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Perception-driven semi-structured boundary vectorization

University of British Columbia

Artist-drawn images with distinctly colored, piecewise continuous boundaries, which are referred to as semi-structured imagery in this thesis, are very common in online raster databases and typically allow for a perceptually unambiguous mental vector interpretation. Yet, perhaps surprisingly, existing vectorization algorithms frequently fail to generate these viewer-expected interpretations on such imagery. In particular, the vectorized region boundaries they produce frequently diverge from those anticipated by viewers. This thesis proposes a new approach to region boundary vectorization that targets semi-structured inputs, and leverages observations about human perception of shapes to generate vector images consistent with viewer expectations. When viewing raster imagery, observers expect the vector output to be an accurate representation of the raster input. However, perception studies suggest that viewers implicitly account for the lossy nature of the rasterization process and mentally smooth and simplify the observed boundaries. The core algorithmic challenge is to balance these conflicting cues and obtain a piecewise continuous vectorization whose discontinuities, or corners, are aligned with human expectations. This work centers around a simultaneous spline fitting and corner detection method that combines a learned metric, that approximates human perception of boundary discontinuities on raster inputs, with perception-driven algorithmic discontinuity analysis. The resulting method balances local cues provided by the learned metric with global cues obtained by balancing simplicity and continuity expectations. Given the finalized set of corners, the proposed framework connects those using simple, continuous curves that capture input regularities. The proposed method is demonstrated on a range of inputs, with its superiority validated over existing alternatives via an extensive comparative user study.

Text

2019-02-11

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/68337

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/