Image Processing

This dataset is composed by both real and sythetic images of power transmission lines, which can be fed to deep neural networks training and applied to line's inspection task. The images are divided into three distinct classes, representing power lines with different geometric properties. The real world acquired images were labeled as "circuito_real" (real circuit), while the synthetic ones were identified as "circuito_simples" (simple circuit) or "circuito_duplo" (double circuit). There are 348 total images for each class, 232 inteded for training and 116 aimed for validation/testing.

Researchers are becoming increasingly interested in dorsal hand vein biometrics because of their characteristics. These characteristics can be summarized as follows: it does not need contact with the capture device, cannot be forged, does not change over time, and provides high accuracy. Recognition systems of the dorsal hand rely on how the collected images captured from the device are good. Near-infrared (NIR) light is used to distinguish veins from the back of the hand.

These simulated live cell microscopy sequences were generated by the CytoPacq web service https://cbia.fi.muni.cz/simulator [R1]. The dataset is composed of 51 2D sequences and 41 3D sequences. The 2D sequences are divided into distinct 44 training and 7 test sets. The 3D sequences are divided into distinct 34 training and 7 test sets. Each sequence contains up to 200 frames.

Datasets for image and video aesthetics

1. Video Dataset : 107 videos 

This dataset has videos that can be framed into images.

Color contrast,Depth of Field[DoF],Rule of Third[RoT] attributes

that affect aesthetics can be extracted from the video datasets.

2.Slow videos and Fast videos can be assessed for motion

affecting aesthetics