Mathematics for Computer Vision (Book)

Abstract 

This book provides a comprehensive overview of the mathematical foundations of computer vision, including linear algebra, calculus, probability and statistics, differential equations, and geometry. Each chapter introduces key concepts and their applications in computer vision, providing examples and practical exercises for readers to apply these concepts in real-world scenarios.

The first chapter focuses on linear algebra, which is used extensively in computer vision for tasks such as image processing, image compression, and feature extraction. The second chapter discusses the use of calculus for image segmentation, object detection, and tracking, including the calculation of gradients and optimization algorithms.

The third chapter covers probability and statistics, which are essential for object recognition, classification, and machine learning in computer vision. The fourth chapter delves into differential equations, which are used for image restoration and denoising. The fifth and final chapter discusses the use of geometry in computer vision, including 3D reconstruction, image registration, and camera calibration.

Throughout the book, readers will gain a deep understanding of the mathematical concepts underlying computer vision and their practical applications in real-world scenarios. This book is ideal for students, researchers, and professionals seeking to expand their knowledge of the mathematical foundations of computer vision.

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