Constrained Convolutional Sparse Coding for Parametric Based Reconstruction Of Line Drawings

Constrained Convolutional Sparse Coding for Parametric Based Reconstruction Of Line Drawings

​​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)​
​​Sara Shaheen, Lama Affara, Bernard Ghanem,
convolutional sparse coding, sketch drawing, parametric reconstruction
2017
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.