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Boundary tracing, also known as contour tracing, of a binary digital region can be thought of as a segmentation technique that identifies the boundary pixels of the digital region. Boundary tracing is an important first step in the analysis of that region. Boundary is a topological notion. However, a digital image is no topological space. Therefore, it is impossible to define the notion of a boundary in a digital image mathematically exactly. Most publications about tracing the boundary of a subset S of a digital image I describe algorithms which find a set of pixels belonging to S and having in their direct neighborhood pixels belonging both to S and to its complement I - S. According to this definition the boundary of a subset S is different from the boundary of the complement I – S which is a topological paradox. To define the boundary correctly it is necessary to introduce a topological space corresponding to the given digital image. Such space can be a two-dimensional abstract cell complex. It contains cells of three dimensions: the two-dimensional cells corresponding to pixels of the digital image, the one-dimensional cells or “cracks” representing short lines lying between two adjacent pixels, and the zero-dimensional cells or “points” corresponding to the corners of pixels. The boundary of a subset S is then a sequence of cracks and points while the neighborhoods of these cracks and points intersect both the subset S and its complement I – S. The boundary defined in this way corresponds exactly to the topological definition and corresponds also to our intuitive imagination of a boundary because the boundary of S should contain neither elements of S nor those of its complement. It should contain only elements lying between S and the complement. This are exactly the cracks and points of the complex. This method of tracing boundaries is described in the book of Vladimir A. Kovalevsky and in the web site.
Underwater computer vision is a subfield of computer vision. In recent years, with the development of underwater vehicles, the need to be able to record and process huge amounts of information has become increasingly important. Applications range from inspection of underwater structures for the offshore industry to the identification and counting of fishes for biological research. However, no matter how big the impact of this technology can be to industry and research, it still is in a very early stage of development compared to traditional computer vision. One reason for this is that, the moment the camera goes into the water, a whole new set of challenges appear. On one hand, cameras have to be made waterproof, marine corrosion deteriorates materials quickly and access and modifications to experimental setups are costly, both in time and resources. On the other hand, the physical properties of the water make light behave differently, changing the appearance of a same object with variations of depth, organic material, currents, temperature etc.
Visual computing is a generic term for all computer science disciplines dealing with images and 3D models, such as computer graphics, image processing, visualization, computer vision, virtual and augmented reality and video processing. Visual computing also includes aspects of pattern recognition, human computer interaction, machine learning and digital libraries. The core challenges are the acquisition, processing, analysis and rendering of visual information. Application areas include industrial quality control, medical image processing and visualization, surveying, robotics, multimedia systems, virtual heritage, special effects in movies and television, and computer games.
In mathematics and computer science, graph edit distance (GED) is a measure of similarity between two graphs. The concept of graph edit distance was first formalized mathematically by Alberto Sanfeliu and King-Sun Fu in 1983. A major application of graph edit distance is in inexact graph matching, such as error-tolerant pattern recognition in machine learning.
References
- ↑ "About the author" . Retrieved 10 August 2022.
- ↑ "SRH Hochschule Berlin". www.researchgate.net.
- ↑ "Scispace - V. Kovalevsky". Typeset.
- ↑ "University of Rostock" (in German). Retrieved 15 September 2022.
- ↑ "TU Dresden, lectures 2007". TU Dresden.
- ↑ Schlesinger, Michail (2007). "Ключевые моменты становления киевской школы распознавания изображений". www.irtc.org.ua (in Russian). Retrieved 15 September 2022.
- ↑ Vladimir Kovalevsky (1980), Image Pattern Recognition, Springer Verlag, ISBN 0-387-90440-9
- ↑ Kovalevskiĭ, V. A. (2021). Image processing with cellular topology. Singapore. ISBN 978-981-16-5772-6. OCLC 1306277103.
{{cite book}}: CS1 maint: location missing publisher (link) - ↑ Miszalok, Volkmar. "Lecture: Computer Vision, Chapter 1: Digital Topology". www.miszalok.de. Retrieved 15 September 2022.
- ↑ Kovalevsky, Vladimir (2006). "Axiomatic Digital Topology". Journal of Mathematical Imaging and Vision. 26 (1–2): 41–58. arXiv: 1010.0649 . doi:10.1007/s10851-006-7453-6. S2CID 459012.
