Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology
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 conv...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Little Lion Scientific
2020
|
| Subjects: | |
| Online Access: | http://eprints.utm.my/90015/1/MohdShafryMohdRahim2020_GrayscaleImageEnhancementforEnhancingFeaturesDetection.pdf |
| _version_ | 1796864944371662848 |
|---|---|
| author | Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis Machfiroh, Runik Budiman, Arif Rahmansyah, Aris Dahliyusmanto, Dahliyusmanto Atan, Noor Azean |
| author_facet | Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis Machfiroh, Runik Budiman, Arif Rahmansyah, Aris Dahliyusmanto, Dahliyusmanto Atan, Noor Azean |
| author_sort | Anggara, Devi Willieam |
| collection | ePrints |
| description | 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. |
| first_indexed | 2024-03-05T20:49:30Z |
| format | Article |
| id | utm.eprints-90015 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:49:30Z |
| publishDate | 2020 |
| publisher | Little Lion Scientific |
| record_format | dspace |
| spelling | utm.eprints-900152021-03-31T05:04:00Z http://eprints.utm.my/90015/ Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis Machfiroh, Runik Budiman, Arif Rahmansyah, Aris Dahliyusmanto, Dahliyusmanto Atan, Noor Azean QA75 Electronic computers. Computer science 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. Little Lion Scientific 2020-07 Article PeerReviewed application/pdf en http://eprints.utm.my/90015/1/MohdShafryMohdRahim2020_GrayscaleImageEnhancementforEnhancingFeaturesDetection.pdf Anggara, Devi Willieam and Mohd. Rahim, Mohd. Shafry and Ismail, Ajune Wanis and Machfiroh, Runik and Budiman, Arif and Rahmansyah, Aris and Dahliyusmanto, Dahliyusmanto and Atan, Noor Azean (2020) Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology. Journal of Theoretical and Applied Information Technology, 98 (13). pp. 2671-2683. ISSN 1992-8645 http://www.jatit.org/volumes/ninetyeight13.php |
| spellingShingle | QA75 Electronic computers. Computer science Anggara, Devi Willieam Mohd. Rahim, Mohd. Shafry Ismail, Ajune Wanis Machfiroh, Runik Budiman, Arif Rahmansyah, Aris Dahliyusmanto, Dahliyusmanto Atan, Noor Azean Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology |
| title | Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology |
| title_full | Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology |
| title_fullStr | Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology |
| title_full_unstemmed | Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology |
| title_short | Grayscale image enhancement for enhancing features detection in marker-less augmented reality technology |
| title_sort | grayscale image enhancement for enhancing features detection in marker less augmented reality technology |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/90015/1/MohdShafryMohdRahim2020_GrayscaleImageEnhancementforEnhancingFeaturesDetection.pdf |
| work_keys_str_mv | AT anggaradeviwillieam grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT mohdrahimmohdshafry grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT ismailajunewanis grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT machfirohrunik grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT budimanarif grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT rahmansyaharis grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT dahliyusmantodahliyusmanto grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology AT atannoorazean grayscaleimageenhancementforenhancingfeaturesdetectioninmarkerlessaugmentedrealitytechnology |