Computational visualistics
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Computational visualistics is an interdisciplinary field focused on the use of computers to generate and analyze images.[1]
Areas covered
In the study of images within computer science, the abstract data type "image" (or potentially several such types) is a central focus, along with its various implementations.[2] Three main groups of algorithms are relevant to this data type in computational visualistics:
Algorithms from "image" to "image"
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Algorithms from "image" to "image" involve image processing, which focuses on operations that convert one or more input images, possibly with additional non-image parameters, into an output image. These operations support various applications, including enhancing image quality through techniques like contrast enhancement, extracting features such as edge detection, and identifying and isolating patterns based on predefined criteria, such as the blue screen technique. The field also encompasses the development of compression algorithms, crucial for the efficient storage and transmission of image data.
Algorithms from "image" to "not-image"
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Two disciplines focus on transforming images into non-pictorial data. The field of pattern recognition, although not limited to images, has made significant contributions to computational visualistics since the early 1950s. This work includes classifying information within images, such as identifying geometric shapes (e.g., circular regions), recognizing handwritten text, detecting spatial objects, and associating stylistic attributes. The goal is to map images to non-pictorial data types that describe various aspects of the images. In contrast, computer vision, a branch of artificial intelligence (AI), aims to enable computers to achieve visual perception akin to human vision. Problems in computer vision are considered semantic when their objectives closely align with human-like understanding of objects within images.
Algorithms from "not-image" to "image"
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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.
Computational visualistics degree programmes
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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. In addition 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.
References
- ^ "Computational Visualistics". unimagdeburg. Retrieved 2023-11-15.
- ^ "Schirra 2005". Archived from the original on 2007-05-23. Retrieved 2006-06-09.
- ^ "OVGU - Computational Visualistics - Dual". Retrieved 17 December 2021.
Further reading
- Jochen Schneider, Thomas Strothotte & Winfried Marotzki (2003). Computational Visualistics, Media Informatics, and Virtual Communities. Deutscher Universitätsverlag.
- Jörg R.J. Schirra (1999). "Computational Visualistics: Bridging the Two Cultures in a Multimedia Degree Programme". In: Forum Proceedings, ed.: Z. J. Pudlowski, p. 47–51,
- Jörg R. J. Schirra (2000). "A New Theme for Educating New Engineers: Computational visualistics". In: Global Journal of Engineering Education, Vol. 4, No. 1, 73–82. (June 2000)
- Jörg R. J. Schirra (2005). "Foundation of Computational Visualistics". Deutscher Universitätsverlag
- Jörg R. J. Schirra (2005). "Computational Visualistics: Dealing with Pictures in Computer Science". In: K. Sachs-Hombach (Ed.): Bildwissenschaft zwischen Reflexion und Anwendung. Köln: Herbert von Halem Verlag, 2005, 494–509.
- Jörg R. J. Schirra (2005) "Ein Disziplinen-Mandala für die Bildwissenschaft - Kleine Provokation zu einem Neuen Fach"" Archived 2007-05-23 at the Wayback Machine. In: Vol. I: Bildwissenschaft als interdisziplinäres Unternehmen. Eine Standortbestimmung. 2005, Köln: Herbert-von-Halem-Verlag
- Bernhard Preim, Dirk Bartz (2007). Visualization in Medicine. Morgan Kaufmann, 2007.
- Bernhard Preim, Charl Botha (2013). Visual Computing for Medicine. Morgan Kaufmann, 2013.