Computational visualistics is an interdisciplinary field of study of how computers can be used to generate and analyse images.[1]
For a science of images within computer science, the abstract data type "image" (or perhaps several such types) stands in the center of interest together with the potential implementations.[2] There are three main groups of algorithms for that data type to be considered in computational visualistics:
Image processing is a field of study that primarily involves operations converting one or more input images, along with possible additional non-image parameters, into an output image. These operations facilitate various applications, such as enhancing image quality, including techniques like contrast enhancement; extracting specific features from an image, such as edge detection; and identifying and isolating patterns based on predefined criteria, exemplified by the blue screen technique. Additionally, the field includes the development of compression algorithms, which play a significant role in the efficient storage and transmission of image data.
Two disciplines share the operations of transforming images into non-pictorial data items. The pattern recognition field is not restricted to pictures. But it has performed important precursory work for computational visualistics since the early 1950s in those areas that essentially classify information in given images: the identification of simple geometric Gestalts (e.g., "circular region"), the classification of letters (recognition of handwriting), the "seeing" of spatial objects in the images or even the association of stylistic attributes of the representation. The images are to be associated with instances of a non-pictorial data type forming a description of some of their aspects. The neighboring field of computer vision is the part of AI (artificial intelligence) in which computer scientists try to teach – loosely speaking – computers the ability of visual perception. Therefore, a problem rather belongs to computer vision to the degree to which its goal is "semantic", i.e., the result approximates the human seeing of objects in a picture.
The investigation of possibilities gained by the operations that result in instances of the data type "image" but take as a starting point instances of non-pictorial data types is performed in particular in computer graphics and information visualization. The former deals with images in the closer sense, i.e., those pictures showing spatial configurations of objects (in the colloquial meaning of 'object') in a more or less naturalistic representation like, e.g., in virtual architecture. The starting point of the picture-generating algorithms in computer graphics is usually a data type that allows us to describe the geometry in three dimensions and the scene's lighting to be depicted together with the important optical properties of the surfaces considered. Scientists in information visualization are interested in presenting pictorially any other data type, in particular those that consist of non-visual components in a "space" of states: to do so, a convention of visual presentation must first be determined – e.g., a code of colors or certain icons. The well-known fractal images (e.g., of the Mandelbrot set) form a borderline case of information visualization since an abstract mathematical property has been visualized.
The subject of computational visualistics was introduced at the University of Magdeburg, Germany, in the fall of 1996. [3] It was initiated by Thomas Strothotte, Prof. for computer graphics in Magdeburg and largely supported by Jörg Schirra together with a whole team of interdisciplinary researchers from the social and technical sciences as well as from medicine.This five-year diploma programme has computer science courses as its core: students learn about digital methods and electronic tools for solving picture-related problems. The technological areas of endeavor are complemented by courses on pictures in the humanities. Inaddition to learning about the traditional (i.e. not computerized) contexts of using pictures, students intensively practice their communicative skills. As the third component of the program, an application subject such as biology and medicine gives students an early opportunity to apply their knowledge in that they learn the skills needed for co-operating with clients and experts in other fields where digital image data are essential, e.g. microscopy and radiologic image data in biology and medicine. Bachelor and Master's programmes were introduced in 2006.
The expression 'computational visualistics' is also used for a similar degree programme of the University at Koblenz.