<< Chapter < Page Chapter >> Page >

Introduction

Object recognition is one of the hottest areas in computer vision. Finding and identifying objects in an image is fundamental to facial recognition, object tracking in videos, object matching and many other applications. To achieve object recognition (from images), one first must be able to extract distinct features of the image, then correctly identify the object of interest from these key features. Our group explored three different algorithms. The first, Scale-Invariant Feature Transform, was a method to obtain the distinct features of the image while the other two, Hough Transform and Moment of Inertia, were pattern recognizing algorithms.

Motivation

We wanted to simulate a user being given a sheet of paper with squares and other shapes on it. The user would tell us how many squares appeared in the image. Because we were highly interested in the field of object recognition we decided to implement two systems and compare the results. Both systems used Scale-Invariant Feature Transform to obtain feature points. The first system then passed those results into the Hough Transform. The second system used the feature points to implement a formula involving the properties of the Moment of Inertia.

design1
Flow of the system design.

square
Our square template.

testImage4
An example test image.

Get Jobilize Job Search Mobile App in your pocket Now!

Get it on Google Play Download on the App Store Now




Source:  OpenStax, A comparison of object recognition using the hough transform and the properties of moment of inertia. OpenStax CNX. Dec 16, 2014 Download for free at http://legacy.cnx.org/content/col11727/1.4
Google Play and the Google Play logo are trademarks of Google Inc.

Notification Switch

Would you like to follow the 'A comparison of object recognition using the hough transform and the properties of moment of inertia' conversation and receive update notifications?

Ask