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Understanding Infographics through Textual and Visual Tag Prediction
Zoya Bylinskii, Sami Alsheikh, Spandan Madan +5
We introduce the problem of visual hashtag discovery for infographics: extracting visual elements from an infographic that are diagnostic of its topic. Given an infographic as inpu…
Learning Visual Importance for Graphic Designs and Data Visualizations
Zoya Bylinskii, Nam Wook Kim, Peter O'Donovan +6
Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most importa…
Anisotropic EM Segmentation by 3D Affinity Learning and Agglomeration
Toufiq Parag, Fabian Tschopp, William Grisaitis +6
The field of connectomics has recently produced neuron wiring diagrams from relatively large brain regions from multiple animals. Most of these neural reconstructions were computed…
Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking
James Tompkin, Kwang In Kim, Hanspeter Pfister +1
Large databases are often organized by hand-labeled metadata, or criteria, which are expensive to collect. We can use unsupervised learning to model database variation, but these m…
Morphological Error Detection in 3D Segmentations
David Rolnick, Yaron Meirovitch, Toufiq Parag +5
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans,…
Guided Proofreading of Automatic Segmentations for Connectomics
Daniel Haehn, Verena Kaynig, James Tompkin +2
Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual…