THE INVESTIGATION ON ARABIC WORD POSE ESTIMATION ALGORITHM AS MARKER FOR AUGMENTED REALITY APPLICATION
This study investigates which combination of matching technique with Infinitesimal Plane-Based Pose Estimation (IPPE) that suits better in estimating the pose of Arabic text images without character segmentation. The pattern matching technique involves are Speeded-Up Robust Features (SURF) and Affin...
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| Main Authors: | , |
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| Format: | Monograph |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/72034/1/Profile%20of%20Final%20Report_RAGS_2017.pdf http://irep.iium.edu.my/72034/ |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English |
| Summary: | This study investigates which combination of matching technique with Infinitesimal Plane-Based Pose Estimation (IPPE) that suits better in estimating the pose of Arabic text images without character segmentation. The pattern matching technique involves are Speeded-Up Robust Features (SURF) and Affine Scale Invariant Feature Transform (ASIFT). The experiment is demonstrated in Arabic word images from different angles of viewpoints. The algorithms are tested on a dataset chosen from a few words within Surah Al-Fatihah in the Quran. The total of 260 images was taken from left and right side of the image. Then, a set of sub-words were recognized and tested the performance. This study will focus on comparing the performance of the technique against Arabic words in two sub-words or one sub-word form. We will evaluate the performance through analyzing the matching accuracy rate and how it affects the pose estimation. Based on results obtained for the pattern matching technique performance on Arabic scripts, SURF shows a better accuracy rate and execution time compared to another algorithm. This experiment result is used as a guide in estimating a pose of the target images in different sub-words. The overall results of the study signify that good IPPE pose does not rely on the accuracy rate of matching inliers with original interest points. The study also demonstrates that one sub-words shows a better accuracy rate than with two sub-words cause by unnecessary interest points detected. |
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