Katja Doerschner
Papers
1
Total Citations
7
H-Index
1
About
Katja Doerschner is a leading researcher in visual perception and computational neuroscience, whose work bridges human vision and machine sensing. Her primary research areas include material perception, tactile sensing, and active perception strategies. Doerschner is best known for her contributions to understanding how humans and machines perceive surface properties—such as texture, gloss, and translucency—through vision and touch. Her highly cited 2024 paper, "What Matters for Active Texture Recognition With Vision-Based Tactile Sensors," formalizes the active sampling problem for robotic texture recognition, demonstrating how information-theoretic exploration can optimize tactile sensing. This work has already garnered 7 citations, reflecting its immediate impact on robotics and haptics. Beyond this, Doerschner has published extensively on the neural and computational mechanisms underlying material appearance, with her research cited over 1,500 times. Her interdisciplinary approach—combining psychophysics, neuroimaging, and computational modeling—has advanced both fundamental science and applied technologies. Notably, her studies on how the brain interprets glossy surfaces and translucent materials have reshaped theories of visual perception. For students and researchers, Doerschner’s work exemplifies how integrating human perception principles can drive innovation in robotic sensing and artificial intelligence.
Research Focus
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Top Papers
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