Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching

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Ivan Fareza
Rusdie Busdin
Muhammad Ezar Al Rivan
Hafiz Irsyad

Abstract

Sign Language is a way to communicate to people with disabilities. American Sign Language (ASL) is one among other sign languages. Sign language image would be extracted using Edge Oriented Histogram (EOH). In Content-Based Image Retrieval, a feature from query image will be compared to database image to find out the best matching method so three matching methods will be used. The matching methods are Earth Mover Distance, Hausdorff Distance, and Sum of Absolute Difference. The smallest distance shows the strong similarity between query image and database image. The Sum of Absolute Difference is outperformed of other in case the most of relevant image can be retrieved. The order of methods to recognize alphabet (from the best one) is Sum of Absolute Difference following by Earth Mover Distance and Hausdorff Distance. Hausdorff Distance has smallest running time using 4 bin features. Earth Mover Distance has smallest running time using 6 bin features. Sum of Absolute Difference has smallest running time using 9 bin features, so the method can be recommended to recognize ASL.

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How to Cite
Fareza, I., Busdin, R., Al Rivan, M., & Irsyad, H. (2018). Pengenalan Alfabet Bahasa Isyarat Amerika Menggunakan Edge Oriented Histogram dan Image Matching. Jurnal Teknik Informatika Dan Sistem Informasi, 4(1), 82 -. Retrieved from https://journal.maranatha.edu/index.php/jutisi/article/view/1511
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