Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV
暫譯: 實用電腦視覺:使用 TensorFlow、Keras 和 OpenCV 從影像中提取有價值的信息

Abhinav Dadhich

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商品描述

A practical guide designed to get you from basics to current state of art in computer vision systems.

Key Features

  • Master the different tasks associated with Computer Vision and develop your own Computer Vision applications with ease
  • Leverage the power of Python, Tensorflow, Keras, and OpenCV to perform image processing, object detection, feature detection and more
  • With real-world datasets and fully functional code, this book is your one-stop guide to understanding Computer Vision

Book Description

In this book, you will find several recently proposed methods in various domains of computer vision. You will start by setting up the proper Python environment to work on practical applications. This includes setting up libraries such as OpenCV, TensorFlow, and Keras using Anaconda. Using these libraries, you'll start to understand the concepts of image transformation and filtering. You will find a detailed explanation of feature detectors such as FAST and ORB; you'll use them to find similar-looking objects.

With an introduction to convolutional neural nets, you will learn how to build a deep neural net using Keras and how to use it to classify the Fashion-MNIST dataset. With regard to object detection, you will learn the implementation of a simple face detector as well as the workings of complex deep-learning-based object detectors such as Faster R-CNN and SSD using TensorFlow. You'll get started with semantic segmentation using FCN models and track objects with Deep SORT. Not only this, you will also use Visual SLAM techniques such as ORB-SLAM on a standard dataset.

By the end of this book, you will have a firm understanding of the different computer vision techniques and how to apply them in your applications.

What you will learn

  • Learn the basics of image manipulation with OpenCV
  • Implement and visualize image filters such as smoothing, dilation, histogram equalization, and more
  • Set up various libraries and platforms, such as OpenCV, Keras, and Tensorflow, in order to start using computer vision, along with appropriate datasets for each chapter, such as MSCOCO, MOT, and Fashion-MNIST
  • Understand image transformation and downsampling with practical implementations.
  • Explore neural networks for computer vision and convolutional neural networks using Keras
  • Understand working on deep-learning-based object detection such as Faster-R-CNN, SSD, and more
  • Explore deep-learning-based object tracking in action
  • Understand Visual SLAM techniques such as ORB-SLAM

Who This Book Is For

This book is for machine learning practitioners and deep learning enthusiasts who want to understand and implement various tasks associated with Computer Vision and image processing in the most practical manner possible. Some programming experience would be beneficial while knowing Python would be an added bonus.

Table of Contents

  1. A fast introduction to Computer vision
  2. Libraries, Development platform and Datasets
  3. Image filtering and Transformations in OpenCV
  4. Application of Feature Extraction Extraction technique
  5. Introduction to Advanced Features
  6. Feature based object detection
  7. Object Tracking and Segmentation
  8. 3D Computer Vision
  9. Appendix A
  10. Appendix B

商品描述(中文翻譯)

一本實用指南,旨在幫助您從基礎知識進入計算機視覺系統的最新技術。

主要特點


  • 掌握與計算機視覺相關的不同任務,輕鬆開發自己的計算機視覺應用程式

  • 利用 Python、Tensorflow、Keras 和 OpenCV 的強大功能進行影像處理、物體檢測、特徵檢測等

  • 本書提供真實世界的數據集和完整功能的代碼,是您理解計算機視覺的一站式指南

書籍描述

在本書中,您將找到最近在計算機視覺各個領域提出的幾種方法。您將從設置適當的 Python 環境開始,以便進行實際應用。這包括使用 Anaconda 設置 OpenCV、TensorFlow 和 Keras 等庫。使用這些庫,您將開始理解影像轉換和過濾的概念。您將找到對特徵檢測器(如 FAST 和 ORB)的詳細解釋;您將使用它們來尋找相似的物體。

通過對卷積神經網絡的介紹,您將學習如何使用 Keras 構建深度神經網絡,以及如何使用它來分類 Fashion-MNIST 數據集。關於物體檢測,您將學習簡單的人臉檢測器的實現,以及使用 TensorFlow 的複雜基於深度學習的物體檢測器(如 Faster R-CNN 和 SSD)的工作原理。您將開始使用 FCN 模型進行語義分割,並使用 Deep SORT 追蹤物體。不僅如此,您還將在標準數據集上使用 Visual SLAM 技術,如 ORB-SLAM。

在本書結束時,您將對不同的計算機視覺技術有堅實的理解,並知道如何在您的應用中應用它們。

您將學到什麼


  • 學習使用 OpenCV 進行影像操作的基本知識

  • 實現和可視化影像過濾器,如平滑、膨脹、直方圖均衡等

  • 設置各種庫和平台,如 OpenCV、Keras 和 Tensorflow,以開始使用計算機視覺,並為每一章提供適當的數據集,如 MSCOCO、MOT 和 Fashion-MNIST

  • 理解影像轉換和下採樣的實際實現。

  • 探索計算機視覺的神經網絡和使用 Keras 的卷積神經網絡

  • 理解基於深度學習的物體檢測(如 Faster-R-CNN、SSD 等)的工作原理

  • 探索基於深度學習的物體追蹤的實際應用

  • 理解 Visual SLAM 技術,如 ORB-SLAM

本書適合誰

本書適合希望以最實用的方式理解和實現與計算機視覺和影像處理相關的各種任務的機器學習從業者和深度學習愛好者。具備一些程式設計經驗將是有益的,而了解 Python 將是額外的加分。

目錄


  1. 計算機視覺的快速介紹

  2. 庫、開發平台和數據集

  3. OpenCV 中的影像過濾和轉換

  4. 特徵提取技術的應用

  5. 高級特徵介紹

  6. 基於特徵的物體檢測

  7. 物體追蹤和分割

  8. 3D 計算機視覺

  9. 附錄 A

  10. 附錄 B

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