A custom made multispectral camera was used to collect a novel dataset of images of untreated lettuce leaves or leaves treated with vinegar, oil, or a combination of these. The camera captured image data at 10 wavelengths ∈[380nm,980nm] across the electromagnetic spectrum in the visible and NIR (near-infrared) regions. Imaging was done in a lab environment with the presence of ambient light.
Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. This dataset contains 388 images of structures with and without dirt buildup.
One can use this dataset for experimentation on classifying the dirt buildup.
The data are separated into folders that specify each class of the dataset: Clean and Dirty.
As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (CURE-OR) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In CURE
Image name format :
1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office
1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top
No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise
A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.
We proposed a new dataset, HazeRD, for benchmarking dehazing algorithms under realistic haze conditions. As opposed to prior datasets that made use of synthetically generated images or indoor images with unrealistic parameters for haze simulation, our outdoor dataset allows for more realistic simulation of haze with parameters that are physically realistic and justified by scattering theory.
I) Installation:Unzip the source code archive. This will create a sub-directory "HazeRD", which is intended to be the directory where you run the MATLAB script. II(a) HazeRD Dataset Generation:Run script demo_simu_haze.m to generate the HazeRD datasetII(b) Computing fidelity metrics for dehazed images with respect to originals:Run script demo_metrics.m to compute the fidelity metrics for dehazed images.Please see the README.txt for detailed instructions.