Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings

• Main Authors: Shaheen, Sara, Affara, Lama, Ghanem, Bernard
• Format: Conference Proceeding
• Language: English
• Subjects: Convolution; Convolutional codes; Dictionaries; Encoding; Image reconstruction; Optimization
• ISSN: 2380-7504
• DOI: 10.1109/ICCV.2017.474

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.