DESCRIPTION
This book is an in-depth guide that merges machine learning techniques with OpenCV, the most popular computer vision library, using Python. The book introduces fundamental concepts in machine learning and computer vision, progressing to practical implementation with OpenCV. Concepts related to image preprocessing, contour and thresholding techniques, motion detection and tracking are explained in a step-by-step manner using code and output snippets.
Hands-on projects with real-world datasets will offer you an invaluable experience in solving OpenCV challenges with machine learning. It's an ultimate guide to explore areas like deep learning, transfer learning, and model optimization, empowering readers to tackle complex tasks. Every chapter offers practical tips and tricks to build effective ML models.
TABLE OF CONTENTS
Chapter 1: Getting Started With OpenCV
Chapter 2: Basic Image & Video Analytics in OpenCV
Chapter 3: Image Processing 1 using OpenCV
Chapter 4: Image Processing 2 using OpenCV
Chapter 5: Thresholding and Contour Techniques Using OpenCV
Chapter 6: Detect Corners and Road Lane using OpenCV
Chapter 7: Object And Motion Detection Using Opencv
Chapter 8: Image Segmentation and Detecting Faces Using OpenCV
Chapter 9: Introduction to Deep Learning with OpenCV
Chapter 10: Advance Deep Learning Projects with OpenCV
Chapter 11: Deployment of OpenCV projects
By the end, you would have mastered and applied ML concepts confidently to real-world computer vision problems and will be able to develop robust and accurate machine-learning models for diverse applications.
Whether you are new to machine learning or seeking to enhance your computer vision skills, This book is an invaluable resource for mastering the integration of machine learning and computer vision using OpenCV and Python.
