StrokeStrip : joint parameterization and fitting of stroke clusters
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Pagurek van Mossel, Dave
issued: 2021-04-15T17:30:22Z
issued: 2021
related degree: Master of Science - MSc
degree grantor: University of British Columbia
description: When creating freeform drawings, artists routinely employ clusters of overdrawn strokes to convey intended, aggregate curves. The ability to algorithmically fit these intended curves to their corresponding clusters is central to many applications that use artist drawings as inputs. However, while human observers effortlessly envision the intended curves given stroke clusters as input, existing fitting algorithms lack robustness and frequently fail when presented with input stroke clusters with non-trivial geometry or topology. We present StrokeStrip, a new and robust method for fitting intended curves to vector-format stroke clusters. Our method generates fitting outputs consistent with viewer expectations across a vast range of input stroke cluster configurations. We observe that viewers perceive stroke clusters as continuous, varying-width strips whose paths are described by the intended curves. An arc length parameterization of these strips defines a natural mapping from a strip to its path. We recast the curve fitting problem as one of parameterizing the cluster strokes using a joint 1D parameterization that is the restriction of the natural arc length parameterization of this strip to the strokes in the cluster. We simultaneously compute the joint cluster parameterization and implicitly reconstruct the a priori unknown strip geometry by solving a variational problem using a discrete-continuous optimization framework. We use this parameterization to compute parametric aggregate curves whose shape reflects the geometric properties of the cluster strokes at the corresponding isovalues. We demonstrate StrokeStrip outputs to be significantly better aligned with observer preferences compared to those of prior art; in a perceptual study, viewers preferred our fitting outputs by a factor of 12:1 compared to alternatives. We further validate our algorithmic choices via a range of ablation studies; extend our framework to raster data; and illustrate applications that ben…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/77804
note: StrokeStrip: Joint Parameterization and Fitting of Stroke ClustersbyDave Pagurek van MosselB. Software Engineering, University of Waterloo, 2019A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFMaster of ScienceinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Computer Science)The University of British Columbia(Vancouver)April 2021c© Dave Pagurek van Mossel, 2021The following individuals certify that they have read, and recommend to the Faculty of Graduate and Post-doctoral Studies for acceptance, the thesis entitled:StrokeStrip: Joint Parameterization and Fitting of Stroke Clusterssubmitted by Dave Pagurek van Mossel in partial fulfillment of the requirements for the degree of Masterof Science in Computer Science.Examining Committee:Alla Sheffer, Professor, Computer Science, UBCSupervisorLeonid Sigal, Associate Professor, Computer Science, UBCSupervisory Committee MemberiiAbstractWhen creating freeform drawings, artists routinely employ clusters of overdrawn strokes to convey intended,aggregate curves. The ability to algorithmically fit these intended curves to their corresponding clusters iscentral to many applications that use artist drawings as inputs. However, while human observers effortlesslyenvision the intended curves given stroke clusters as input, existing fitting algorithms lack robustness andfrequently fail when presented with input stroke clusters with non-trivial geometry or topology. We presentStrokeStrip, a new and robust method for fitting intended curves to vector-format stroke clusters. Our methodgenerates fitting outputs consistent with viewer expectations across a vast range of input stroke clusterconfigurations. We observe that viewers perceive stroke clusters as continuous, varying-width strips whosepaths are described by the intended curves. An arc length parameterization of these strips defines a naturalmapping from a strip to its path. We recast the curve fitting problem as one of parameterizing the clusterstr…
has type: Thesis/Dissertation
date issued: 2021-05
is shown at: 10.14288/1.0396730
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution 4.0 International
rights URI: http://creativecommons.org/licenses/by/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/77804