Vectorization, Decomposition, and PDE Identification in Imaging and Inverse Problems

Explore cutting-edge topics in imaging and inverse problems through this 58-minute virtual seminar presented by Dr. Sung Ha Kang from Georgia Tech. Delve into three key areas: image vectorization, image decomposition, and PDE identification. Learn about a mathematically founded silhouette vectorization algorithm that converts bitmap images to scalable vector files using cubic Bezier polygons and perfect circles. Discover a convex non-convex variational decomposition model for separating images into piecewise-constant, smooth homogeneous, and noisy/texture components, with applications in denoising and shadow removal. Examine methods for identifying differential equations from discrete time-dependent data, addressing challenges such as noise sensitivity and nonlinearity. Gain insights into numerical PDE techniques for stable identification of underlying PDEs. This SIAM-IS seminar offers a comprehensive overview of advanced imaging and inverse problem techniques, suitable for researchers and professionals in applied mathematics and computer vision.

Introduction
Binary Image
Control Points
Priorities
Pixelization
Accuracy
Default
Image Decomposition
Summary
Parameter
Questions
Boundary Conditions
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