Method for classifying invariant moment of similar objects by multi-scale mathematical morphology

A technology of mathematical morphology and classification method, applied in the direction of instruments, character and pattern recognition, computer parts, etc., to achieve the effect of effectively classifying similar objects, broad market prospects and application value, and wide application

Inactive Publication Date: 2009-12-16
BEIHANG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the effect of wavelet invariant moments is inhibited to a certain extent when distinguishing different types of objects with very similar shapes.

Method used

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  • Method for classifying invariant moment of similar objects by multi-scale mathematical morphology
  • Method for classifying invariant moment of similar objects by multi-scale mathematical morphology
  • Method for classifying invariant moment of similar objects by multi-scale mathematical morphology

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Embodiment Construction

[0032] The prerequisite for implementing the present invention is: training images of objects to be classified must be provided.

[0033] The condition for classifying by using the present invention is: the image of the object to be classified needs to be obtained.

[0034] In order to better understand the technical solutions of the present invention, the implementation manners of the present invention will be further described below in conjunction with the accompanying drawings.

[0035]The present invention uses a method for classifying similar objects invariant moments of multi-scale mathematical morphology, and its working process is as follows: figure 1 As shown, the specific implementation details of each part are as follows:

[0036] Step 1: Take the two types of objects f that need to be distinguished 1 and f 2 training images;

[0037] Step 2: Set i=1 (1≤i≤n), and take the i-th invariant moment. When classifying similar objects, the effect of Hu moment is poor. ...

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Abstract

The invention provides a method for classifying an invariant moment of similar objects by multi-scale mathematical morphology. The method comprises the following basic flow: firstly, extracting detailed characteristics of an object training image by the multi-scale mathematical morphology; performing various combinations on multi-scale detailed characteristics of an object to form real different parts of similar objects in different types on multiple scales; secondly, calculating values of invariant moments of the real different parts by selecting an arbitrary invariant moment method; thirdly, evaluating the values of the invariant moments of the real different parts through one measurement, and automatically selecting the real different parts which can effectively differentiate the similar objects and the corresponding invariant moment thereof; and finally, classifying the similar objects by the selected real different parts and the corresponding invariant moment thereof. The method can realize effective classification on the similar objects, can be widely applied to various target identification and classification systems, and has wide market prospect and application value in the field of digital image processing and pattern recognition.

Description

(1) Technical field [0001] The invention relates to a method for classifying invariant moments of similar objects using multi-scale mathematical morphology, and belongs to the technical fields of digital image processing and pattern recognition. It mainly involves mathematical morphology, invariant moments and target recognition technology, and can be widely used in target recognition and target classification. (2) Background technology [0002] Invariant moment is an important mathematical tool for target recognition and classification, and good invariant moment classification ability can effectively improve the effectiveness of target recognition and classification. In order to achieve better recognition and classification results, various invariant moments are proposed, including: Hu moment, Z moment, wavelet moment, etc. Although these moment invariant methods are good at distinguishing objects of different classes, they are less effective when the shapes of objects of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 白相志周付根
Owner BEIHANG UNIV
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