Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV

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,旨在將您從基礎知識帶到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等工具進行圖像處理、物體檢測、特徵檢測等操作
- 通過真實世界的數據集和完整的代碼,本書是您理解計算機視覺的一站式指南

書籍描述:
本書介紹了計算機視覺領域中的幾種最新方法。您將首先建立適合實際應用的Python環境,包括使用Anaconda設置OpenCV、Tensorflow和Keras等庫。利用這些庫,您將開始理解圖像轉換和過濾的概念。本書詳細解釋了FAST和ORB等特徵檢測器,並使用它們來查找相似的物體。

通過介紹卷積神經網絡,您將學習如何使用Keras構建深度神經網絡,以及如何使用它對Fashion-MNIST數據集進行分類。在物體檢測方面,您將學習實現簡單的人臉檢測器以及使用Tensorflow實現基於深度學習的複雜物體檢測器(如Faster R-CNN和SSD)的工作原理。您將使用FCN模型進行語義分割,並使用Deep SORT跟踪物體。此外,您還將在標準數據集上使用ORB-SLAM等視覺SLAM技術。

通過閱讀本書,您將對不同的計算機視覺技術有深入的理解,並能夠在應用中應用它們。

您將學到:
- 學習使用OpenCV進行圖像處理的基礎知識
- 實現並可視化圖像過濾器,如平滑、膨脹、直方圖均衡化等
- 在各種庫和平台上建立環境,如OpenCV、Keras和Tensorflow,以便開始使用計算機視覺,並使用每個章節的相應數據集,如MSCOCO、MOT和Fashion-MNIST
- 理解圖像轉換和降採樣,並進行實際實現
- 探索用於計算機視覺和使用Keras的卷積神經網絡的神經網絡
- 理解基於深度學習的物體檢測,如Faster-R-CNN、SSD等
- 實現基於深度學習的物體跟踪
- 理解ORB-SLAM等視覺SLAM技術

本書適合機器學習從業者和深度學習愛好者,他們希望以最實用的方式理解和實現與計算機視覺和圖像處理相關的各種任務。具備一些編程經驗將有所幫助,而熟悉Python則是一個額外的優勢。

目錄:
1. 計算機視覺的快速介紹
2. 库、開發平台和數據集
3. OpenCV中的圖像過濾和轉換
4. 特徵提取技術的應用
5. 高級特性介紹
6. 基於特徵的物體檢測
7. 物體跟踪和分割
8. 3D計算機視覺
9. 附錄A
10. 附錄B