Abstract
This paper proposed the development of an effective algorithm for marker detection from products for augmented reality by Speeded-Up Robust Features (SURF) algorithm that provided the efficiency in term of speed and accuracy. The SURF alorithm is consisted of 3 processes that are (1) feature extraction calculates the interested point and interested descriptions, (2) feature matching is that the correlation of all points is calculated from the distance of similarity of featuers, and (3) logo indentification is used to find the four corner point of the logo. This experiment is conducted from the recording video at 100 frames with resolution of 640 \(\times \) 360 pixels and logo appeared all frames. Objects used in the experiment are consists of 3 shapes, cylindrical (can), rectangular (bag), and bottle. The logo template is divided into 5 sizes. The result of experiment found that the best detection accuracy of logo detection is from the size of 100\(\,\times \,\)100 pixels. The accuracy of the region of marker detection compared with ground truth shows that the bag is equal to 94.96 %, can is equal to 93.99 %, and bottle is equal to 91.01 %, respectively. The difference of the logo is not affected with the computational time. However, the fast moving camera creates the blurred image and the reflection on the packaging creats a shiny surface which affects with the accuracy.
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This research is supported by Mahasarakham University.
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Boonrod, T., Chomphuwiset, P., Jareanpon, C. (2016). The Marker Detection from Product Logo for Augmented Reality Technology. In: Huynh, VN., Inuiguchi, M., Le, B., Le, B., Denoeux, T. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2016. Lecture Notes in Computer Science(), vol 9978. Springer, Cham. https://doi.org/10.1007/978-3-319-49046-5_36
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