Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Modern Computer Vision with PyTorch

You're reading from   Modern Computer Vision with PyTorch Explore deep learning concepts and implement over 50 real-world image applications

Arrow left icon
Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781839213472
Length 824 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Yeshwanth Reddy Yeshwanth Reddy
Author Profile Icon Yeshwanth Reddy
Yeshwanth Reddy
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Arrow right icon
View More author details
Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1 - Fundamentals of Deep Learning for Computer Vision
2. Artificial Neural Network Fundamentals FREE CHAPTER 3. PyTorch Fundamentals 4. Building a Deep Neural Network with PyTorch 5. Section 2 - Object Classification and Detection
6. Introducing Convolutional Neural Networks 7. Transfer Learning for Image Classification 8. Practical Aspects of Image Classification 9. Basics of Object Detection 10. Advanced Object Detection 11. Image Segmentation 12. Applications of Object Detection and Segmentation 13. Section 3 - Image Manipulation
14. Autoencoders and Image Manipulation 15. Image Generation Using GANs 16. Advanced GANs to Manipulate Images 17. Section 4 - Combining Computer Vision with Other Techniques
18. Training with Minimal Data Points 19. Combining Computer Vision and NLP Techniques 20. Combining Computer Vision and Reinforcement Learning 21. Moving a Model to Production 22. Using OpenCV Utilities for Image Analysis 23. Other Books You May Enjoy Appendix
Using OpenCV Utilities for Image Analysis

So far, in previous chapters, we have learned about leveraging various techniques to perform object classification, localization, and segmentation, as well as generating images. While all these techniques leverage deep learning to solve tasks, for relatively simple and well-defined tasks, we can leverage specific functionalities provided in the OpenCV package. For example, we don't need YOLO if the object that needs to be detected is always the same object with the same background. In cases where images come from a constrained environment, there is a high chance that one of the OpenCV utilities can help solve the problem to a large extent.

We are going to cover only a few use cases in this chapter as there are just so many utilities to cover that it would warrant a separate book focusing on OpenCV. In doing word detection, you will...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime