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All Journal ComEngApp : Computer Engineering and Applications Journal Annual Research Seminar Sriwijaya Journal of Informatics and Applications Jurnal Generic Jurnal Abdimas Maduma
Osvari Arsalan
Universitas Sriwijaya
Humanities Computer Science & IT Education Engineering

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Journal : ComEngApp : Computer Engineering and Applications Journal

 1 
Object Reconstruction from 2D Drawing sketch to 3D Object Rossi Passarella; Osvari Arsalan
Computer Engineering and Applications Journal Vol 5 No 3 (2016)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.997 KB) | DOI: 10.18495/comengapp.v5i3.183

Abstract

Design engineer in the early phase of building up another product is  typically using a freehand sketching to communicate or illustrate the idea in the form of orthographic projection. This orthographic projection is based on viewpoint. A translation from 2D drawing  view point to 3D models is needed to help engineer to imagine the product preview in 3D. This procedure includes a tedious, so that automation is needed. The way to deal with this reproduction issue begin straightforwardly from 2D freehand portraying, by using the camera, the 2D drawing is captured and then transferred to a Personal Computer. Inside the computer, the image is processed with  filtering to find the view point zones. The view point zone than separate to 3 zones, each zone consists of the pixel coordinate. This coordinates are used to generated and processing of 3D voxel Image according to the form of geometries. A case study is presented in order to emphasize and discuss the proposed method
Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method Ahmad Zarkasi; Huda Ubaya; Kemahyanto Exaudi; Alif Almuqsit; Osvari Arsalan
Computer Engineering and Applications Journal Vol 12 No 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v12i3.444

Abstract

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.
Robot Vision Pattern Recognition of the Eye and Nose Using the Local Binary Pattern Histogram Method Zarkasi, Ahmad; Ubaya, Huda; Exaudi, Kemahyanto; Almuqsit, Alif; Arsalan, Osvari
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The local binary pattern histogram (LBPH) algorithm is a computer technique that can detect a person's face based on information stored in a database (trained model). In this research, the LBPH approach is applied for face recognition combined with the embedded platform on the actuator system. This application will be incorporated into the robot's control and processing center, which consists of a Raspberry Pi and Arduino board. The robot will be equipped with a program that can identify and recognize a human's face based on information from the person's eyes and nose. Based on the results of facial feature identification testing, the eyes were recognized 131 times (87.33%), and the nose 133 times (88.67%) out of 150 image data samples. From the test results, an accuracy rate of 88%, the partition rate of 95.23%, the recall of 30%, the specificity of 99%, and the F1-Score of 57.5% were obtained.
Co-Authors Aditya Aditya Aditya Putra Perdana Prasetyo Afriansyah, Indra Gifari Agung Juli Anda Ahmad Heryanto Ahmad Rifai Ahmad Rifai Ahmad Zarkasi Ahmad Zarkasi Al Amin Mulya Al Farissi Ali Bardadi Alif Almuqsit Almuqsit, Alif Alvi Syahrini Utami Anna Dwi Marjusalinah Atika Mailasari Bhakti Yudho Suprapto Buchari, Muhammad Ali Deris Stiawan Desty Rodiah Dian Palupi Rini Dian Palupi Rini Elin Darnela Ermatita - Fachrudin Abdau Fathan, Muhammad Rifqi Febrivia, Pretty Fujianti Firdaus Firdaus Hadipurnawan Satria Hidayatullah, Altundri Wahyu Huda Ubaya Huda Ubaya Kanda Januar Miraswan Kemahyanto Exaudi M. Fachrurrozi . Megah Mulya Muhammad Fachrurrozi Muhammad Qurhanul Rizqie Nabila Febriyanti Novi Yusliani Passarela, Rossi Primanita, Anggina Rahmat Fadli Isnanto Risky Tama Putri Rizki Kurniati Rizki Ramadandi Rossi Passarella Sarmayanta Sembiring Siti Nurmaini Vindriani, Marsella Yousnaidi, Rani Silvani Yulinda Ramadhana Yunita Yunita