Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

Sara Shaheen, Lama Affara, Bernard Ghanem; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4424-4432

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.

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[bibtex]
@InProceedings{Shaheen_2017_ICCV,
author = {Shaheen, Sara and Affara, Lama and Ghanem, Bernard},
title = {Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}