Max von Buelow
Papers
1
Total Citations
2
H-Index
1
About
Max von Buelow is a researcher advancing the field of computer vision and deep learning, with a focus on transparency detection in digital imagery. His most notable contribution, "Distortion-Based Transparency Detection Using Deep Learning on a Novel Synthetic Image Dataset" (2023), introduces a pioneering approach that leverages synthetic data to train models for identifying transparent objects and surfaces—a notoriously challenging problem due to their lack of distinct texture and shape. By generating a specialized dataset that captures distortion patterns caused by transparent materials, von Buelow’s work enables more robust and scalable detection methods, with potential applications in autonomous navigation, augmented reality, and image forensics. Although his citation count is currently modest at two, the novelty of his synthetic dataset and distortion-based methodology positions his research as a foundational step for future studies in transparency perception. This work underscores his commitment to solving real-world visual recognition challenges through innovative data generation and deep learning techniques, marking him as an emerging voice in applied computer vision.
Research Focus
Key Achievements
Top Papers
- 1