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Machine Learning for OpenCV 4: Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn , Second Edition

Michael Beyeler (USD)

Sharma

Vishwesh Ravi Shrimali

Michael Beyeler

₹800 per month

Paperback Sep 2019 420 pages 2nd Edition

Michael Beyeler (USD)

Sharma

Vishwesh Ravi Shrimali

Michael Beyeler

₹800 per month

Paperback Sep 2019 420 pages 2nd Edition

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Key benefits

Gain insights into machine learning algorithms, and implement them using OpenCV 4 and scikit-learn
Get up to speed with Intel OpenVINO and its integration with OpenCV 4
Implement high-performance machine learning models with helpful tips and best practices

Description

OpenCV is an opensource library for building computer vision apps. The latest release, OpenCV 4, offers a plethora of features and platform improvements that are covered comprehensively in this up-to-date second edition. You'll start by understanding the new features and setting up OpenCV 4 to build your computer vision applications. You will explore the fundamentals of machine learning and even learn to design different algorithms that can be used for image processing. Gradually, the book will take you through supervised and unsupervised machine learning. You will gain hands-on experience using scikit-learn in Python for a variety of machine learning applications. Later chapters will focus on different machine learning algorithms, such as a decision tree, support vector machines (SVM), and Bayesian learning, and how they can be used for object detection computer vision operations. You will then delve into deep learning and ensemble learning, and discover their real-world applications, such as handwritten digit classification and gesture recognition. Finally, you’ll get to grips with the latest Intel OpenVINO for building an image processing system. By the end of this book, you will have developed the skills you need to use machine learning for building intelligent computer vision applications with OpenCV 4.

Who is this book for?

This book is for Computer Vision professionals, machine learning developers, or anyone who wants to learn machine learning algorithms and implement them using OpenCV 4. If you want to build real-world Computer Vision and image processing applications powered by machine learning, then this book is for you. Working knowledge of Python programming is required to get the most out of this book.

What you will learn

Understand the core machine learning concepts for image processing
Explore the theory behind machine learning and deep learning algorithm design
Discover effective techniques to train your deep learning models
Evaluate machine learning models to improve the performance of your models
Integrate algorithms such as support vector machines and Bayes classifier in your computer vision applications
Use OpenVINO with OpenCV 4 to speed up model inference

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Free for first 7 days. ₹800 p/m after that. Cancel any time!

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

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Product Details

Publication date : Sep 06, 2019

Length: 420 pages

Edition : 2nd

Language : English

ISBN-13 : 9781789536300

Category :

Data

Languages :

C++

Concepts :

Computer Vision

Tools :

OpenCV

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17 Chapters

Section 1: Fundamentals of Machine Learning and OpenCV

A Taste of Machine Learning

Technical requirements

Getting started with machine learning

Problems that machine learning can solve

Getting started with Python

Getting started with OpenCV

Installation

Applications of machine learning

What's new in OpenCV 4.0?

Summary

Working with Data in OpenCV

Technical requirements

Understanding the machine learning workflow

Dealing with data using OpenCV and Python

Summary

First Steps in Supervised Learning

Technical requirements

Understanding supervised learning

Using classification models to predict class labels

Using regression models to predict continuous outcomes

Classifying iris species using logistic regression

Summary

Representing Data and Engineering Features

Technical requirements

Understanding feature engineering

Preprocessing data

Understanding dimensionality reduction

Representing categorical variables

Representing text features

Representing images

Summary

Section 2: Operations with OpenCV

Using Decision Trees to Make a Medical Diagnosis

Technical requirements

Understanding decision trees

Using decision trees to diagnose breast cancer

Using decision trees for regression

Summary

Detecting Pedestrians with Support Vector Machines

Technical requirement

Understanding linear SVMs

Dealing with nonlinear decision boundaries

Detecting pedestrians in the wild

Summary

Implementing a Spam Filter with Bayesian Learning

Technical requirements

Understanding Bayesian inference

Implementing your first Bayesian classifier

Classifying emails using the Naive Bayes classifier

Summary

Discovering Hidden Structures with Unsupervised Learning

Technical requirements

Understanding unsupervised learning

Understanding k-means clustering

Understanding expectation-maximization

Compressing color spaces using k-means

Classifying handwritten digits using k-means

Organizing clusters as a hierarchical tree

Summary

Section 3: Advanced Machine Learning with OpenCV

Using Deep Learning to Classify Handwritten Digits

Technical requirements

Understanding the McCulloch-Pitts neuron

Understanding the perceptron

Implementing your first perceptron

Understanding multilayer perceptrons

Getting acquainted with deep learning

Classifying handwritten digits

Summary

Ensemble Methods for Classification

Technical requirements

Understanding ensemble methods

Combining decision trees into a random forest

Using random forests for face recognition

Implementing AdaBoost

Combining different models into a voting classifier

Summary

Selecting the Right Model with Hyperparameter Tuning

Technical requirements

Evaluating a model

Understanding cross-validation

Estimating robustness using bootstrapping

Assessing the significance of our results

Tuning hyperparameters with grid search

Scoring models using different evaluation metrics

Chaining algorithms together to form a pipeline

Summary

Using OpenVINO with OpenCV

Technical requirements

Introduction to OpenVINO

OpenVINO toolkit installation

Interactive face detection demo

Using OpenVINO Inference Engine with OpenCV

Using OpenVINO Model Zoo with OpenCV

Image classification using OpenCV with OpenVINO Inference Engine

Summary

Conclusion

Technical requirements

Approaching a machine learning problem

Building your own estimator

Where to go from here

Summary

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About the 4 authors

Michael Beyeler (USD)

Michael Beyeler is a postdoctoral fellow in neuroengineering and data science at the University of Washington, where he is working on computational models of bionic vision in order to improve the perceptual experience of blind patients implanted with a retinal prosthesis (bionic eye).His work lies at the intersection of neuroscience, computer engineering, computer vision, and machine learning. He is also an active contributor to several open source software projects, and has professional programming experience in Python, C/C++, CUDA, MATLAB, and Android. Michael received a PhD in computer science from the University of California, Irvine, and an MSc in biomedical engineering and a BSc in electrical engineering from ETH Zurich, Switzerland.

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Sharma

Tushar Sharma has a master's degree specializing in data science from the International Institute of Information Technology, Bangalore. He works as a data scientist with Intel. In his previous job he used to work as a research engineer for a financial consultancy firm. His work involves handling big data at scale generated by the massive infrastructure at Intel. He engineers and delivers end to end solutions on this data using the latest machine learning tools and frameworks. He is proficient in R, Python, Spark, and mathematical aspects of machine learning among other things. Tushar has a keen interest in everything related to technology. He likes to read a wide array of books ranging from history to philosophy and beyond. He is a running enthusiast and likes to play badminton and tennis.

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Vishwesh Ravi Shrimali

Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.

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Michael Beyeler

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Machine Learning for OpenCV 4: Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn , Second Edition

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