Computer Vision: Algorithms and Applications

Computer Vision: Algorithms and Applications is a comprehensive textbook that covers the variety of techniques used to analyze and interpret images, with a focus on real-world applications. It takes a scientific approach to formulating computer vision problems and solving them using classical and deep learning models, with exercises and additional material for testing algorithms. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering.

Format: Paperback / softback
Length: 925 pages
Publication date: 06 January 2023
Publisher: Springer Nature Switzerland AG

Computer Vision: Algorithms and Applications delves into a diverse range of techniques employed to analyze and interpret images. It also showcases real-world applications where vision is successfully utilized, spanning specialized domains like image search and autonomous navigation, as well as consumer-level tasks that students can apply to their personal photos and videos.

This textbook goes beyond a mere collection of "recipes" and adopts a scientific approach to formulating computer vision problems. These problems are then analyzed using the latest classical and deep learning models, employing rigorous engineering principles to find solutions.

Topics and Features:

Structured to support active curricula and project-oriented courses, with tips in the Introduction for utilizing the book in various customized courses.

Incorporates entirely new material on deep learning and its applications, including mobile computational photography, autonomous navigation, and augmented reality.

Presents exercises at the end of each chapter, with a strong emphasis on testing algorithms and offering numerous suggestions for small mid-term projects.

Includes 1,500 new citations and 200 new figures that capture significant developments from the past decade.

Provides additional material and more detailed mathematical topics in the Appendices, covering linear algebra, numerical techniques, estimation theory, datasets, and software.

Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on fundamental techniques that operate in real-world scenarios while encouraging students to explore their creative boundaries. Its design and exposition make it highly accessible and conducive to learning.

Weight: 2570g
Dimension: 210 x 280 x 43 (mm)
ISBN-13: 9783030343743
Edition number: 2nd ed. 2022