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- Detecting viewer-perceived intended vector sketch connectivity
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Detecting viewer-perceived intended vector sketch connectivity Yin, Jerry
Abstract
Many sketch processing applications target precise vector drawings with accurately specified stroke intersections, yet free-form artist drawn sketches are typically inexact: strokes that are intended to intersect often stop short of doing so. While human observers easily perceive the artist intended stroke connectivity, manually or even semi-manually correcting drawings to generate correctly-connected outputs is tedious and highly time consuming. We propose a novel, robust algorithm that extracts viewer-perceived stroke connectivity from inexact free-form vector drawings by leveraging observations about local and global factors that impact human perception of inter-stroke connectivity. We employ the identified local cues to train classifiers that assess the likelihood that pairs of strokes are perceived as forming end-to-end or T-junctions based on local context. We then use these classifiers within an incremental framework that combines classifier-provided likelihoods with a more global, contextual, and closure-based analysis. We demonstrate our method on over 95 diversely sourced inputs, and validate it via a series of perceptual studies; participants prefer our outputs over the closest alternative by a factor of 9 to 1.
Item Metadata
Title |
Detecting viewer-perceived intended vector sketch connectivity
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Creator | |
Supervisor | |
Publisher |
University of British Columbia
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Date Issued |
2022
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Description |
Many sketch processing applications target precise vector drawings with accurately specified stroke intersections, yet free-form artist drawn sketches are typically inexact: strokes that are intended to intersect often stop short of doing so. While human observers easily perceive the artist intended stroke connectivity, manually or even semi-manually correcting drawings to generate correctly-connected outputs is tedious and highly time consuming. We propose a novel, robust algorithm that extracts viewer-perceived stroke connectivity from inexact free-form vector drawings by leveraging observations about local and global factors that impact human perception of inter-stroke connectivity. We employ the identified local cues to train classifiers that assess the likelihood that pairs of strokes are perceived as forming end-to-end or T-junctions based on local context. We then use these classifiers within an incremental framework that combines classifier-provided likelihoods with a more global, contextual, and closure-based analysis. We demonstrate our method on over 95 diversely sourced inputs, and validate it via a series of perceptual studies; participants prefer our outputs over the closest alternative by a factor of 9 to 1.
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Genre | |
Type | |
Language |
eng
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Date Available |
2022-04-07
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0412642
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2022-05
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Campus | |
Scholarly Level |
Graduate
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International