Building Computer Vision Applications Using Artificial Neural Networks
With Examples in OpenCV and TensorFlow with Python
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Building Computer Vision Applications Using Artificial Neural Networks
With Examples in OpenCV and TensorFlow with Python
About this book
Computer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition's publication. All code used in the book has also been fully updated.
This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer vision applications, and you'll gain a thorough understanding of them. The book's source code has been updated from TensorFlow 1.x to 2.x, and includes step-by-step examples using both OpenCV and TensorFlow with Python.
Upon completing this book, you'll have the knowledge and skills to build your own computer vision applications using neural networks
What You Will Learn
- Understand image processing, manipulation techniques, and feature extractionmethods
- Work with convolutional neural networks (CNN), single-shot detector (SSD), and YOLO
- Utilize large scale model development and cloud infrastructure deployment
- Gain an overview of FaceNet neural network architecture and develop a facial recognition system
Who This Book Is For
Those who possess a solid understanding of Python programming and wish to gain an understanding of computer vision and machine learning. It will prove beneficial to data scientists, deep learning experts, and students.Tools to learn more effectively
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Information
Table of contents
- Cover
- Front Matter
- 1. Prerequisites and Software Installation
- 2. Core Concepts of Image and Video Processing
- 3. Techniques of Image Processing
- 4. Building a Machine LearningâBased Computer Vision System
- 5. Deep Learning and Artificial Neural Networks
- 6. Deep Learning in Object Detection
- 7. Practical Example: Object Tracking in Videos
- 8. Practical Example: Face Recognition
- 9. Industrial Application: Real-Time Defect Detection in Industrial Manufacturing
- 10. Computer Vision Modeling on the Cloud
- Back Matter
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