Vladimir Antonovich Kovalevsky | |
---|---|
Владимир Антонович Ковалевский | |
Born | |
Other names | Wladimir Kovalevski |
Occupation(s) | physicist, mathematician, computer scientist |
Title | Prof. Dr. |
Awards | Recipient Gold medal Allunion Exhibition of National Economy, Moscow, 1964. State Prize, Central Committee of Communist Party of Ukraine, Kiev, 1978. |
Honours | Listed as a noteworthy mathematician by Marquis Who's Who. |
Website | http://www.kovalevsky.de |
Vladimir Antonovich Kovalevsky (born 1927) is a physicist. His research interests include digital geometry, digital topology, computer vision, image processing and pattern recognition.
Vladimir A. Kovalevsky received his diploma in physics from Kharkiv University (Ukraine) in 1950, [1] his first doctoral degree in technical sciences from the Central Institute of Metrology (Leningrad) in 1957 and his second doctoral degree in computer science from the Institute of Cybernetics of the Academy of Sciences of Ukraine (Kiev) in 1968. From 1961 to 1983 he served as Head of Department of Pattern Recognition at that Institute. In 1983 he moved to the GDR. He worked as teaching professor or as scientific collaborator on universities in Germany [2] (Zentralinstitut für Kybernetik at the ADW, Berlin University of Applied Sciences and Technology, University of Rostock, [3] [4] Technische Universität Dresden [5] ), USA (University of Pennsylvania, Drexel University), Mexico (National Autonomous University of Mexico), New Zealand (University of Auckland, Manukau Institute of Technology) and Korea (Chonbuk National University).
Over a period of nearly 40 years, Vladimir A. Kovalevsky made many fundamental and pioneering contributions to nearly every area of the above-mentioned fields. The research on digital image analysis (specifically on digital geometry and digital topology) is an important insertion to image processing and image analysis. He developed the statistically founded correlation method of recognizing optical patterns and the department “Pattern Recognition” at the Institute of Cybernetics, Kiew, has constructed in 1968 the optical character reading machine “Chars” implementing this method. The machine could read typed pages with high security. [6] [7]
He suggested 1989 using topological knowledge, especially those of abstract cell complexes, in image processing. This has improved the definitions and the processing of boundaries and edges in two- and three-dimensional digital images. Vladimir A. Kovalevsky invented new efficient algorithms for edge detection in color images which can detect edges between subsets of different colors but the same lightness. He suggested efficient algorithms for tracing and encoding boundaries and also new definitions and recognition algorithms for recognizing digital straight segments. [8] Kovalevsky developed as programmer many projects implementing these algorithms. [9] [10] The results of his research are described in his monographs.
Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decisions. "Understanding" in this context signifies the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
Discrete mathematics is the study of mathematical structures that can be considered "discrete" rather than "continuous". Objects studied in discrete mathematics include integers, graphs, and statements in logic. By contrast, discrete mathematics excludes topics in "continuous mathematics" such as real numbers, calculus or Euclidean geometry. Discrete objects can often be enumerated by integers; more formally, discrete mathematics has been characterized as the branch of mathematics dealing with countable sets. However, there is no exact definition of the term "discrete mathematics".
In mathematics, a fractal is a geometric shape containing detailed structure at arbitrarily small scales, usually having a fractal dimension strictly exceeding the topological dimension. Many fractals appear similar at various scales, as illustrated in successive magnifications of the Mandelbrot set. This exhibition of similar patterns at increasingly smaller scales is called self-similarity, also known as expanding symmetry or unfolding symmetry; if this replication is exactly the same at every scale, as in the Menger sponge, the shape is called affine self-similar. Fractal geometry lies within the mathematical branch of measure theory.
Theoretical computer science is a subfield of computer science and mathematics that focuses on the abstract and mathematical foundations of computation.
Edge detection includes a variety of mathematical methods that aim at identifying edges, defined as curves in a digital image at which the image brightness changes sharply or, more formally, has discontinuities. The same problem of finding discontinuities in one-dimensional signals is known as step detection and the problem of finding signal discontinuities over time is known as change detection. Edge detection is a fundamental tool in image processing, machine vision and computer vision, particularly in the areas of feature detection and feature extraction.
