Building Computer Vision Applications Using Artificial Neural Networks: With Examples in OpenCV and TensorFlow with Python. 2 Ed

Front Matter....2

1. Prerequisites and Software Installation....24

2. Core Concepts of Image and Video Processing....38

3. Techniques of Image Processing....59

4. Building a Machine Learning–Based Computer Vision System....144

5. Deep Learning and Artificial Neural Networks....189

6. Deep Learning in Object Detection....257

7. Practical Example: Object Tracking in Videos....322

8. Practical Example: Face Recognition....344

9. Industrial Application: Real-Time Defect Detection in Industrial Manufacturing....364

10. Computer Vision Modeling on the Cloud....381

Back Matter....417

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.