit.11.5.10990.
Citation Format :
Natural Feature Tracking (NFT) in Augmented Reality (AR) applications use feature detection and a feature matching approach to aligning virtual objects in a real environment. Thus, this tracking detects and compares features that are naturally found in the image (query of image) with the visible feature in the real environment. Therefore, the query of an image must contain good features to track. One of the representing natural features that is easily found in the image is in the corner, and a feature from Accelerated Segment Test (FAST) is one of the fastest corner detectors. However, the FAST corner uses the intensity of the grayscale pixel to determine the candidate corner. Hence, the intensity greatly affects the detection result. Therefore, FAST corner uses the grayscale conversion process to changes the color image into a grayscale image. Thus, the conversion process can lose some details of the images, such as sharpness, shadow, and color image structure. Hence, this process will affect the result of FAST corner to find the feature corner. Besides, Contrast Enhancement also can improve the quality of low contrast grayscale image. In this paper, there are three techniques of the Contrast Enhancement (CE) method were compared, which are Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Colormap. As a result, Colormap is better than HE and CLAHE to extract conner and others feature accurately.

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