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Feature extraction and matching method and device for digital image based on PCA (principal component analysis)

A digital image and feature extraction technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as algorithm complexity and data redundancy

Active Publication Date: 2013-05-01
BEIJING UNIV OF TECH
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Problems solved by technology

E.g image 3 In this method, the user needs to extract the features of the "elephant" and perform matching, without the information of grassland and tree branches in the image. This information is "wrongly" selected by the user, so it will also participate in the extraction of image features, which brings complexity to the algorithm. Degree and Data Redundancy

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  • Feature extraction and matching method and device for digital image based on PCA (principal component analysis)
  • Feature extraction and matching method and device for digital image based on PCA (principal component analysis)
  • Feature extraction and matching method and device for digital image based on PCA (principal component analysis)

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Embodiment Construction

[0061] In order to better understand the present invention, the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. The present invention proposes a new method and device for digital image feature extraction and matching based on principal component analysis, which includes the following steps:

[0062] 1) Scale space extreme point detection.

[0063] First, according to the convolution of the original image I(x,y) and the variable-scale Gaussian function, a multi-scale spatial image L(x,y,σ) is generated. The formula is: L(x,y,σ)=G( x,y,σ)*I(x,y)

[0064] The Gaussian convolution kernel G(x,y,σ) is defined as:

[0065] G ( x , y , σ ) = 1 2 π σ 2 e ...

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Abstract

The invention provides a feature extraction and matching method and device for a digital image based on PCA (principal component analysis), belonging to the technical field of image analysis. The method comprises the following steps of: 1) detecting scale space extreme points; 2) locating the extreme points; 3) distributing directions of the extreme points; 4) reducing dimension of PCA and generating image feature descriptors; and 5) judging similarity measurement and feature matching. The device mainly comprises a numerical value preprocessing module, a feature point extraction module and a feature point matching module. Compared with the existing SIFI (Scale Invariant Feature Transform) feature extraction and matching algorithm, the feature extraction and matching method has higher accuracy and matching speed. The method and device provided by the invention can be directly applied to such machine vision fields as digital image retrieval based on contents, digital video retrieval based on contents, digital image fusion and super-resolution image reconstruction.

Description

technical field [0001] The invention relates to a method and device for feature extraction and matching of digital images. Background technique [0002] Principal Component Analysis (PCA), also known as principal component analysis. It is a multivariate statistical analysis method that selects a small number of important variables through linear transformation of multiple variables. This method is an effective analysis method that converts multiple related variables into a few independent variables, and achieves the purpose of reducing data channels or subbands by reducing the dependence between channels. [0003] Feature extraction is a concept in computer vision and image processing. It refers to the use of computers to extract image information and determine whether each image point belongs to an image feature. The result of feature extraction is to divide the points on the image into different subsets, which often belong to isolated points, continuous curves or contin...

Claims

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Application Information

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IPC IPC(8): G06T7/00
Inventor 王卓峥贾克斌
Owner BEIJING UNIV OF TECH
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