Methodology Used to Evaluate Computer Vision Algorithms in Adverse Weather Conditions

A conventional approach to nighttime visibility in adverse weather conditions

Scene Segmentation in Adverse Vision Conditions

Semantic Image Segmentation is the task of segmenting images into homogeneous regions with semantic meaning. In Computer Vision it means assigning a class-label from a known set of classes to every pixel in the image. The standard approach is to take some sample data, divide it into training and testing sets, and to learn the class distribution on the training set, followed by checking the qual...

AUV Hydrodynamics In Shallow Water During Adverse Weather Conditions

My long-term goal is to contribute, by means of an accurate hydrodynamics model, to efficient design, development and operation of autonomous underwater vehicles in energetic shallow waters. From a scientific viewpoint, the development of the AUV hydrodynamic model will contribute to a better understanding of wave-body-current interactions, nonlinear shallow-water waves, wave effects on stabili...

Uncertainty Comparison of Visual Sensing in Adverse Weather Conditions†

This paper focuses on flood-region detection using monitoring images. However, adverse weather affects the outcome of image segmentation methods. In this paper, we present an experimental comparison of an outdoor visual sensing system using region-growing methods with two different growing rules-namely, GrowCut and RegGro. For each growing rule, several tests on adverse weather and lens-stained...

Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

Ecological remote sensing is being transformed by three-dimensional (3D), multispectral measurements of forest canopies by unmanned aerial vehicles (UAV) and computer vision structure from motion (SFM) algorithms. Yet applications of this technology have out-paced understanding of the relationship between collection method and data quality. Here, UAV-SFM remote sensing was used to produce 3D mu...