The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality
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 fea...
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| Format: | Article |
| Language: | English |
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Insight Society
2021
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| Online Access: | http://eprints.utm.my/95006/1/AjuneWanis2021_TheComparisonofGrayscaleImageEnhancement.pdf |
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| author | Dahliyusmanto, Dahliyusmanto Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis |
| author_facet | Dahliyusmanto, Dahliyusmanto Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis |
| author_sort | Dahliyusmanto, Dahliyusmanto |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T21:04:38Z |
| format | Article |
| id | utm.eprints-95006 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:04:38Z |
| publishDate | 2021 |
| publisher | Insight Society |
| record_format | dspace |
| spelling | utm.eprints-950062022-04-29T22:32:46Z http://eprints.utm.my/95006/ The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality Dahliyusmanto, Dahliyusmanto Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis QA75 Electronic computers. Computer science 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. Insight Society 2021-10 Article PeerReviewed application/pdf en http://eprints.utm.my/95006/1/AjuneWanis2021_TheComparisonofGrayscaleImageEnhancement.pdf Dahliyusmanto, Dahliyusmanto and Anggara, Devi Willieam and Mohd. Rahim, Mohd. Shafry and Ismail, Ajune Wanis (2021) 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, 11 (5). pp. 2104-2111. ISSN 2088-5334 http://dx.doi.org/10.18517/IJASEIT.11.5.10990 DOI:10.18517/IJASEIT.11.5.10990 |
| spellingShingle | QA75 Electronic computers. Computer science Dahliyusmanto, Dahliyusmanto Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| title | The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| title_full | The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| title_fullStr | The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| title_full_unstemmed | The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| title_short | The comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| title_sort | comparison of grayscale image enhancement techniques for improving the quality of marker in augmented reality |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/95006/1/AjuneWanis2021_TheComparisonofGrayscaleImageEnhancement.pdf |
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