About mathematical image science
Mathematical image science is an interdisciplinary scientific field in applied mathematics that focuses on development of mathematical theories and algorithms for quantitative handling of image data.
Gathering and analyzing images is necessary in upholding several critical functions in todays society. Some examples are interpretation of barcodes in industrial logistic solutions as well as in the retail sector, automatic registration and interpretation of car registration numbers (congestion taxes in Stockholm and Gothenburg), image guided diagnostics, therapies and monitoring in health care (medical imaging science), and seciurty (survalliance). Common to all these applications is the capability to gather and analyze digital images in a computer.
Mathematics is important in de development of computationally feasible algorithms. This is a key issue for applicability since the handling of large scale image data needs to meet the time constraints imposed from the application. Mathematics does however have a broader role, especially when the goal is to model, extract and represent the information content in images. A typical problem is to quantitatively measure the shape similarity between two objects being imaged. This leads to the need to model shape similarity and variation. Another problem is to extract and represent various image features such as edges, surfaces, corners, and corners. How do we mathematically represent an edge? What methods are there to extract such features from incomplete noisy data? Mathematics is central in addressing these, and similar, issues, which also serve as input for research in pure and applied mathematics.