A United Image Force for Deformable Models and Direct Transforming Geometric Active Contorus to Snakes by Level Sets
This work addresses segmentation accuracy and speed issues for computer vision applications, but it appears incremental as it builds on existing models without a major breakthrough.
The study tackles the problem of slow convergence and inaccurate boundary detection in image segmentation by introducing a fusion scheme of heat diffusion and electrostatic field image forces, achieving high precision and fast speed. It also demonstrates that the Geometric Active Contours model can be directly deduced from the Snakes model, revealing correspondences between their terms.
A uniform distribution of the image force field around the object fasts the convergence speed of the segmentation process. However, to achieve this aim, it causes the force constructed from the heat diffusion model unable to indicate the object boundaries accurately. The image force based on electrostatic field model can perform an exact shape recovery. First, this study introduces a fusion scheme of these two image forces, which is capable of extracting the object boundary with high precision and fast speed. Until now, there is no satisfied analysis about the relationship between Snakes and Geometric Active Contours (GAC). The second contribution of this study addresses that the GAC model can be deduced directly from Snakes model. It proves that each term in GAC and Snakes is correspondent and has similar function. However, the two models are expressed using different mathematics. Further, since losing the ability of rotating the contour, adoption of level sets can limits the usage of GAC in some circumstances.