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Practical Computer Vision

By : Abhinav Dadhich

1.5 (2)

Buy this Book

Practical Computer Vision

1.5 (2)

By: Abhinav Dadhich

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

A Fast Introduction to Computer Vision

What constitutes computer vision?

Computer vision is everywhere

Getting started

Computer vision research conferences

Summary

Libraries, Development Platform, and Datasets

Libraries and installation

Datasets

Summary

References

Image Filtering and Transformations in OpenCV

Datasets and libraries required

Image manipulation

Introduction to filters

Transformation of an image

Image pyramids

Summary

What is a Feature?

Features use cases

Harris Corner Detection

Summary

References

Convolutional Neural Networks

Datasets and libraries used

Introduction to neural networks

Revisiting the convolution operation

Convolutional Neural Networks

CNN in practice

Summary

Feature-Based Object Detection

Introduction to object detection

Challenges in object detection

Dataset and libraries used

Methods for object detection

Summary

References

Segmentation and Tracking

Datasets and libraries

Segmentation

Tracking

Summary

References

3D Computer Vision

Dataset and libraries

Applications

Image formation

Aligning images

Visual odometry

Visual SLAM

Summary

References

Mathematics for Computer Vision

Datasets and libraries

Linear algebra

Introduction to probability theory

Summary

Machine Learning for Computer Vision

What is machine learning?

Kinds of machine learning techniques

Dimensionality's curse

A rolling-ball view of learning

Useful tools

Evaluation

Summary

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Datasets and libraries required

We will be using a sample image for most of this task. However, you can try the code with any other image or also use a webcam to see live results. The libraries used in this chapter are OpenCV, NumPy, and matplotlib. Even if you are not acquainted with libraries, you can still understand the code and implement them. There are also remarks for special cases when using a Jupyter notebook for the code written here:

import numpy as np 
import matplotlib.pyplot as plt 
import cv2 
# With jupyter notebook uncomment below line 
# %matplotlib inline 
# This plots figures inside the notebook

The sample image used in this chapter can be loaded as follows:

# read an image 
img = cv2.imread('flower.png')

This image can be plotted either using OpenCV or matplotlib libraries. We will be using matplotlib for the majority of plots as this will be beneficial...