This repository contains code to apply the ESPER method to quasi-continuum models of biomolecules exhibiting multiple degrees of freedom, as described in Seitz et al. (2022, IEEE TCI). As inputs into ESPER, detailed instructions are also provided for generating custom synthetic datasets with increasing complexity to mirror known cryo-EM image attributes.

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In this paper, we propose a framework for 3D human pose estimation using a single 360° camera mounted on the user's wrist. Perceiving a 3D human pose with such a simple setup has remarkable potential for various applications (e.g., daily-living activity monitoring, motion analysis for sports training). However, no existing method has tackled this task due to the difficulty of estimating a human pose from a single camera image in which only a part of the human body is captured, and because of a lack of training data.

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Document layout analysis (DLA) plays an important role for identifying and classifying the different regions of digital documents in the context of Document Understanding tasks. In light of this, SciBank seeks to provide a considerable amount  of data from text (abstract, text blocks, caption, keywords, reference, section, subsection, title), tables, figures and equations (isolated equations and inline equations) of 74435 scientific articles pages. Human curators validated that these 12 regions were properly labeled.

Instructions: 
  1. Datasheet_for_SciBank_Dataset.pdf. The Datasheet for this Dataset includes all the relevant details of the composition, collection, preprocessing, cleaning and labeling process used to construct SciBank.
  2. METADATA_FINAL.csv. Each row represent the metadata for every region according to the following fields
    1. Folder: the name of the folder within the main folder PAPER_TAR
    2. Page: png filename of the image where the region is located
    3. Height_Page, Width_Page: dimensions in pixels of the png image page
    4. CoodX, CoodY, Width, Height: coordinates of the region in pixels 
    5. Class: region label
    6. Page_in_pdf: page number within the PDF containing the page of the region
  3. PAPER_TAR folder includes the PNG images from all paper pages and the PDF papers in hierarchical subdirectories, both referenced by METADATA_FINAL.csv. 

 

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DATA PROVIDED PRIOR TO ACCEPTANCE OF THE ASSOCIATED MANUSCRIPT.

This dataset contains video sequences and stereo reconstruction results supporting the IEEE Access contribution "Stereo laryngoscopic impact site prediction for droplet-based stimulation of the laryngeal adductor reflex" (J. F. Fast et al.).

See readme file for further information.

Instructions: 

See provided readme file for instructions.

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A collection of about 30K images that represents figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST).

Instructions: 

These files are in PNG format. Due to upload size limit, these files are divided into five zip files organized by year. 

The full collection in one-file is about 21.2G and can also be found online at http://www.cse.osu.edu/~chen.8028/VIS30K/VIS30K.tar.gz. 

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768 Views

The dataset contains high-resolution microscopy images and confocal spectra of semiconducting single-wall carbon nanotubes. Carbon nanotubes allow down-scaling of electronic components to the nano-scale. There is initial evidence from Monte Carlo simulations that microscopy images with high digital resolution show energy information in the Bessel wave pattern that is visible in these images. In this dataset, images from Silicon and InGaAs cameras, as well as spectra, give valuable insights into the spectroscopic properties of these single-photon emitters.

Instructions: 

The dataset is generated with docker containers from the measurement data. The measured data is in Igor Binary Waves. The specific format can be read with a custom reader an processed with various tools.

Processing will be applied automatically to various output formats using docker containers.

 

See current development status and dataset description will be updated on

https://gitlab.com/ukos-git/nanotubes

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537 Views

This dataset includes all letters from Turkish Alphabet in two parts. In the first part, the dataset was categorized by letters, and the second part dataset was categorized by fonts. Both parts of dataset includes the features mentioned below.

  • 72, 20 AND 8 POINT LETTERS
  • UPPER AND LOWER CASES

The all characters in Turkish Alphabet are included (a, b, c, ç, d, e, f, g, ğ, h, ı, i, j, k, l, m, n, o, ö, p, r, s, ş, t, u, ü, v, y, z).

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867 Views

The Dataset

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.

Instructions: 

Onera Satellite Change Detection dataset

 

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Authors: Rodrigo Caye Daudt, rodrigo.daudt@onera.fr

Bertrand Le Saux, bls@ieee.org

Alexandre Boulch, alexandre.boulch@valeo.ai

Yann Gousseau, yann.gousseau@telecom-paristech.fr

 

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About: This dataset contains registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites of the Copernicus program. Pixel-level urban change groundtruth is provided. In case of discrepancies in image size, the older images with resolution of 10m per pixel is used. Images vary in spatial resolution between 10m, 20m and 60m. For more information, please refer to Sentinel-2 documentation.

 

For each location, folders imgs_1_rect and imgs_2_rect contain the same images as imgs_1 and imgs_2 resampled at 10m resolution and cropped accordingly for ease of use. The proposed split into train and test images is contained in the train.txt and test.txt files.

For downloading and cropping the images, the Medusa toolbox was used: https://github.com/aboulch/medusa_tb

For precise registration of the images, the GeFolki toolbox was used. https://github.com/aplyer/gefolki

 

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Labels: The train labels are available in two formats, a .png visualization image and a .tif label image. In the png image, 0 means no change and 255 means change. In the tif image, 0 means no change and 1 means change.

<ROOT_DIR>//cm/ contains: - cm.png - -cm.tif

Please note that prediction images should be formated as the -cm.tif rasters for upload and evaluation on DASE (http://dase.grss-ieee.org/).

(Update June 2020) Alternatively, you can use the test labels which are now provided in a separate archive, and compute standard metrics using the python notebook provided in this repo, along with a fulll script to train and classify fully-convolutional networks for change detection: https://github.com/rcdaudt/fully_convolutional_change_detection 

 

##################################################

Citation: If you use this dataset for your work, please use the following citation:

@inproceedings{daudt-igarss18,

author = {{Caye Daudt}, R. and {Le Saux}, B. and Boulch, A. and Gousseau, Y.},

title = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},

booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS'2018)},

venue = {Valencia, Spain},

month = {July},

year = {2018},

}

 

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Copyright: Sentinel Images: This dataset contains modified Copernicus data from 2015-2018. Original Copernicus Sentinel Data available from the European Space Agency (https://sentinel.esa.int).

Change labels: Change maps are released under Creative-Commons BY-NC-SA. For commercial purposes, please contact the authors.

 

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15356 Views

The odometric model is simulated herein. We described the trajectory of such one odometric model, with the delta of the heading angle given as one parameter of the simulation. The iterations show that the trajectory is well in the continuity of the variations of the heading angle. Moreover the distance in X and in Y are shown for the vehicle to be driven in the trajectory of the odometric model. Moreover I have displayed the linear approximation then. This is a tiny approximation made based upon the equations of the odometric model.

Instructions: 

Please take the odometric model in the context.

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209 Views

This dataset provides digital images and videos of surface ice conditions were collected from two Alberta rivers - North Saskatchewan River and Peace River - in the 2016-2017 winter seasons.

Images from North Saskatchewan River were collected using both Reconyx PC800 Hyperfire Professional game cameras mounted on two bridges in Edmonton as well as a Blade Chroma UAV equipped with a CGO3 4K camera at the Genesee boat launch.

Data for the Peace River was collected using only the UAV at the Dunvegan Bridge boat launch and Shaftesbury Ferry crossing.

Instructions: 

Python code and instructions for using the dataset are available in this repository: https://github.com/abhineet123/river_ice_segmentation

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669 Views

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