Karianto Leman

Papers

Total Citations

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About

Karianto Leman is a researcher in computer vision and intelligent surveillance systems, with a focus on human appearance analysis and face recognition. His work bridges the gap between low-level visual features and high-level semantic understanding, enabling more effective human-machine interaction and automated surveillance. Leman’s notable contributions include the development of a system for detecting sling bags and backpacks in surveillance footage, which enhances the semantic labeling of human appearance beyond typical attributes like clothing color or height—a valuable tool for searching archived video data. In face recognition, he proposed a novel architecture for incremental learning using Gabor features, allowing real-time robotic systems to recognize faces by continuously updating their knowledge base. Although his most-cited papers have modest citation counts (3 and 2 respectively), they represent foundational steps in practical, real-world applications of computer vision. Leman’s work underscores the importance of integrating contextual cues, such as carried objects, into automated visual systems, and his incremental learning approach offers a scalable solution for dynamic environments. His research continues to inspire advancements in surveillance, robotics, and human-computer interaction.