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ZENODO
Dataset . 2016
License: CC 0
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2016
License: CC 0
Data sources: ZENODO
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Shadow Detection/Texture Segmentation Computer Vision Dataset

Authors: Newey, Charles; Jones, Owain; Dee, Hannah;

Shadow Detection/Texture Segmentation Computer Vision Dataset

Abstract

A simple computer vision dataset for shadow detection and texture analysis, specifically created to help test shadow detection algorithms (and texture segmentation algorithms) for mobile robots - that is, shadow detection with an active (moving) camera. The dataset is focused around texture analysis, so each image sequence contains shadows moving in front of a number of various textured surfaces. The dataset contains four main subfolders: "active", "artificial", "kondo", and "static". The "static" folder contains ground-truthed image sequences of textured surfaces with shadows moving over them, and the "active" folder contains ground-truthed image sequences of a camera travelling over textured surfaces. The "artificial" folder contains a computer-generated 3D scene with computer-generated ground truth, but note that texture is absent from all images within. Finally, the "kondo" folder contains a series of extremely challenging images captured from a webcam mounted to a Kondo bipedal robot. This final dataset is challenging because it contains a high level of noise, flicker and interference from electrical lighting, and the poor lighting conditions make for complex shadows with large penumbrae.

Related Organizations
Keywords

shadow, detection, segmentation, texture, computer, vision

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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