Continuous computer vision (CV) tasks increasingly rely on convolutional neural networks
(CNN). However, CNNs have massive compute demands that far exceed the performance …

Continuous mobile vision is limited by the inability to efficiently capture image frames and
process vision features. This is largely due to the energy burden of analog readout circuitry …

A major hurdle to frequently performing mobile computer vision tasks is the high power
consumption of image sensing. In this work, we report the first publicly known experimental …

Novel pervasive devices such as smart surveillance cameras and autonomous micro-UAVs
could greatly benefit from the availability of a computing device supporting embedded …

Energy efficiency is critical for running computer vision on battery-powered systems, such as
mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the …

Computer vision enables a wide range of applications in robotics/drones, self-driving cars,
smart Internet of Things, and portable/wearable electronics. For many of these applications …

The rapid emergence of head-mounted devices such as the Microsoft Holo-lens enables a
wide variety of continuous vision applications. Such applications often adopt deep-learning …

Continuous mobile vision is becoming increasingly important as it finds compelling
applications which substantially improve our everyday life. However, meeting the …

We consider the problem of continuous computer-vision based analysis of video streams
from mobile cameras over extended periods. Given high computational demands, general …

CMOS Image Sensors (CIS) are fundamental to emerging visual computing applications.
While conventional CIS are purely imaging devices for capturing images, increasingly CIS …