Digital geometry deals with discrete sets considered to be digitized models or images of objects of the 2D or 3D Euclidean space. Simply put, digitizing is replacing an object by a discrete set of its points. The images we see on the TV screen, the raster display of a computer, or in newspapers are in fact digital images.
Discrete geometry and combinatorial geometry are branches of geometry that study combinatorial properties and constructive methods of discrete geometric objects. Most questions in discrete geometry involve finite or discrete sets of basic geometric objects, such as points, lines, planes, circles, spheres, polygons, and so forth. The subject focuses on the combinatorial properties of these objects, such as how they intersect one another, or how they may be arranged to cover a larger object.
In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple image segments, also known as image regions or image objects. The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
Robert M. Haralick is Distinguished Professor in Computer Science at Graduate Center of the City University of New York (CUNY). Haralick is one of the leading figures in computer vision, pattern recognition, and image analysis. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and a Fellow and past president of the International Association for Pattern Recognition. Professor Haralick is the King-Sun Fu Prize winner of 2016, "for contributions in image analysis, including remote sensing, texture analysis, mathematical morphology, consistent labeling, and system performance evaluation".
In shape analysis, skeleton of a shape is a thin version of that shape that is equidistant to its boundaries. The skeleton usually emphasizes geometrical and topological properties of the shape, such as its connectivity, topology, length, direction, and width. Together with the distance of its points to the shape boundary, the skeleton can also serve as a representation of the shape.
Neural gas is an artificial neural network, inspired by the self-organizing map and introduced in 1991 by Thomas Martinetz and Klaus Schulten. The neural gas is a simple algorithm for finding optimal data representations based on feature vectors. The algorithm was coined "neural gas" because of the dynamics of the feature vectors during the adaptation process, which distribute themselves like a gas within the data space. It is applied where data compression or vector quantization is an issue, for example speech recognition, image processing or pattern recognition. As a robustly converging alternative to the k-means clustering it is also used for cluster analysis.
Geometric modeling is a branch of applied mathematics and computational geometry that studies methods and algorithms for the mathematical description of shapes. The shapes studied in geometric modeling are mostly two- or three-dimensional, although many of its tools and principles can be applied to sets of any finite dimension. Today most geometric modeling is done with computers and for computer-based applications. Two-dimensional models are important in computer typography and technical drawing. Three-dimensional models are central to computer-aided design and manufacturing (CAD/CAM), and widely used in many applied technical fields such as civil and mechanical engineering, architecture, geology and medical image processing.
Digital topology deals with properties and features of two-dimensional (2D) or three-dimensional (3D) digital images that correspond to topological properties or topological features of objects.
Godfried Theodore Patrick Toussaint was a Canadian computer scientist, a professor of computer science, and the head of the Computer Science Program at New York University Abu Dhabi (NYUAD) in Abu Dhabi, United Arab Emirates. He is considered to be the father of computational geometry in Canada. He did research on various aspects of computational geometry, discrete geometry, and their applications: pattern recognition, motion planning, visualization, knot theory, linkage (mechanical) reconfiguration, the art gallery problem, polygon triangulation, the largest empty circle problem, unimodality, and others. Other interests included meander (art), compass and straightedge constructions, instance-based learning, music information retrieval, and computational music theory.
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated. Objects can even be recognized when they are partially obstructed from view. This task is still a challenge for computer vision systems. Many approaches to the task have been implemented over multiple decades.
James W. Cannon is an American mathematician working in the areas of low-dimensional topology and geometric group theory. He was an Orson Pratt Professor of Mathematics at Brigham Young University.
In mathematics, an abstract cell complex is an abstract set with Alexandrov topology in which a non-negative integer number called dimension is assigned to each point. The complex is called “abstract” since its points, which are called “cells”, are not subsets of a Hausdorff space as is the case in Euclidean and CW complexes. Abstract cell complexes play an important role in image analysis and computer graphics.
CVIP has the ability to read various image formats, including TIFF, PNG, GIF, JPEG, BMP, and RAW formats. It supports standard image processing functions, image compression, restoration, logical and arithmetical operations between images, contrast manipulation, image sharpening, frequency transformation, edge detection, segmentation, and geometric transformations.
Nicolai Petkov is Dutch computer scientist and professor emeritus of Intelligent Systems and Computer Science at the University of Groningen, known for his contributions in the fields of brain-inspired computing, pattern recognition, machine learning, and parallel computing.
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.
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