Compact image vectorization by stochastic approaches

Jean-Dominique Favreau 1
1 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Artists appreciate vector graphics for their compactness and editability. However many artists express their creativity by sketching, painting or taking photographs. Digitizing these images produces raster graphics. The goal of this thesis is to convert raster graphics into vector graphics that are easy to edit. We cast image vectorization as an energy minimization problem. Our energy is a combination of two terms. The first term measures the fidelity of the vector graphics to the input raster graphics. This term is a standard term for image reconstruction problems. The main novelty is the second term which measures the simplicity of the vector graphics. The simplicity term is global and involves discrete unknowns which makes its minimization challenging. We propose two stochastic optimizations for this formulation: one for the line drawing vectorization problem and another one for the color image vectorization problem. These optimizations start by extracting geometric primitives (skeleton for sketches and segmentation for color images) and then assembling these primitives together to form the vector graphics. In the last chapter we propose a generic optimization method for the problem of geometric shape extraction. This new algorithm does not require any preprocessing step. We show its efficiency in a variety of vision problems including line network extraction, object contouring and image compression.
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Jean-Dominique Favreau. Compact image vectorization by stochastic approaches. Signal and Image processing. Université Côte d'Azur, 2018. English. ⟨NNT : 2018AZUR4004⟩. ⟨tel-01818515⟩

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