OpenCV 4 Computer Vision Application Programming Cookbook
暫譯: OpenCV 4 電腦視覺應用程式開發食譜

Escriva, David Millan, Laganiere, Robert

OpenCV 4 Computer Vision Application Programming Cookbook

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商品描述

Key Features

  • Explore the latest features and APIs in OpenCV 4 and build computer vision algorithms
  • Develop effective, robust, and fail-safe vision for your applications
  • Build computer vision algorithms with machine learning capabilities

Book Description

OpenCV is an image and video processing library used for all types of image and video analysis. Throughout the book, you'll work through recipes that implement a variety of tasks, such as facial recognition and detection. With 70 self-contained tutorials, this book examines common pain points and best practices for computer vision (CV) developers. Each recipe addresses a specific problem and offers a proven, best-practice solution with insights into how it works, so that you can copy the code and configuration files and modify them to suit your needs.

This book begins by setting up OpenCV, and explains how to manipulate pixels. You'll understand how you can process images with classes and count pixels with histograms. You'll also learn detecting, describing, and matching interest points. As you advance through the chapters, you'll get to grips with estimating projective relations in images, reconstructing 3D scenes, processing video sequences, and tracking visual motion. In the final chapters, you'll cover deep learning concepts such as face and object detection.

By the end of the book, you'll be able to confidently implement a range to computer vision algorithms to meet the technical requirements of your complex CV projects

What you will learn

  • Install and create a program using the OpenCV library
  • Segment images into homogenous regions and extract meaningful objects
  • Apply image filters to enhance image content
  • Exploit image geometry to relay different views of a pictured scene
  • Calibrate the camera from different image observations
  • Detect people and objects in images using machine learning techniques
  • Reconstruct a 3D scene from images
  • Explore face detection using deep learning

Who this book is for

If you're a CV developer or professional who already uses or would like to use OpenCV for building computer vision software, this book is for you. You'll also find this book useful if you're a C++ programmer looking to extend your computer vision skillset by learning OpenCV.

商品描述(中文翻譯)

**主要特點**

- 探索 OpenCV 4 中的最新功能和 API，並構建計算機視覺算法
- 為您的應用程序開發有效、穩健且安全的視覺系統
- 構建具有機器學習能力的計算機視覺算法

**書籍描述**

OpenCV 是一個用於各類圖像和視頻分析的圖像和視頻處理庫。在整本書中，您將通過實現各種任務的食譜進行學習，例如面部識別和檢測。這本書包含 70 個獨立的教程，探討計算機視覺 (CV) 開發者常見的痛點和最佳實踐。每個食譜針對特定問題，提供經過驗證的最佳解決方案，並深入解釋其工作原理，以便您可以複製代碼和配置文件並根據需要進行修改。

本書首先介紹 OpenCV 的設置，並解釋如何操作像素。您將了解如何使用類別處理圖像以及如何使用直方圖計算像素。您還將學習如何檢測、描述和匹配興趣點。隨著章節的深入，您將掌握如何估計圖像中的投影關係、重建 3D 場景、處理視頻序列以及跟踪視覺運動。在最後幾章中，您將涵蓋深度學習概念，例如面部和物體檢測。

到書籍結束時，您將能夠自信地實現一系列計算機視覺算法，以滿足您複雜 CV 項目的技術需求。

**您將學到的內容**

- 安裝並使用 OpenCV 庫創建程序
- 將圖像分割為同質區域並提取有意義的物體
- 應用圖像濾鏡以增強圖像內容
- 利用圖像幾何傳達所描繪場景的不同視角
- 從不同的圖像觀察中校準相機
- 使用機器學習技術檢測圖像中的人和物體
- 從圖像重建 3D 場景
- 探索使用深度學習的面部檢測

**本書適合誰**

如果您是計算機視覺開發者或專業人士，已經在使用或希望使用 OpenCV 來構建計算機視覺軟件，那麼這本書適合您。如果您是 C++ 程序員，想通過學習 OpenCV 擴展您的計算機視覺技能，您也會發現這本書非常有用。

