Camera sensors rely on global or rolling shutter functions to expose an image. This fixed
function approach severely limits the sensors' ability to capture high-dynamic-range (HDR) …

Conventional machine vision systems have separate perception, memory, and processing
architectures, which may exacerbate the increasing need for ultrahigh image processing …

Computationally removing the motion blur introduced by camera shake or object motion in a
captured image remains a challenging task in computational photography. Deblurring …

Conventional image sensors digitize high-resolution images at fast frame rates producing a
large amount of data that needs to be transmitted off the sensor for further processing. This is …

Depth from defocus is an important mechanism that enables vision systems to perceive
depth. While machine vision has developed several algorithms to estimate depth from the …

Video object detection is a widely studied topic and has made significant progress in the
past decades. However, the feature extraction and calculations in existing video object …

Visual input can be used to recover the 3-D structure of a scene by estimating distances
(depth) to the observer. Depth estimation is performed in various applications, such as …

Reinterpretable cameras are defined by their post-processing capabilities that exceed
traditional imaging. We present" SoDaCam" that provides reinterpretable cameras at the …

Abstract Convolutional Neural Networks (CNNs) have revolutionised the Computer Vision
discipline in the last few years. CNNs now are state of the art methods to solve almost all …

The traditional machine vision systems use separate architectures for perception, memory,
and processing. This approach may hinder the growing demand for high image processing …