File:Machine-Learning-of-Hierarchical-Clustering-to-Segment-2D-and-3D-Images-pone.0071715.s004.ogv
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Size of this JPG preview of this OGG file: 800 × 264 pixels. Other resolutions: 320 × 106 pixels | 972 × 321 pixels.
Original file (Ogg Theora video file, length 55 s, 972 × 321 pixels, 651 kbps, file size: 4.27 MB)
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[edit]DescriptionMachine-Learning-of-Hierarchical-Clustering-to-Segment-2D-and-3D-Images-pone.0071715.s004.ogv |
English: Agglomeration by classifiers trained using GALA (left) or a flat learning protocol (right). It rapidly becomes obvious that the GALA-trained classifier can confidently merge regions of arbitrary size, while the flat-trained classifier hesitates to continue merging once it encounters moderately-sized regions. |
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Source | Video S1 from Nunez-Iglesias J, Kennedy R, Parag T, Shi J, Chklovskii D (2013). "Machine Learning of Hierarchical Clustering to Segment 2D and 3D Images". PLOS ONE. DOI:10.1371/journal.pone.0071715. PMC: 3748125. | ||
Author | Nunez-Iglesias J, Kennedy R, Parag T, Shi J, Chklovskii D | ||
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Date/Time | Thumbnail | Dimensions | User | Comment | |
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current | 09:58, 26 August 2013 | 55 s, 972 × 321 (4.27 MB) | Open Access Media Importer Bot (talk | contribs) | Automatically uploaded media file from Open Access source. Please report problems or suggestions here. |
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Author | Nunez-Iglesias J, Kennedy R, Parag T, Shi J, Chklovskii D |
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Usage terms | http://creativecommons.org/licenses/by/3.0/ |
Image title | Agglomeration by classifiers trained using GALA (left) or a flat learning protocol (right). It rapidly becomes obvious that the GALA-trained classifier can confidently merge regions of arbitrary size, while the flat-trained classifier hesitates to continue merging once it encounters moderately-sized regions. |
Software used | Xiph.Org libtheora 1.1 20090822 (Thusnelda) |
Date and time of digitizing | 2013 |