File Name: applied graph theory in computer vision and pattern recognition .zip
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- Applied Graph Theory in Computer Vision and Pattern Recognition
- Recent advances and applications of machine learning in solid-state materials science
- [PDF] Applied Graph Theory in Computer Vision and Pattern Recognition (Studies in Computational
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.
Applied Graph Theory in Computer Vision and Pattern Recognition
Recent advances and applications of machine learning in solid-state materials science
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. Haxhimusa , A. Ion Published
It seems that you're in Germany. We have a dedicated site for Germany. 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.
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.
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[PDF] Applied Graph Theory in Computer Vision and Pattern Recognition (Studies in Computational
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.
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This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas.
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