This paper focuses on the emerging Infrared-Visible cross-modal person re-identification
task (IV-ReID), which takes infrared images as input and matches with visible color images …

Color image dehazing using the near-infrared Page 1 COLOR IMAGE DEHAZING USING THE
NEAR-INFRARED Lex Schaul, Clément Fredembach, and Sabine Süsstrunk Ecole …

The camera is one of the most important sensors for an autonomous vehicle (AV) to perform
Environment Perception and Simultaneous Localization and Mapping (SLAM). To secure …

Near-infrared (NIR) light has stronger penetration capability than visible light due to its long
wavelengths and is thus less scattered by particles in the air. This makes it desirable for …

Image visibility is affected by the presence of haze, fog, smoke, aerosol, etc. Image dehazing
using either single visible image or visible and near-infrared (NIR) image pair is often …

Convolutional neural networks rely on image texture and structure to serve as discriminative
features to classify the image content. Image enhancement techniques can be used as …

For vehicles to navigate autonomously, they need to perceive and understand their
immediate surroundings. Currently, cameras are the preferred sensors, due to their high …

Near-infrared (NIR) band sensors capture digital images of scenes under special conditions
such as haze, fog, overwhelming light or mist, where visible (VS) band sensors get …

Semantic segmentation is a challenging and important task in computer vision.
Convolutional neural networks (CNNs) have demonstrated their outstanding performances …

We present a machine learning technique for estimating absolute, per-pixel depth using any
conventional monocular 2D camera, with minor hardware modifications. Our approach …