Mastering OpenCV 3, 2/e (Paperback) (精通 OpenCV 3(第二版))

Daniel Lelis Baggio, Shervin Emami, David Millan Escriva, Khvedchenia Ievgen, Jason Saragih, Roy Shilkrot

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商品描述

Practical Computer Vision Projects

About This Book

  • Updated for OpenCV 3, this book covers new features that will help you unlock the full potential of OpenCV 3
  • Written by a team of 7 experts, each chapter explores a new aspect of OpenCV to help you make amazing computer-vision aware applications
  • Project-based approach with each chapter being a complete tutorial, showing you how to apply OpenCV to solve complete problems

Who This Book Is For

This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.

What You Will Learn

  • Execute basic image processing operations and cartoonify an image
  • Build an OpenCV project natively with Raspberry Pi and cross-compile it for Raspberry Pi.text
  • Extend the natural feature tracking algorithm to support the tracking of multiple image targets on a video
  • Use OpenCV 3's new 3D visualization framework to illustrate the 3D scene geometry
  • Create an application for Automatic Number Plate Recognition (ANPR) using a support vector machine and Artificial Neural Networks
  • Train and predict pattern-recognition algorithms to decide whether an image is a number plate
  • Use POSIT for the six degrees of freedom head pose
  • Train a face recognition database using deep learning and recognize faces from that database

In Detail

As we become more capable of handling data in every kind, we are becoming more reliant on visual input and what

商品描述(中文翻譯)

《實用計算機視覺專案》

關於本書


  • 本書更新至OpenCV 3版本,介紹了能夠發揮OpenCV 3潛力的新功能

  • 由7位專家團隊撰寫,每一章節都探討OpenCV的不同方面,幫助您創建令人驚艷的計算機視覺應用程式

  • 以專案為基礎的方法,每一章節都是一個完整的教學,展示如何應用OpenCV解決完整的問題

本書適合對象

本書適合具備基本OpenCV知識且具備C++程式設計能力的讀者。您需要對一些較理論/數學概念有一定的理解,因為本書進展相對迅速。

您將學到什麼


  • 執行基本的影像處理操作並將影像轉換為卡通風格

  • 使用Raspberry Pi原生建立OpenCV專案並進行交叉編譯以適用於Raspberry Pi

  • 擴展自然特徵追蹤演算法以支援在影片中追蹤多個影像目標

  • 使用OpenCV 3的新3D視覺化框架來呈現3D場景幾何

  • 使用支援向量機和人工神經網路創建自動車牌識別(ANPR)應用程式

  • 訓練和預測模式識別演算法,以判斷一個影像是否為車牌

  • 使用POSIT進行六自由度頭部姿態估計

  • 使用深度學習訓練人臉識別資料庫並從該資料庫識別人臉

詳細內容

隨著我們在各種數據處理方面變得更加能幹,我們對視覺輸入和其所帶來的影響越來越依賴。