Reverse-Engineering Biological Cameras

Abstract:  Biological vision systems are efficient, robust, and well-adapted to specific tasks in specific environments. By many metrics, they continue to outperform the state of the art in artificial vision. Lessons from natural vision are particularly valuable for computational imaging systems, because eyes and brains evolve together to reveal scene information. By analyzing animals’ behaviors, brains, eyes, and environments, we can uncover computational models that enable better camera designs. This talk will describe cameras developed with a blend of techniques from the physical, computational, and biological sciences, and discuss tools for finding useful inspiration in the natural world.

Bio:  Emma Alexander is an assistant professor of computer science at Northwestern University, where she leads the Bio Inspired Vision Lab. Her training in physics (BS Yale), computer science (BS Yale, MS PhD Harvard), and vision science (postdoc, UC Berkeley) support her interest in low-level, physics-based, bio-inspired artificial vision. Her work won best student paper at ECCV 2016 and best demo at ICCP 2018.

Reverse-Engineering Biological Cameras

Abstract:  Biological vision systems are efficient, robust, and well-adapted to specific tasks in specific environments. By many metrics, they continue to outperform the state of the art in artificial vision. Lessons from natural vision are particularly valuable for computational imaging systems, because eyes and brains evolve together to reveal scene information. By analyzing animals’ behaviors, brains, eyes, and environments, we can uncover computational models that enable better camera designs. This talk will describe cameras developed with a blend of techniques from the physical, computational, and biological sciences, and discuss tools for finding useful inspiration in the natural world.

Bio:  Emma Alexander is an assistant professor of computer science at Northwestern University, where she leads the Bio Inspired Vision Lab. Her training in physics (BS Yale), computer science (BS Yale, MS PhD Harvard), and vision science (postdoc, UC Berkeley) support her interest in low-level, physics-based, bio-inspired artificial vision. Her work won best student paper at ECCV 2016 and best demo at ICCP 2018.