Muhammad Ichwan
Papers
1
Total Citations
5
H-Index
1
About
Muhammad Ichwan is a researcher in computer vision and human-computer interaction, with a particular focus on hand gesture recognition. His most-cited work, "Pengenalan Pose Tangan Menggunakan HuMoment" (2017, 5 citations), introduces a shape-based recognition method using Hu Moments to interpret hand poses for interaction and sign language applications. This contribution highlights his interest in making technology more accessible through intuitive, non-verbal interfaces. While his citation count is modest, Ichwan's work addresses a foundational challenge in pattern recognition—enabling computers to understand human gestures without complex hardware. His research sits at the intersection of image processing, machine learning, and assistive technology, offering practical pathways for developing gesture-controlled systems. By exploring how simple shape descriptors can reliably classify hand poses, Ichwan contributes to the broader goal of creating seamless, natural interactions between humans and machines. His work is particularly relevant for students and researchers interested in low-cost, computationally efficient approaches to gesture recognition, especially in contexts where accessibility and simplicity are paramount.
Research Focus
Key Achievements
Top Papers
- 1Pengenalan Pose Tangan Menggunakan HuMoment5 citations · 2017