An Image Segmentation Model Based on a Variational Formulation
This work addresses image segmentation for computer vision applications, but it appears incremental as it builds on existing variational methods without major breakthroughs.
The authors tackled image segmentation by developing a model that integrates region statistics and edge information from a variational formulation, achieving improved flexibility for a wider class of images, as demonstrated through simulations with real images.
Starting from a variational formulation, we present a model for image segmentation that employs both region statistics and edge information. This combination allows for improved flexibility, making the proposed model suitable to process a wider class of images than purely region-based and edge-based models. We perform several simulations with real images that attest to the versatility of the model. We also show another set of experiments on images with certain pathologies that suggest opportunities for improvement.