  Power consumption is a critical factor for the deployment of embedded
computer vision systems. We explore the use of computational cameras that
directly output binary gradient images to reduce the portion of the power
consumption allocated to image sensing. We survey the accuracy of binary
gradient cameras on a number of computer vision tasks using deep learning.
These include object recognition, head pose regression, face detection, and
gesture recognition. We show that, for certain applications, accuracy can be on
par or even better than what can be achieved on traditional images. We are also
the first to recover intensity information from binary spatial gradient
images--useful for applications with a human observer in the loop, such as
surveillance. Our results, which we validate with a prototype binary gradient
camera, point to the potential of gradient-based computer vision systems.
