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

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@article{IJASEIT10990,
   author = {- Dahliyusmanto and Devi Willieam Anggara and Mohd Shafry Mohd Rahim and Ajune Wanis Ismail},
   title = {The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {5},
   year = {2021},
   pages = {2104--2111},
   keywords = {Augmented reality; FAST corner detector; contrast enhancement; natural feature tracking; feature matching.},
   abstract = {

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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10990},    doi = {10.18517/ijaseit.11.5.10990} }

EndNote

%A Dahliyusmanto, -
%A Anggara, Devi Willieam
%A Mohd Rahim, Mohd Shafry
%A Ismail, Ajune Wanis
%D 2021
%T The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality
%B 2021
%9 Augmented reality; FAST corner detector; contrast enhancement; natural feature tracking; feature matching.
%! The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality
%K Augmented reality; FAST corner detector; contrast enhancement; natural feature tracking; feature matching.
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10990 %R doi:10.18517/ijaseit.11.5.10990 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 5 %@ 2088-5334

IEEE

- Dahliyusmanto,Devi Willieam Anggara,Mohd Shafry Mohd Rahim and Ajune Wanis Ismail,"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, pp. 2104-2111, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.10990.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Dahliyusmanto, -
AU  - Anggara, Devi Willieam
AU  - Mohd Rahim, Mohd Shafry
AU  - Ismail, Ajune Wanis
PY  - 2021
TI  - The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2  - 2021
SP  - 2104
EP  - 2111
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Augmented reality; FAST corner detector; contrast enhancement; natural feature tracking; feature matching.
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10990 DO - 10.18517/ijaseit.11.5.10990

RefWorks

RT Journal Article
ID 10990
A1 Dahliyusmanto, -
A1 Anggara, Devi Willieam
A1 Mohd Rahim, Mohd Shafry
A1 Ismail, Ajune Wanis
T1 The Comparison of Grayscale Image Enhancement Techniques for Improving the Quality of Marker in Augmented Reality
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 2104
OP 2111
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Augmented reality; FAST corner detector; contrast enhancement; natural feature tracking; feature matching.
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10990 DO - 10.18517/ijaseit.11.5.10990