Book Image

Practical Computer Vision

By : Abhinav Dadhich
Book Image

Practical Computer Vision

By: Abhinav Dadhich

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.

Related Content you might be interested in

Current Title:

Small book image
Practical Computer Vision
Abhinav Dadhich
Feb, 2018 | 234 pages
Small book image
Computer Vision with Python 3
Salil Kapur | Saurabh Kapur
Aug, 2017 | 206 pages
Small book image
Hands-On Image Processing with Python
Sandipan Dey
Nov, 2018 | 492 pages
Small book image
Practical Convolutional Neural Networks
Mohit Sewak | Md Rezaul Karim | Pradeep Pujari
Feb, 2018 | 218 pages
Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
A Fast Introduction to Computer Vision
A Fast Introduction to Computer Vision
What constitutes computer vision?
Computer vision is everywhere
Getting started
Computer vision research conferences
Summary
2
Libraries, Development Platform, and Datasets
Libraries, Development Platform, and Datasets
Libraries and installation
Datasets
Summary
References
3
Image Filtering and Transformations in OpenCV
Image Filtering and Transformations in OpenCV
Datasets and libraries required
Image manipulation
Introduction to filters
Transformation of an image
Image pyramids
Summary
4
What is a Feature?
What is a Feature?
Features use cases
Harris Corner Detection
Summary
References
5
Convolutional Neural Networks
Convolutional Neural Networks
Datasets and libraries used
Introduction to neural networks
Revisiting the convolution operation
Convolutional Neural Networks
CNN in practice
Summary
6
Feature-Based Object Detection
Feature-Based Object Detection
Introduction to object detection
Challenges in object detection
Dataset and libraries used
Methods for object detection
Summary
References
7
Segmentation and Tracking
Segmentation and Tracking
Datasets and libraries
Segmentation
Tracking
Summary
References
8
3D Computer Vision
3D Computer Vision
Dataset and libraries
Applications
Image formation
Aligning images
Visual odometry
Visual SLAM
Summary
References
9
Mathematics for Computer Vision
Mathematics for Computer Vision
Datasets and libraries
Linear algebra
Introduction to probability theory
Summary
10
Machine Learning for Computer Vision
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
11
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Other Books You May Enjoy

If you enjoyed this book, you may be interested in these other books by Packt:

Machine Learning for OpenCV
Michael Beyeler

ISBN: 978-1-78398-028-4

  • Explore and make effective use of OpenCV's Machine Learning module
  • Learn deep learning for computer vision with Python
  • Master linear regression and regularization techniques
  • Classify objects such as flower species, handwritten digits, and pedestrians
  • Explore the effective use of support vector machines, boosted decision trees, and random forests
  • Get acquainted with neural networks and Deep Learning to address real-world problems
  • Discover hidden structures in your data using k-means clustering
    Get to grips with data pre-processing and feature engineering

Computer Vision with Python 3
Saurabh Kapur

ISBN: 978-1-78829-976-3

  • Working with open source libraries such Pillow, Scikit-image, and OpenCV
  • Writing...
Previous Section
End of Section 1
Next Section