We develop a level set based region growing method for automatic partitioning of color images into segments. Previous attempts at image segmentation either suffer from requiring a priori information to initialize regions, being computationally complex, or fail to establish the color consistency and spatial connectivity at the same time. Here, we represent the segmentation problem as monotonic wave propagation in an absorbing medium with varying front speeds. We iteratively emit waves from the selected base points. At a base point, the local variance of the data reaches a minimum, which indicates the base point is a suitable representative of its local neighborhood. We determine local variance by applying a hierarchical gradient operator. The speed of the wave is determined by the color similarity of the point on the front to the current coverage of the wave, and by edge information. Thus, the wave advances in an anisotropic spatial-color space. The absorbing function acts as a stopping criterion of the wave front. We take advantage of fast marching methods to solve the Eikonal equation for finding the travel times of the waves. Besides, each region boundary is represented as a mixture of Gaussian models. This formulation enables segmentation of multi-modal color objects. Our method is superior to the linkage-based and snake-based region growing techniques since it prevents leakage and imposes compactness on the region without over-smoothing its boundary. Furthermore, we can deal with sharp corners and changes in topology. The automatic segmentation method is Eulerian, thus it is computationally efficient. Our experiments illustrate the robustness, accuracy, and effectiveness of the proposed method.

Date Published: 28 May 2004
PDF: 8 pages
Proc. SPIE 5298, Image Processing: Algorithms and Systems III, (28 May 2004); doi: 10.1117/12.527193

Published in SPIE Proceedings Vol. 5298:
Image Processing: Algorithms and Systems III
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)