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

5 Scopus citations


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)
Number of pages9
ISBN (Print)9781538610329
StatePublished - Dec 25 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST).


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