Learning Path Building Computer Vision Projects with OpenCV 4 and C++ (Paperback)
David Millan Escriva , Prateek Joshi , Vinicius G. Mendonca , Roy Shilkrot
- 出版商: Packt Publishing
- 出版日期: 2019-03-22
- 售價: $1,800
- 貴賓價: 9.5 折 $1,710
- 語言: 英文
- 頁數: 538
- 裝訂: Paperback
- ISBN: 1838644679
- ISBN-13: 9781838644673
-
相關分類:
C++ 程式語言、影像辨識 Image-recognition、Computer Vision
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相關主題
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項目的最佳實踐
- 探索用於圖像分類的重要深度學習工具
- 了解基本的圖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圖像處理、運動檢測和圖像分割項目。
這個學習路徑是您理解OpenCV概念和算法的指南,通過真實世界的例子和活動。通過各種項目,您還將發現如何使用複雜的計算機視覺和機器學習算法以及人臉檢測從圖像和視頻中提取最大量的信息。在後面的章節中,您將學習如何使用光流分析和背景減除來增強您的視頻和圖像。學習路徑中的部分內容將幫助您掌握文本分割和識別,並引導您了解新的和改進的深度學習模塊的基礎知識。通過這個學習路徑的結束,您將掌握常用的計算機視覺技術,從頭開始構建OpenCV項目。這個學習路徑包括以下Packt書籍的內容:
- 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
你將學到什麼
- 了解複雜計算機視覺任務的算法設計方法
- 通過各種項目使用OpenCV最新的API
- 理解3D場景重建和運動結構(SfM)
- 研究相機校準和使用ArUco模塊進行增強現實(AR)
- 創建CMake腳本來編譯您的C++應用程序
- 探索分割和特徵提取技術
- 從靜態場景中去除背景以識別移動物體進行監視
- 使用新的OpenCV函數檢測和識別Tesseract文本
適合閱讀對象
如果您是一名具有基本的計算機視覺和圖像處理理解的軟件開發人員,並且希望使用OpenCV開發有趣的計算機視覺應用程序,那麼這個學習路徑適合您。對C++的先備知識和對數學概念的熟悉將有助於您更好地理解這個學習路徑中的概念。
目錄大綱
Table of Contents
- Getting Started with OpenCV
- An Introduction to the Basics of OpenCV
- Learning Graphical User Interfaces
- Delving into Histogram and Filters
- Automated Optical Inspection, Object Segmentation, and Detection
- Learning Object Classification
- Detecting Face Parts and Overlaying Masks
- Video Surveillance, Background Modeling, and Morphological Operations
- Learning Object Tracking
- Developing Segmentation Algorithms for Text Recognition
- Text Recognition with Tesseract
- Deep Learning with OpenCV
- Cartoonifier and Skin Color Analysis on the RaspberryPi
- Explore Structure from Motion with the SfM Module
- Face Landmark and Pose with the Face Module
- Number Plate Recognition with Deep Convolutional Networks
- Face Detection and Recognition with the DNN Module
- Android Camera Calibration and AR Using the ArUco Module
- iOS Panoramas with the Stitching Module
- Finding the Best OpenCV Algorithm for the Job
- Avoiding Common Pitfalls in OpenCV
目錄大綱(中文翻譯)
目錄
- 開始使用OpenCV
- OpenCV基礎介紹
- 學習圖形使用者介面
- 深入瞭解直方圖和濾鏡
- 自動光學檢測、物體分割和偵測
- 學習物體分類
- 偵測臉部特徵並套用遮罩
- 視頻監控、背景建模和形態學操作
- 學習物體追蹤
- 開發用於文字識別的分割算法
- 使用Tesseract進行文字識別
- 使用OpenCV進行深度學習
- 在Raspberry Pi上進行卡通化和膚色分析
- 使用SfM模組探索運動結構
- 使用Face模組進行臉部特徵點和姿勢識別
- 使用深度卷積網絡進行車牌識別
- 使用DNN模組進行臉部偵測和識別
- 使用ArUco模組進行Android相機校準和擴增實境
- 使用拼接模組在iOS上製作全景圖
- 尋找最適合的OpenCV算法
- 避免OpenCV中的常見問題