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Constrained Convolutional Sparse Coding for Parametric Based Reconstruction Of Line Drawings
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Constrained Convolutional Sparse Coding for Parametric Based Reconstruction Of Line Drawings
Constrained Convolutional Sparse Coding for Parametric Based Reconstruction Of Line Drawings
Bibliography:
Sara Shaheen, Lama Affara, and Bernard Ghanem,
"Constrained Convolutional Sparse Coding for Parametric Based Reconstruction Of Line Drawings"
,
International Conference on Computer Vision (ICCV 2017)
Publication Extra Information:
[supplementary material]
Authors:
Sara Shaheen, Lama Affara, Bernard Ghanem,
Keywords:
convolutional sparse coding, sketch drawing, parametric reconstruction
Year:
2017
Abstract:
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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