Local Structure Analysis by Isotropic Hilbert Transforms
This work presents the isotropic and orthogonal decomposition of 2D signals into local geometrical and structural components. We will present the solution for 2D image signals in four steps: signal modeling in scale space, signal extension by higher order generalized Hilbert transforms, signal representation in classical matrix form, followed by the most important step, in which the matrix-valued signal will be mapped to a so called multivector. We will show that this novel multivector-valued signal representation is an interesting space for complete geometrical and structural signal analysis. In practical computer vision applications lines, edges, corners, and junctions as well as local texture patterns can be analyzed in one unified algebraic framework. Our novel approach will be applied to parameter-free multilayer decomposition.
KeywordsScale Space Geometric Algebra Convolution Kernel Signal Extension Inverse Radon
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