Many creative ideas are being proposed for image sensor designs, and these may be useful in applications ranging from consumer photography to computer vision. To understand and evaluate each new design, we must create a corresponding image-processing pipeline that transforms the sensor data into a form that is appropriate for the application. The need to design and optimize these pipelines is time-consuming and costly. I explain a method that combines machine learning and image systems simulation that automates the pipeline design. The approach is based on a new way of thinking of the image-processing pipeline as a large collection of local linear filters. Finally, I illustrate how the method has been used to design pipelines for consumer photography and mobile imaging.
Bio:
Brian A. Wandell is the first Isaac and Madeline Stein Family Professor. He joined the Stanford Psychology faculty in 1979 and is a member, by courtesy, of Electrical Engineering and Ophthalmology. Wandell is the founding director of Stanford's Center for Cognitive and Neurobiological Imaging, and a Deputy Director of the Stanford Neuroscience Institute. He is the author of the vision science textbook Foundations of Vision. His research centers on vision science, spanning topics from visual disorders, reading development in children, to digital imaging devices and algorithms for both magnetic resonance imaging and digital imaging. In 1996, together with Prof. J. Goodman, Wandell founded Stanford's Center for Image Systems Engineering.
