Mastering OpenCV Android Application Programming

Master the art of implementing computer vision algorithms on Android platforms to build robust and efficient applications

Mastering OpenCV Android Application Programming

Salil Kapur, Nisarg Thakkar

1 customer reviews
Master the art of implementing computer vision algorithms on Android platforms to build robust and efficient applications
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Book Details

ISBN 139781783988204
Paperback216 pages

Book Description

OpenCV is a famous computer vision library, used to analyze and transform copious amounts of image data, even in real time and on a mobile device.

This book focuses on leveraging mobile platforms to build interactive and useful applications. The book starts off with an introduction to OpenCV and Android and how they interact with each other using OpenCV's Java API. You'll also discover basic image processing techniques such as erosion and dilation of images, before walking through how to build more complex applications, such as object detection, image stitching, and face detection. As you progress, you will be introduced to OpenCV's machine learning framework, enabling you to make your applications smarter.

The book ends with a short chapter covering useful Android tips and tricks and some common errors and solutions that people might face while building an application. By the end of the book, readers will have gained more expertise in building their own OpenCV projects for the Android platform and integrating OpenCV application programming into existing projects.

Table of Contents

Chapter 1: Applying Effects to Images
Getting started
Setting up OpenCV
Storing images in OpenCV
Linear filters in OpenCV
Summary
Chapter 2: Detecting Basic Features in Images
Creating our application
Edge and Corner detection
Hough transformations
Contours
Project – detecting a Sudoku puzzle in an image
Summary
Chapter 3: Detecting Objects
What are features?
Scale Invariant Feature Transform
Matching features and detecting objects
Speeded Up Robust Features
Oriented FAST and Rotated BRIEF
Binary Robust Invariant Scalable Keypoints
Fast Retina Keypoint
Summary
Chapter 4: Drilling Deeper into Object Detection – Using Cascade Classifiers
An introduction to cascade classifiers
Face detection using the cascade classifier
HOG descriptors
Project – Happy Camera
Summary
Chapter 5: Tracking Objects in Videos
Optical flow
Image pyramids
Basic 2D transformations
Global motion estimation
The Kanade-Lucas-Tomasi tracker
Summary
Chapter 6: Working with Image Alignment and Stitching
Image stitching
Summary
Chapter 7: Bringing Your Apps to Life with OpenCV Machine Learning
Optical Character Recognition
Solving a Sudoku puzzle
Summary
Chapter 8: Troubleshooting and Best Practices
Troubleshooting errors
Best practices
Summary
Chapter 9: Developing a Document Scanning App
Let's begin
The algorithm
Implementing on Android
Summary

What You Will Learn

  • Understand image processing using OpenCV
  • Detect specific objects in an image or video using various state-of-the-art feature-matching algorithms such as SIFT, SURF, and ORB
  • Perform image transformations such as changing color, space, resizing, applying filters like Gaussian blur, and likes
  • Use mobile phone cameras to interact with the real world
  • Explore face detection, object detection, and image stitching in OpenCV Android programming
  • Build smarter applications by using machine learning algorithms
  • Learn to debug applications and create optimal custom algorithms by understanding how data is stored internally

Authors

Table of Contents

Chapter 1: Applying Effects to Images
Getting started
Setting up OpenCV
Storing images in OpenCV
Linear filters in OpenCV
Summary
Chapter 2: Detecting Basic Features in Images
Creating our application
Edge and Corner detection
Hough transformations
Contours
Project – detecting a Sudoku puzzle in an image
Summary
Chapter 3: Detecting Objects
What are features?
Scale Invariant Feature Transform
Matching features and detecting objects
Speeded Up Robust Features
Oriented FAST and Rotated BRIEF
Binary Robust Invariant Scalable Keypoints
Fast Retina Keypoint
Summary
Chapter 4: Drilling Deeper into Object Detection – Using Cascade Classifiers
An introduction to cascade classifiers
Face detection using the cascade classifier
HOG descriptors
Project – Happy Camera
Summary
Chapter 5: Tracking Objects in Videos
Optical flow
Image pyramids
Basic 2D transformations
Global motion estimation
The Kanade-Lucas-Tomasi tracker
Summary
Chapter 6: Working with Image Alignment and Stitching
Image stitching
Summary
Chapter 7: Bringing Your Apps to Life with OpenCV Machine Learning
Optical Character Recognition
Solving a Sudoku puzzle
Summary
Chapter 8: Troubleshooting and Best Practices
Troubleshooting errors
Best practices
Summary
Chapter 9: Developing a Document Scanning App
Let's begin
The algorithm
Implementing on Android
Summary

Book Details

ISBN 139781783988204
Paperback216 pages
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