Segmentation and Classification: Theories, Algorithms and Applications

Segmentation and classification are fundamental tasks in image processing and computer vision. In addition to having ubiquitous applications in a variety of different fields, segmentation and classification also have their own theoretical foundations with the potential to nurture new methods with greater performance. However, the barriers between subject disciplines hinder communication between researchers with different research backgrounds. This lack of communication, to some extent, limits the power of the state-of-the-art segmentation and classification methods developed in different fields, and restricts the development of new and more powerful methods.

The goal of this Research Topic is to bring together the research challenges related to segmentation and classification in different fields in order to break down the subject barriers and deliver interdisciplinary solutions. Therefore, we welcome any contributions related to segmentation and classification, including theoretical analyses, methodologies, algorithms, and applications.

Topics of interest include, but are not limited to:

- Segmentation and classification methodologies based on mathematics, statistics, computer science, etc.;
- Uncertainty quantifications for segmentation and classification techniques;
- Deep analysis and theoretical interpretation of segmentation and classification methods;
- Semantic segmentation and classification;
- Segmentation and classification for 3D point clouds;
- Segmentation and classification for high-dimensional data;
- Deep learning;
- Medical imaging;
- Remote sensing;
- Other applications of segmentation and classification