Mastering New Age Computer Vision : Advanced techniques in computer vision object detection, segmentation, and deep learning (English Edition)
Language: English
Published by BPB Publications, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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nach der Bestellung gedruckt Neuware - Printed after ordering - DESCRIPTION Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications.This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks.By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field.WHAT YOU WILL LEARN¿ Use PyTorch for both basic and advanced image processing.¿ Build object detection models using CNNs and modern frameworks.¿ Apply multi-task and multi-instance learning to complex datasets.¿ Develop segmentation models, including panoptic segmentation.¿ Improve feature representation with metric learning and bilinear pooling.¿ Explore transformers and self-supervised learning for computer vision.WHO THIS BOOK IS FORThis book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed.…
Seller Inventory # 9789365898408
- Title
- Mastering New Age Computer Vision : Advanced techniques in computer vision object detection, segmentation, and deep learning (English Edition)
- Author
- Zonunfeli Ralte
- Publisher
- BPB Publications
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9365898404
- ISBN 13
- 9789365898408
- Item weight
- 795 grams
- Dimensions
- 235x191x23 mm
Description
Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications.
This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks.
By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field.
Key Features
● Master PyTorch for image processing, segmentation, and object detection.
● Explore advanced computer vision techniques like ViT and panoptic models.
● Apply multi-tasking, metric, bilinear pooling, and self-supervised learning in real-world scenarios.
What you will learn
● Use PyTorch for both basic and advanced image processing.
● Build object detection models using CNNs and modern frameworks.
● Apply multi-task and multi-instance learning to complex datasets.
● Develop segmentation models, including panoptic segmentation.
● Improve feature representation with metric learning and bilinear pooling.
● Explore transformers and self-supervised learning for computer vision.
Who this book is for
This book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed.
Table of Contents
1. Evolution of New Age Computer Vision Models
2. Image Processing with PyTorch
3. Designing of Advanced Computer Vision Techniques
4. Designing Superior Computer Vision Techniques
5. Advanced Object Detection with FPN, RPN, and DetectoRS
6. Multi-instance Learning
7. More Advanced Multi-instance Learning
8. Beyond Classical Segmentation Panoptic Segmentation with SAM
9. Crafting Deep Metric Learning in Embedding Space
10. Navigating the Realm of Metric Learning
11. Multi-tasking with Multi-task Learning
12. Fine-grained Bilinear CNN
13. The Rise of Self-supervised Learning
14. Advancements in Computer Vision Landscape
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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last update: 01/06/2022
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last updated: 01/06/2022