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Towards New Foundations Of Computer Vision - A Groundbreaking Book

Jese Leos
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Published in Deep Learning To See: Towards New Foundations Of Computer Vision (SpringerBriefs In Computer Science)
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Towards New Foundations Of Computer Vision A Groundbreaking Book Deep Learning To See: Towards New Foundations Of Computer Vision (SpringerBriefs In Computer Science)

Computer vision is a rapidly evolving field that holds immense potential for revolutionizing a wide range of industries, from healthcare and autonomous vehicles to entertainment and security. Advancements in this field have paved the way for numerous applications, but to fully unlock its potential, new foundations need to be laid.

Introducing "Towards New Foundations Of Computer Vision," a groundbreaking book authored by leading experts in the field. This book, part of the prestigious SpringerBriefs in Computer Science series, offers a comprehensive overview of the current state of computer vision and provides insights into the future directions and advancements that will shape this field.

What Sets This Book Apart

Unlike other computer vision books on the market, this publication focuses on pioneering research that challenges existing paradigms and pushes the boundaries of what is possible. The authors, renowned computer vision researchers, present innovative theories, algorithms, and techniques that lay the groundwork for future breakthroughs.

Deep Learning to See: Towards New Foundations of Computer Vision (SpringerBriefs in Computer Science)
Deep Learning to See: Towards New Foundations of Computer Vision (SpringerBriefs in Computer Science)
by Alessandro Betti(Kindle Edition)

4.5 out of 5

Language : English
File size : 10736 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
Word Wise : Enabled
Print length : 202 pages
Screen Reader : Supported

The book takes a multidisciplinary approach, drawing from diverse fields such as artificial intelligence, machine learning, and neuroscience. This interdisciplinary perspective enables readers to grasp the broader implications of computer vision and its potential impact on other disciplines.

The Content

The book is divided into four parts, each covering a different aspect of computer vision:

  1. Foundations and Principles
  2. Machine Learning and Deep Learning
  3. Visual Recognition
  4. 3D Computer Vision

The authors provide a comprehensive to each topic, beginning with the fundamental concepts and gradually delving into advanced theories and applications. The inclusion of numerous examples and case studies further enhances the reader's understanding.

Long Descriptive Alt Attribute for Images

At Springerbriefs in Computer Science, we understand the importance of web accessibility, and we have ensured that our images have accurate alt attributes. For instance, the book cover image above has the alt attribute "Towards New Foundations Of Computer Vision - A Groundbreaking Book." This descriptive alt text enables visually impaired individuals using screen readers to understand the image content.

Renowned Computer Vision Researcher Dr. John Doe Deep Learning To See: Towards New Foundations Of Computer Vision (SpringerBriefs In Computer Science)

Renowned computer vision researcher Dr. John Doe

"Towards New Foundations Of Computer Vision" offers a fresh perspective on computer vision and presents innovative research that challenges existing paradigms. This groundbreaking book is a must-read for researchers, academics, and professionals working in the field of computer vision.

Deep Learning to See: Towards New Foundations of Computer Vision (SpringerBriefs in Computer Science)
Deep Learning to See: Towards New Foundations of Computer Vision (SpringerBriefs in Computer Science)
by Alessandro Betti(Kindle Edition)

4.5 out of 5

Language : English
File size : 10736 KB
Text-to-Speech : Enabled
Enhanced typesetting : Enabled
Word Wise : Enabled
Print length : 202 pages
Screen Reader : Supported

The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature.

This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal.

Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.

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