Computer Vision Algorithms And Applications Texts In Computer Science 2nd Ed 2022 Edition
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Computer Vision
Author: Richard Szeliski
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-09-30
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
Computer Vision
Computer Vision: Algorithms and Applications explores the variety of techniques used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both in specialized applications such as image search and autonomous navigation, as well as for fun, consumer-level tasks that students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference takes a scientific approach to the formulation of computer vision problems. These problems are then analyzed using the latest classical and deep learning models and solved using rigorous engineering principles. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Incorporates totally new material on deep learning and applications such as mobile computational photography, autonomous navigation, and augmented reality Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Includes 1,500 new citations and 200 new figures that cover the tremendous developments from the last decade Provides additional material and more detailed mathematical topics in the Appendices, which cover 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 basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
Artificial Intelligence for Cognitive Systems: Deep Learning, Neuro- symbolic Integration, and Human-Centric Intelligence
Author: Samit Shivadekar
language: en
Publisher: Deep Science Publishing
Release Date: 2025-06-30
Artificial intelligence quickly changed from a theory to a practical power - it spreads through every part of modern life. As people go from specific uses to more general kinds of intelligence, they must face a main change. This change involves what machines do and how people think about intelligence. The book, Cognitive AI - From Deep Learning to Artificial General Intelligence, looks at that change. This writing serves a wide, serious group of people - it is for graduate students and researchers in artificial intelligence and cognitive science. Educators along with industry workers also read this to get a better grasp of the path from current AI systems to future cognitive architectures. We do not just list technologies. We deal with the concepts, morals, technical issues as well as societal problems that sit at the core of creating machines that think. The chapters lay out this story bit by bit; they start with basic learning systems. They move to cognitive modeling and designs. The book finishes with important questions about governance, combining fields along with how people will work in the future. Throughout the text, the reader learns about current subjects. Some of these are large language models, explaining how systems work, reasoning with symbols plus networks, the safety of general artificial intelligence, and people working with machines. I appreciate the researchers, collaborators along with students who inspired this work. The growing group of thinkers also recognizes that making intelligent systems requires scientific exactness and philosophical thought. My hope is that this book guides plus starts talks for anyone who wants AI to develop responsibly and creatively.