Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

Sara Shaheen, Lama Ahmed Affara, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Convolutional sparse coding (CSC) plays an essential role in many computer vision applications ranging from image compression to deep learning. In this work, we spot the light on a new application where CSC can effectively serve, namely line drawing analysis. The process of drawing a line drawing can be approximated as the sparse spatial localization of a number of typical basic strokes, which in turn can be cast as a non-standard CSC model that considers the line drawing formation process from parametric curves. These curves are learned to optimize the fit between the model and a specific set of line drawings. Parametric representation of sketches is vital in enabling automatic sketch analysis, synthesis and manipulation. A couple of sketch manipulation examples are demonstrated in this work. Consequently, our novel method is expected to provide a reliable and automatic method for parametric sketch description. Through experiments, we empirically validate the convergence of our method to a feasible solution.
Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Computer Vision (ICCV)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages4434-4442
Number of pages9
ISBN (Print)9781538610329
DOIs
StatePublished - Dec 25 2017

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