Andrzej Seul

About

Andrzej Seul is a researcher whose work lies at the intersection of computer vision, robotics, and texture analysis. His most cited paper, "Classification of Textures for Autonomous Cleaning Robots Based on the GLCM and Statistical Local Texture Features" (2018), has garnered 5 citations and represents a focused contribution to the field of autonomous systems. In this work, Seul developed a method using Gray-Level Co-occurrence Matrices (GLCM) combined with statistical local texture features to enable cleaning robots to classify floor surfaces—such as carpet, tile, or wood—in real time. This innovation directly improves the adaptability and efficiency of autonomous cleaning robots, allowing them to adjust their cleaning strategies based on surface type. While his citation count is modest, the practical, application-driven nature of his research highlights its relevance to robotics and smart home technology. Seul’s work demonstrates a clear commitment to solving tangible problems in autonomous navigation and perception, bridging the gap between theoretical texture analysis and real-world robotic functionality.

Research Focus

Key Achievements

Top Papers

1. 1
   Classification of Textures for Autonomous Cleaning Robots Based on the GLCM and Statistical Local Texture Features

   5 citations · 2018

Key Collaborators

Contact & Links