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OpenCV with Python By Example

You're reading from  OpenCV with Python By Example

Product type Book
Published in Sep 2015
Publisher Packt
ISBN-13 9781785283932
Pages 296 pages
Edition 1st Edition
Languages
Author (1):
Prateek Joshi Prateek Joshi
Profile icon Prateek Joshi

Table of Contents (19) Chapters

OpenCV with Python By Example
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Applying Geometric Transformations to Images 2. Detecting Edges and Applying Image Filters 3. Cartoonizing an Image 4. Detecting and Tracking Different Body Parts 5. Extracting Features from an Image 6. Creating a Panoramic Image 7. Seam Carving 8. Detecting Shapes and Segmenting an Image 9. Object Tracking 10. Object Recognition 11. Stereo Vision and 3D Reconstruction 12. Augmented Reality Index

Using Haar cascades to detect things


When we say Haar cascades, we are actually talking about cascade classifiers based on Haar features. To understand what this means, we need to take a step back and understand why we need this in the first place. Back in 2001, Paul Viola and Michael Jones came up with a very effective object detection method in their seminal paper. It has become one of the major landmarks in the field of machine learning.

In their paper, they have described a machine learning technique where a boosted cascade of simple classifiers is used to get an overall classifier that performs really well. This way, we can circumvent the process of building a single complex classifier that performs with high accuracy. The reason this is so amazing is because building a robust single-step classifier is a computationally intensive process. Besides, we need a lot of training data to build such a classifier. The model ends up becoming complex and the performance might not be up to the mark...

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