Image Vectorization with Depth: convexified shape layers with depth ordering
Ho Law, Sung Ha Kang

TL;DR
This paper introduces a novel image vectorization method that incorporates depth ordering and curvature-based inpainting to produce scalable, convex shape layers, improving boundary representation and editing capabilities.
Contribution
The paper proposes a new vectorization approach that integrates depth ordering, convexification, and curvature-based inpainting, enabling more natural shape decomposition and editing.
Findings
Effective removal of pixelization effects.
Improved shape boundary accuracy with curvature-based inpainting.
Enhanced image editing through layered shape decomposition.
Abstract
Image vectorization is a process to convert a raster image into a scalable vector graphic format. Objective is to effectively remove the pixelization effect while representing boundaries of image by scaleable parameterized curves. We propose new image vectorization with depth which considers depth ordering among shapes and use curvature-based inpainting for convexifying shapes in vectorization process.From a given color quantized raster image, we first define each connected component of the same color as a shape layer, and construct depth ordering among them using a newly proposed depth ordering energy. Global depth ordering among all shapes is described by a directed graph, and we propose an energy to remove cycle within the graph. After constructing depth ordering of shapes, we convexify occluded regions by Euler's elastica curvature-based variational inpainting, and leverage on the…
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Taxonomy
TopicsMedical Image Segmentation Techniques · Advanced Vision and Imaging
MethodsInpainting
