This book is incredibly informative and beneficial for data scientists and software developers specializing in image processing and computer vision. This book will assist you in learning Python-based image-processing algorithms and their uses. You will learn about image processing using the OpenCV library in this book. Use TensorFlow, Scikit-Learn, NumPy, as well as other libraries are covered in this book. The use of machine learning and deep learning algorithms for image processing, and then how to apply these techniques to five real-world applications, will all be covered in this course. Real-world examples are used to clarify every concept in Practical Machine Learning and Image Processing. You will be able to use image processing techniques and create machine learning models for specific applications after finishing this book.

Machine Learning:

One of the most popular AI strategies for many businesses, organizations, and people involved in automation is machine learning (ML). ML algorithms can now analyze images in the same way as our brains do when dealing with visual data. Nearly everywhere uses them, including self-driving cars, automating arduous manual labor, face recognition when taking pictures on smartphones, and everything in between.

Processing images using machine learning:

Machine learning algorithms frequently follow a set pipeline or set of steps when learning from data. Let’s model a useful algorithm for an Image Processing use case using a generic example of the same.
First of all, in order to learn and predict outcomes with great accuracy, ML algorithms require a sizeable volume of high-quality data. As a result, we must ensure that the photos are correctly edited, labelled, and suitable for ML image processing. This is where Computer Vision (CV), a field concerned with machines being able to understand the image data, comes into play. We may process, load, transform, and otherwise work with photos using CV to create a perfect dataset for the machine learning algorithm.
The processed data are then utilised in the following phase, which involves selecting and creating a machine-learning algorithm to categorise novel feature vectors given a large database of feature vectors with established classifications. We must select the best method for this; the most well-liked ones include Bayesian Nets, Decision Trees, Genetic Algorithms, Nearest Neighbors, and Neural Nets, among others.

Topics covered by book:

  • Chapter 1 is about set up Environment in which we will study about installation of Anaconda, window Ubuntu and test installation as well as the virtual environments.

  • Next chapter is about image processing in which we discuss the image file format as well as different image concepts.

  • In chapter 3 we will first study the basic concepts of python then we will learn about scikit image.

  • After that we will study about Advanced images processing using openCv in which we will discuss images blending and changes of shape etc.

  • Chapter 5 is about image processing using machine learning.

  • In the last Chapter we study real time use cases like recognizing and detecting faces and tracking movements etc.

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