The rapid outbreak of COVID-19 due to the novel coronavirus SARS-COV-2 is the biggest issue faced by mankind today. It is important to detect the positive cases as early as possible to prevent the further spread of this pandemic.

Categories:
3568 Views

To improve reproductivity of our papar, we would upload experimental data and resources of evaluations.

Categories:
124 Views

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

Instructions: 

Dataset Characteristics and Filename Formats

 

The "CoMMonS_FullResolution" folder includes 6912 full-resolution images (2560x1920). The "CoMMonS_Sampled" folder includes sampled images (resolution: 300x300), which are sampled from full-resolution images with different positions (x, y), rotation angles (r), zoom levels (z), a touching direction ("pile"), a lightness condition ("l5"), and a camera function setting ("ed3u"). This "CoMMonS_Sampled" folder is an example of a dataset subset for training and testing (e.g. 5: 1). Our dataset focuses on material characterization for one material (fabric) in terms of one of three properties (fiber length, smoothness, and toweling effect), facilitating a fine-grained texture classification. In this particular case, the dataset is used for a standard supervised problem of material quality evaluation. It takes fabric samples with human expert ratings as training inputs, and takes fabric samples without human subject ratings as testing inputs to predict quality ratings of the testing samples. The texture patches are classified into 4 classes according to each surface property measured by human sense of touch. For example, the human expert rates surface fiber length into 4 levels, from 1 (very short) to 4 (long), and similarly for smoothness and toweling effect. In short, the "CoMMonS_Sampled" folder includes 9 subfolders, each of which includes both sampled images and attribute class labels.

Categories:
558 Views

As one of the research directions at OLIVES Lab @ Georgia Tech, we focus on recognizing textures and materials in real-world images, which plays an important role in object recognition and scene understanding. Aiming at describing objects or scenes with more detailed information, we explore how to computationally characterize apparent or latent properties (e.g. surface smoothness) of materials, i.e., computational material characterization, which moves a step further beyond material recognition.

Categories:
203 Views

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.

Instructions: 

 

Categories:
295 Views

The graphical user interface (GUI) for memristor is designed under MATLAB GUIDE to study and visualize it's characteristics in interactive way. The GUI is developed so as to study by taking sinusoidal, square and triangular signal waveform only.

Categories:
366 Views

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.

Instructions: 

The data are separated into folders that specify each class of the dataset: Clean and Dirty.

Categories:
457 Views

An image dataset including five types of weather conditions (cloudy, sunny, foggy, rainy and snowy) was constructed.

 This dataset, called FWID, includes 4000 images for each weather category, leading to a total of 20000 images. 

Categories:
1024 Views

An image dataset including five types of weather conditions (cloudy, sunny, foggy, rainy and snowy) was constructed.

 This dataset, called FWID, includes 4000 images for each weather category, leading to a total of 20000 images. 

Categories:
194 Views

An image dataset including five types of weather conditions (cloudy, sunny, foggy, rainy and snowy) was constructed.

 This dataset, called FWID, includes 4000 images for each weather category, leading to a total of 20000 images. 

Categories:
2602 Views

Pages