Endoscopy is a widely used clinical procedure for the early detection of cancers in hollow-organs such as oesophagus, stomach, and colon. Computer-assisted methods for accurate and temporally consistent localisation and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos which is critical for monitoring and surgical planning. Innovations have the potential to improve current medical practices and refine healthcare systems worldwide.

Last Updated On: 
Sat, 02/27/2021 - 05:11

The data set includes three sub-data sets, namely the DAGM2007 data set, the ground crack data set, and the Yibao bottle cap defect data set, which are divided into a training set and a test set, in which the positive and negative samples are unbalanced.


Nextmed project is a software platform for the segmentation and visualization of medical images. It consist on a series of different automatic segmentation algorithms for different anatomical structures and  a platform for the visualization of the results as 3D models.

This dataset contains the .obj and .nrrd files that correspond to the results of applying our automatic lung segmentation algorithm to the LIDC-IDRI dataset.

This dataset relates to 718 of the 1012 LIDC-IDRI scans.


The file consists in a folder for each result whith the .obj and .nrrd files generated by the Nextmed algorithms.


Abstract: Recent advances in computer vision and deep learning are allowing researchers to develop automated environment recognition systems for robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms (e.g., convolutional neural networks) for recognizing the human walking environment.


*Details on the ExoNet database are provided in the references above. Please email Brokoslaw Laschowski (blaschow@uwaterloo.ca) for any additional questions and/or technical assistance. 


These three datasets cover Western, Chinese and Japanese food used for food instance counting and segmentation evaluation.


This is a small dataset as a part of huge dataset of breast cancer images. The images are mammograms. 


The data made available are the simulations of a time-resolved Monte Carlo model for use in quantitative as well as qualitative analysis of different types of particle atmospheres.


1. Set the geometry

2. Define the atmosphere

   2.1 Define the scattering profile of each type of particle in the atmosphere.

   2.2 Define the relative amount of each type of particle.

   2.2 Define the mean free path.

3. Define other test variables

   3.1 Temperature

   3.2 Refraction index (complex or real)

4. Run the simulations


5. With the data obtained, perform data analysis.


Calibration datasets used in the article Standard Plenoptic Cameras Mapping to Camera Arrays and Calibration based on DLT. These datasets were acquired with a Lytro Illum camera using two calibration grids with different sizes: 8 × 6 grid of 211 × 159 mm (Big Pattern) with approximately 26.5 mm cells, and 20×20 grid of 121.5 × 122 mm (Small Pattern) with approximately 6.1 mm cells. Each dataset acquired is composed of 66 fully observable poses of the calibration pattern.


The dataset is divided into the following zip files:

  • GD44M00145_WhiteImages: White image database of the Lytro Illum camera used to acquire the datasets.

  • Big Pattern 2D - Full: Calibration dataset with 66 poses of the big calibration grid.

  • Big Pattern 2D - Sample: Calibration dataset with 10 poses of the big calibration grid.

  • Big Pattern 2D - Sample Reduced: Calibration dataset with 5 poses of the big calibration grid.

  • Small Pattern 2D - Full: Calibration dataset with 66 poses of the small calibration grid.

  • Small Pattern 2D - Sample: Calibration dataset with 10 poses of the small calibration grid.

  • Small Pattern 2D - Sample Reduced: Calibration dataset with 5 poses of the small calibration grid.

  • Object: Objects dataset with the same acquisition conditions as the calibration datasets.

  • PlenCalCVPR2013Datasets: Lytro images used in the article for Lytro 1st generation calibration.


In order to obtain the lightfield associated with each image, you should read the Lytro raw image files (.lfp) using Dansereau's calibration toolbox (https://github.com/doda42/LFToolbox) and the white images provided here. The calibration of these datasets can be performed using the calibration toolbox provided in the article (http://www.isr.tecnico.ulisboa.pt/~nmonteiro/articles/plenoptic/tcsvt2019).


Research on damage detection of road surfaces has been an active area of research, but most studies have focused so far on the detection of the presence of damages. However, in real-world scenarios, road managers need to clearly understand the type of damage and its extent in order to take effective action in advance or to allocate the necessary resources. Moreover, currently there are few uniform and openly available road damage datasets, leading to a lack of a common benchmark for road damage detection.


Dataset for Telugu Handwritten Gunintam