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Digital Geometry Algorithms: Theoretical Foundations and Applications to Computational Imaging (Lecture Notes in Computational Vision and Biomechanics)

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Digital geometry emerged as an independent discipline in the second half of the last century. It deals with geometric properties of digital objects and is developed with the unambiguous goal to provide rigorous theoretical foundations for devising new advanced approaches and algorithms for various problems of visual computing. Different aspects of digital geometry have been addressed in the literature. This book is the first one that explicitly focuses on the presentation of the most important digital geometry algorithms. Each chapter provides a brief survey on a major research area related to the general volume theme, description and analysis of related fundamental algorithms, as well as new original contributions by the authors. Every chapter contains a section in which interesting open problems are addressed.
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Innovations for Shape Analysis: Models and Algorithms (Mathematics and Visualization)
Innovations for Shape Analysis: Models and Algorithms (Mathematics and Visualization)

The concept of 'shape' is at the heart of image processing and computer vision, yet researchers still have some way to go to replicate the human brain's ability to extrapolate meaning from the most basic of outlines. This volume reflects the advances of the last decade, which have also opened up tough new challenges in...

Separable Type Representations of Matrices and Fast Algorithms: Volume 1 Basics. Completion Problems. Multiplication and Inversion Algorithms (Operator Theory: Advances and Applications)
Separable Type Representations of Matrices and Fast Algorithms: Volume 1 Basics. Completion Problems. Multiplication and Inversion Algorithms (Operator Theory: Advances and Applications)
This two-volume work presents a systematic theoretical and computational study of several types of generalizations of separable matrices. The main attention is paid to fast algorithms (many of linear complexity) for matrices in semiseparable, quasiseparable, band and companion form. The work is focused on algorithms of multiplication, inversion...
Image Processing: Tensor Transform and Discrete Tomography with MATLAB ®
Image Processing: Tensor Transform and Discrete Tomography with MATLAB ®

Focusing on mathematical methods in computer tomography, Image Processing: Tensor Transform and Discrete Tomography with MATLAB® introduces novel approaches to help in solving the problem of image reconstruction on the Cartesian lattice. Specifically, it discusses methods of image processing along...


Exploitation of Linkage Learning in Evolutionary Algorithms (Adaptation, Learning, and Optimization)
Exploitation of Linkage Learning in Evolutionary Algorithms (Adaptation, Learning, and Optimization)

One major branch of enhancing the performance of evolutionary algorithms is the exploitation of linkage learning. This monograph aims to capture the recent progress of linkage learning, by compiling a series of focused technical chapters to keep abreast of the developments and trends in the area of linkage. In evolutionary algorithms, linkage...

Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or...

Internet of Things with Arduino Cookbook
Internet of Things with Arduino Cookbook

Key Features

  • This book offers key solutions and advice to address the hiccups faced when working on Arduino-based IoT projects in the real world
  • Take your existing skills and capabilities to the next level by building challenging IoT applications with ease.
  • Be the tech disruptor you always...
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