Machine Learning for OpenCV 4 : Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn, 2/e (Paperback)

Aditya Sharma , Vishwesh Ravi Shrimali , Michael Beyeler

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商品描述

Key Features

  • 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

Book 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.

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

Who this book is 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.

Table of Contents

商品描述(中文翻譯)

主要特點


  • 深入了解機器學習演算法,並使用OpenCV 4和scikit-learn實現它們

  • 熟悉Intel OpenVINO及其與OpenCV 4的整合

  • 實現高效能的機器學習模型,並提供實用技巧和最佳實踐

書籍描述

OpenCV是一個用於構建計算機視覺應用程式的開源庫。最新版本OpenCV 4提供了大量功能和平台改進,這些內容在這本最新的第二版中得到了全面介紹。

您將首先了解新功能並設置OpenCV 4以構建計算機視覺應用程式。您將探索機器學習的基礎知識,甚至學習設計可用於圖像處理的不同演算法。逐漸地,本書將帶您深入研究監督式和非監督式機器學習。您將通過使用Python中的scikit-learn進行各種機器學習應用程式的實踐經驗。後面的章節將重點介紹不同的機器學習演算法,例如決策樹、支持向量機(SVM)和貝葉斯學習,以及它們在物體檢測計算機視覺操作中的應用。然後,您將深入研究深度學習和集成學習,並發現它們在現實世界中的應用,例如手寫數字分類和手勢識別。最後,您將掌握最新的Intel OpenVINO,用於構建圖像處理系統。

通過閱讀本書,您將學習使用OpenCV 4應用機器學習來構建智能計算機視覺應用程式所需的技能。

您將學到什麼


  • 了解圖像處理的核心機器學習概念

  • 探索機器學習和深度學習演算法設計背後的理論

  • 發現訓練深度學習模型的有效技巧

  • 評估機器學習模型以提高模型性能

  • 在計算機視覺應用程式中集成支持向量機和貝葉斯分類器等演算法

  • 使用OpenCV 4的OpenVINO加速模型推論

本書適合對象

本書適合計算機視覺專業人士、機器學習開發人員或任何想要學習機器學習演算法並使用OpenCV 4實現它們的人。如果您想要構建由機器學習驅動的真實世界計算機視覺和圖像處理應用程式,那麼本書適合您。需要具備Python編程的工作知識,以充分利用本書。

目錄

目錄大綱

  1. A Taste of Machine Learning
  2. Working with Data in OpenCV
  3. First Steps in Supervised Learning
  4. Representing Data and Engineering Features
  5. Using Decision Trees to Make a Medical Diagnosis
  6. Detecting Pedestrians with Support Vector Machines
  7. Implementing a Spam Filter with Bayesian Learning
  8. Discovering Hidden Structures with Unsupervised Learning
  9. Using Deep Learning to Classify Handwritten Digits
  10. Ensemble Methods for Classification
  11. Selecting the Right Model with Hyperparameter Tuning
  12. Using OpenVINO with OpenCV
  13. Conclusion

目錄大綱(中文翻譯)

- 機器學習初探
- 在OpenCV中處理數據
- 監督式學習的第一步
- 數據表示和特徵工程
- 使用決策樹進行醫學診斷
- 使用支持向量機檢測行人
- 使用貝葉斯學習實現垃圾郵件過濾器
- 通過無監督學習發現隱藏結構
- 使用深度學習對手寫數字進行分類
- 集成方法進行分類
- 通過超參數調整選擇合適的模型
- 使用OpenVINO和OpenCV
- 結論