Learning Path Building Computer Vision Projects with OpenCV 4 and C++ (Paperback)
暫譯: 學習路徑：使用 OpenCV 4 和 C++ 建立電腦視覺專案 (平裝本)

David Millan Escriva , Prateek Joshi , Vinicius G. Mendonca , Roy Shilkrot

Learning Path Building Computer Vision Projects with OpenCV 4 and C++ (Paperback)

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商品描述

Key Features

  • Discover best practices for engineering and maintaining OpenCV projects
  • Explore important deep learning tools for image classification
  • Understand basic image matrix formats and filters

Book Description

OpenCV is one of the best open source libraries available and can help you focus on constructing complete projects on image processing, motion detection, and image segmentation.

This Learning Path is your guide to understanding OpenCV concepts and algorithms through real-world examples and activities. Through various projects, you'll also discover how to use complex computer vision and machine learning algorithms and face detection to extract the maximum amount of information from images and videos. In later chapters, you'll learn to enhance your videos and images with optical flow analysis and background subtraction. Sections in the Learning Path will help you get to grips with text segmentation and recognition, in addition to guiding you through the basics of the new and improved deep learning modules. By the end of this Learning Path, you will have mastered commonly used computer vision techniques to build OpenCV projects from scratch. This Learning Path includes content from the following Packt books:

  • Mastering OpenCV 4 - Third Edition by Roy Shilkrot and David Millan Escriva
  • Learn OpenCV 4 By Building Projects - Second Edition by David Millan Escriva, Vinicius G. Mendonca, and Prateek Joshi

What you will learn

  • Stay up-to-date with algorithmic design approaches for complex computer vision tasks
  • Work with OpenCV's most up-to-date API through various projects
  • Understand 3D scene reconstruction and Structure from Motion (SfM)
  • Study camera calibration and overlay augmented reality (AR) using the ArUco module
  • Create CMake scripts to compile your C++ application
  • Explore segmentation and feature extraction techniques
  • Remove backgrounds from static scenes to identify moving objects for surveillance
  • Work with new OpenCV functions to detect and recognize text with Tesseract

Who this book is for

If you are a software developer with a basic understanding of computer vision and image processing and want to develop interesting computer vision applications with OpenCV, this Learning Path is for you. Prior knowledge of C++ and familiarity with mathematical concepts will help you better understand the concepts in this Learning Path.

商品描述(中文翻譯)

#### 主要特點

- 探索工程和維護 OpenCV 專案的最佳實踐
- 探索影像分類的重要深度學習工具
- 理解基本的影像矩陣格式和濾波器

#### 書籍描述

OpenCV 是最好的開源庫之一，可以幫助您專注於構建完整的影像處理、運動檢測和影像分割專案。

這條學習路徑是您通過實際範例和活動來理解 OpenCV 概念和演算法的指南。通過各種專案，您還將發現如何使用複雜的計算機視覺和機器學習演算法以及人臉檢測，從影像和視頻中提取最大量的信息。在後面的章節中，您將學習如何通過光流分析和背景減除來增強您的視頻和影像。學習路徑中的部分將幫助您掌握文本分割和識別，並指導您了解新改進的深度學習模組的基礎知識。在這條學習路徑結束時，您將掌握常用的計算機視覺技術，從零開始構建 OpenCV 專案。這條學習路徑包含以下 Packt 書籍的內容：

- 《Mastering OpenCV 4 - 第三版》由 Roy Shilkrot 和 David Millan Escriva 著
- 《Learn OpenCV 4 By Building Projects - 第二版》由 David Millan Escriva、Vinicius G. Mendonca 和 Prateek Joshi 著

#### 您將學到什麼

- 了解複雜計算機視覺任務的演算法設計方法
- 通過各種專案使用 OpenCV 最新的 API
- 理解 3D 場景重建和運動結構 (SfM)
- 學習相機校準和使用 ArUco 模組疊加擴增實境 (AR)
- 創建 CMake 腳本以編譯您的 C++ 應用程式
- 探索分割和特徵提取技術
- 從靜態場景中去除背景，以識別監控中的移動物體
- 使用 Tesseract 的新 OpenCV 函數來檢測和識別文本

#### 本書適合誰

如果您是一位對計算機視覺和影像處理有基本了解的軟體開發人員，並希望使用 OpenCV 開發有趣的計算機視覺應用程式，那麼這條學習路徑適合您。對 C++ 的先前知識和對數學概念的熟悉將幫助您更好地理解這條學習路徑中的概念。

目錄大綱

Table of Contents

  1. Getting Started with OpenCV
  2. An Introduction to the Basics of OpenCV
  3. Learning Graphical User Interfaces
  4. Delving into Histogram and Filters
  5. Automated Optical Inspection, Object Segmentation, and Detection
  6. Learning Object Classification
  7. Detecting Face Parts and Overlaying Masks
  8. Video Surveillance, Background Modeling, and Morphological Operations
  9. Learning Object Tracking
  10. Developing Segmentation Algorithms for Text Recognition
  11. Text Recognition with Tesseract
  12. Deep Learning with OpenCV
  13. Cartoonifier and Skin Color Analysis on the RaspberryPi
  14. Explore Structure from Motion with the SfM Module
  15. Face Landmark and Pose with the Face Module
  16. Number Plate Recognition with Deep Convolutional Networks
  17. Face Detection and Recognition with the DNN Module
  18. Android Camera Calibration and AR Using the ArUco Module
  19. iOS Panoramas with the Stitching Module
  20. Finding the Best OpenCV Algorithm for the Job
  21. Avoiding Common Pitfalls in OpenCV

目錄大綱(中文翻譯)

Table of Contents


  1. Getting Started with OpenCV

  2. An Introduction to the Basics of OpenCV

  3. Learning Graphical User Interfaces

  4. Delving into Histogram and Filters

  5. Automated Optical Inspection, Object Segmentation, and Detection

  6. Learning Object Classification

  7. Detecting Face Parts and Overlaying Masks

  8. Video Surveillance, Background Modeling, and Morphological Operations

  9. Learning Object Tracking

  10. Developing Segmentation Algorithms for Text Recognition

  11. Text Recognition with Tesseract

  12. Deep Learning with OpenCV

  13. Cartoonifier and Skin Color Analysis on the RaspberryPi

  14. Explore Structure from Motion with the SfM Module

  15. Face Landmark and Pose with the Face Module

  16. Number Plate Recognition with Deep Convolutional Networks

  17. Face Detection and Recognition with the DNN Module

  18. Android Camera Calibration and AR Using the ArUco Module

  19. iOS Panoramas with the Stitching Module

  20. Finding the Best OpenCV Algorithm for the Job

  21. Avoiding Common Pitfalls in OpenCV

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