Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

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 ca...

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Bibliographic Details
Main Authors: Shaheen, Sara, Affara, Lama, Ghanem, Bernard
Format: Conference Proceeding
Language:English
Subjects:
Convolution
Convolutional codes
Dictionaries
Encoding
Image reconstruction
Optimization
Online Access:Request full text
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Summary: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.
ISSN:2380-7504
DOI:10.1109/ICCV.2017.474