A View of Regularized Approaches for Image Segmentation.
Laura Antonelli, Valentina De Simone, Daniela di Serafino · arXiv (Cornell University) · 2021
Image segmentation is a central topic in image processing and computer vision and a key issue in many applications, e.g., in medical imaging, microscopy, document analysis and remote sensing. According to the human perception, image segmentation is the process of dividing an image into non-overlapping regions. These regions, which may correspond, e.g., to different objects, are fundamental for the correct interpretation and classification of the scene represented by the image. The division into regions is not unique, but it depends on the application, i.e., it must be driven by the final goal of the segmentation and hence by the most significant features with respect to that goal. Image segmentation can be regarded as an ill-posed problem. A classical approach to deal with ill posedness consists in the use of regularization, which allows us to incorporate in the model a-priori information about the solution. In this work we provide a brief overview of regularized mathematical models for image segmentation, considering edge-based and region-based variational models, as well as statistical and machine-learning approaches. We also sketch numerical methods that are applied in computing solutions of those models. In our opinion, a unifying view of regularized mathematical models and related computational tools for segmentation can help the readers identify the most appropriate methods for solving their specific segmentation problems. It can also provide a basis for designing new methods combining existing ones.