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*Computer vision, the science and technology of machines that see, has been a rapidly developing research area since the mids. It focuses on the understanding of digital input images in many forms, including video and 3-D range data.*

- Graph theory
- Recent advances and applications of machine learning in solid-state materials science
- APPLIED GRAPH THEORY IN COMPUTER VISION AND PATTERN RECOGNITION

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## Graph theory

Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems.

## Recent advances and applications of machine learning in solid-state materials science

My research is primarily focused around applied machine learning. I have worked in the domains of probabilistic robotics and computer vision for many years, have some recent experience in computational linguistics and reinforcement learning, and currently focus mainly on representation learning and low-resource computer vision. I like working on applications for nature conservation and social impact, and I am passionate about building and serving machine learning communities all across the African continent. After about a year of postdoc work at South Africa's Council for Scientific and Industrial Research , I returned to Stellenbosch in where I have been teaching a variety of undergrad and postgrad courses in Applied Mathematics and Computer Science, and working with many amazing grad students. Deep Learning Indaba The Deep Learning Indaba is an organisation whose mission is to strengthen machine learning and artificial intelligence in Africa.

Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. Example: The colours on the clothes, speech pattern etc. In computer science, a pattern is represented using vector features values. Pattern recognition is the process of recognizing patterns by using machine learning algorithm.

In mathematics , graph theory is the study of graphs , which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of vertices also called nodes or points which are connected by edges also called links or lines. A distinction is made between undirected graphs , where edges link two vertices symmetrically, and directed graphs , where edges link two vertices asymmetrically; see Graph discrete mathematics for more detailed definitions and for other variations in the types of graph that are commonly considered. Graphs are one of the prime objects of study in discrete mathematics. Refer to the glossary of graph theory for basic definitions in graph theory. Definitions in graph theory vary. The following are some of the more basic ways of defining graphs and related mathematical structures.

Applied Graph Theory in Computer Vision and Pattern Recognition watermarked, DRM-free; Included format: PDF; ebooks can be used on all reading devices.

## APPLIED GRAPH THEORY IN COMPUTER VISION AND PATTERN RECOGNITION

This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems.

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Kropatsch and Y. Haxhimusa and A. Kropatsch , Y.

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Graph Similarity, Matching, and Learning for High Level Computer Vision and Pattern Recognition. Front Matter. Pages PDF · How and Why Pattern.