Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement
This work addresses image segmentation for computer vision applications, presenting an incremental improvement by integrating region-based features into a minimal paths framework.
The paper tackles interactive image segmentation by introducing a model that combines geodesic metrics with implicit region-based homogeneity features, resulting in improved performance over existing minimal paths-based methods.
Minimal paths are regarded as a powerful and efficient tool for boundary detection and image segmentation due to its global optimality and the well-established numerical solutions such as fast marching method. In this paper, we introduce a flexible interactive image segmentation model based on the Eikonal partial differential equation (PDE) framework in conjunction with region-based homogeneity enhancement. A key ingredient in the introduced model is the construction of local geodesic metrics, which are capable of integrating anisotropic and asymmetric edge features, implicit region-based homogeneity features and/or curvature regularization. The incorporation of the region-based homogeneity features into the metrics considered relies on an implicit representation of these features, which is one of the contributions of this work. Moreover, we also introduce a way to build simple closed contours as the concatenation of two disjoint open curves. Experimental results prove that the proposed model indeed outperforms state-of-the-art minimal paths-based image segmentation approaches.