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iOS Application Development with OpenCV 3

You're reading from   iOS Application Development with OpenCV 3 Create four mobile apps and explore the world through photography and computer vision

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Product type Paperback
Published in Jun 2016
Publisher Packt
ISBN-13 9781785289491
Length 228 pages
Edition 1st Edition
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Author (1):
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Joseph Howse Joseph Howse
Author Profile Icon Joseph Howse
Joseph Howse
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Table of Contents (7) Chapters Close

Preface 1. Setting Up Software and Hardware FREE CHAPTER 2. Capturing, Storing, and Sharing Photos 3. Blending Images 4. Detecting and Merging Faces of Mammals 5. Classifying Coins and Commodities Index

Defining blob descriptors and a blob classifier


Earlier in this chapter, in the Understanding keypoint matching section, we introduced the concept that a keypoint has a descriptor or set of descriptive statistics. Similarly, we can define a custom descriptor for a blob. As our classifier relies on histogram comparison and keypoint matching, let's say that a blob's descriptor consists of a normalized histogram and a matrix of keypoint descriptors. The descriptor object is also a convenient place to put the label. Create a new header file, BlobDescriptor.h, and put the following declaration of a BlobDescriptor class in it:

#ifndef BLOB_DESCRIPTOR_H
#define BLOB_DESCRIPTOR_H

#include <opencv2/core.hpp>

class BlobDescriptor
{
public:
  BlobDescriptor(const cv::Mat &normalizedHistogram,
    const cv::Mat &keypointDescriptors, uint32_t label);
  
  const cv::Mat &getNormalizedHistogram() const;
  const cv::Mat &getKeypointDescriptors() const;
  uint32_t getLabel() const...
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