A New Adaptive Algorithm for the Polygonization of Noisy Imagery

This report introduces a novel adaptive image segmentation algorithm which represents images by polygonal segments and which is particularly suitable for the segmentation of noisy imagery. At first we suggest an intuitive generative model and its associated cost function. The cost function can effectively be optimized by a hierarchical triangulation algorithm, which iteratively refines and reorganizes a triangular mesh and finally provides a compact description of the essential image structure. After analyzing fundamental properties of our cost function, we adapt an information-theoretic bound to assess the statistical significance of a given triangulation step. The bound effectively defines a stopping criterion to limit the number of triangles in the mesh, thereby avoiding undesirable overfitting phenomena. Besides, it facilitates the development of a multi-scale variant of the triangulation algorithm, which substantially decreases its computational demands. The algorithm has various important applications in contextual classification, remote sensing, and visual object recognition.