Geometric Shape Features Extraction Using a Steady State Partial Differential Equation System
This work addresses a domain-specific problem in image processing for shape analysis, offering incremental improvements by avoiding derivatives in orientation definition and topological constraints in thickness computation.
The paper tackles the problem of extracting geometric shape features like thickness, orientation, and skeleton from binary images by proposing a unified method based on a steady state partial differential equation system, with validation through analytical and numerical examples.
A unified method for extracting geometric shape features from binary image data using a steady state partial differential equation (PDE) system as a boundary value problem is presented in this paper. The PDE and functions are formulated to extract the thickness, orientation, and skeleton simultaneously. The main advantages of the proposed method is that the orientation is defined without derivatives and thickness computation is not imposed a topological constraint on the target shape. A one-dimensional analytical solution is provided to validate the proposed method. In addition, two-dimensional numerical examples are presented to confirm the usefulness of the proposed method.