作者簡介

David Millán Escrivá was 8 years old when he wrote his first program on an 8086 PC in Basic, which enabled the 2D plotting of basic equations. In 2005, he finished his studies in IT with honors, through the Universitat Politécnica de Valencia, in human-computer interaction supported by computer vision with OpenCV (v0.96). He has worked with Blender, an open source, 3D software project, and on its first commercial movie, Plumiferos, as a computer graphics software developer. David has more than 10 years' experience in IT, with experience in computer vision, computer graphics, pattern recognition, and machine learning, working on different projects, and at different start-ups, and companies. He currently works as a researcher in computer vision.

Robert Laganiere is a professor at the University of Ottawa, Canada. He is also a faculty member of the VIVA research lab and is the coauthor of several scientific publications and patents in content-based video analysis, visual surveillance, driver-assistance, object detection, and tracking. He cofounded Visual Cortek, a video analytics start-up, which was later acquired by iWatchLife. He is also a consultant in computer vision and has assumed the role of chief scientist in a number of start-ups companies, including Cognivue Corp, iWatchLife, and Tempo Analytics. Robert has a Bachelor of Electrical Engineering degree from Ecole Polytechnique in Montreal (1987), and M.Sc. and Ph.D. degrees from INRS-Telecommunications, Montreal (1996).

作者簡介(中文翻譯)

大衛·米蘭·埃斯克里瓦在8歲時便在8086 PC上用Basic寫下了他的第一個程式，這個程式能夠進行基本方程式的2D繪圖。2005年，他以優異的成績從瓦倫西亞理工大學畢業，專攻人機互動，並利用OpenCV（v0.96）進行計算機視覺的研究。他曾參與開源3D軟體專案Blender的開發，並在其首部商業電影《Plumiferos》中擔任計算機圖形軟體開發者。大衛在IT領域擁有超過10年的經驗，專長於計算機視覺、計算機圖形、模式識別和機器學習，參與過不同的專案，並在多家初創公司和企業工作。目前，他擔任計算機視覺研究員。

羅伯特·拉加尼耶是加拿大渥太華大學的教授。他也是VIVA研究實驗室的教職員，並且是多篇關於基於內容的視頻分析、視覺監控、駕駛輔助、物體檢測和追蹤的科學出版物和專利的共同作者。他共同創立了視頻分析初創公司Visual Cortek，該公司後來被iWatchLife收購。他還擔任計算機視覺顧問，並在多家初創公司中擔任首席科學家，包括Cognivue Corp、iWatchLife和Tempo Analytics。羅伯特擁有蒙特利爾理工學院的電機工程學士學位（1987年），以及蒙特利爾INRS-電信的碩士和博士學位（1996年）。

目錄大綱

  1. Introduction to OpenCV
  2. Manipulating the pixels
  3. Processing Images with Classes
  4. Counting the Pixels with Histograms
  5. Transforming images with morphological operations
  6. Filtering the images
  7. Extracting Lines, Contours, and Components
  8. Detecting interest points
  9. Describing and Matching interest Points
  10. Estimating projective relations in images
  11. Reconstructing 3D scenes
  12. Processing video sequences
  13. Tracking visual motion
  14. Learning from Examples
  15. OpenCV Advanced features

目錄大綱(中文翻譯)


  1. Introduction to OpenCV

  2. Manipulating the pixels

  3. Processing Images with Classes

  4. Counting the Pixels with Histograms

  5. Transforming images with morphological operations

  6. Filtering the images

  7. Extracting Lines, Contours, and Components

  8. Detecting interest points

  9. Describing and Matching interest Points

  10. Estimating projective relations in images

  11. Reconstructing 3D scenes

  12. Processing video sequences

  13. Tracking visual motion

  14. Learning from Examples

  15. OpenCV Advanced features

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