Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Electrical Engineering Program
Visual Computing Center (VCC)
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AbstractConvolutional 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.
CitationShaheen S, Affara L, Ghanem B (2017) Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings. 2017 IEEE International Conference on Computer Vision (ICCV). Available: http://dx.doi.org/10.1109/ICCV.2017.474.
SponsorsThis work was supported by competitive research funding from King Abdullah University of Science and Technology (KAUST).
Conference/Event name16th IEEE International Conference on Computer Vision, ICCV 2017