Derry Alamsyah

About

Derry Alamsyah is a computer vision researcher whose work centers on advancing fingertip detection—a critical enabler for natural user interfaces, robotics, and 3D simulation. His most impactful contribution, “Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image” (2019), has garnered 40 citations and demonstrates a novel application of deep learning architectures to overcome the challenge of detecting fingertips in uniform, noisy backgrounds. By integrating Faster R-CNN with the Inception V2 network, Alamsyah improved detection accuracy in environments where traditional hand segmentation often fails. His follow-up work in Indonesian, “Deteksi Ujung Jari menggunakan Faster-RCNN dengan Arsitektur Inception v2 pada Citra Derau,” further explores robustness against image noise, though with a smaller citation footprint. Alamsyah’s research is notable for bridging state-of-the-art object detection frameworks with practical, real-time interaction systems. His contributions are particularly valuable for researchers developing touchless interfaces and robotic control systems, offering a reliable method for precise fingertip localization under constrained visual conditions.

Research Focus

Key Achievements

Top Papers

1. 1
   Faster R-CNN with Inception V2 for Fingertip Detection in Homogenous Background Image

   40 citations · 2019
2. 2
   Deteksi Ujung Jari menggunakan Faster-RCNN dengan Arsitektur Inception v2 pada Citra Derau

   2 citations · 2019

Key Collaborators

Contact & Links