Greg Slabaugh

About

Greg Slabaugh is a leading researcher in computer vision and medical image analysis, whose work bridges the gap between 3D reconstruction and intelligent human-machine interfaces. He is best known for his foundational contributions to volumetric scene reconstruction from photographs, a classic computer vision problem with applications in robotics, virtual reality, and entertainment. His highly cited survey on this topic (over 260 combined citations) remains a key reference for researchers tackling 3D modeling from 2D images. More recently, Slabaugh has made significant strides in biomedical engineering, particularly in myoelectric control for prosthetic limbs. His work on stacked sparse autoencoders for EMG-based hand motion classification (65 citations) demonstrates how deep learning can improve the robustness of pattern recognition in wearable prosthetics, addressing a critical barrier to user acceptance. By combining expertise in geometric computer vision with cutting-edge machine learning, Slabaugh’s research has had a tangible impact on both theoretical understanding and practical assistive technologies, making him a respected figure in the intersection of vision, AI, and healthcare.

Research Focus

Key Achievements

Top Papers

1. 1
   A Survey of Methods for Volumetric Scene Reconstruction from Photographs

   162 citations · 2001
2. 2
   A Survey of Methods for Volumetric Scene Reconstruction from Photographs

   107 citations · 2001
3. 3
   Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG

   65 citations · 2018

Key Collaborators

Contact & Links