SIFT image matching method based on module value difference mirror image invariant property

A matching method and image technology, applied in the field of image matching, can solve the problems of slow matching speed, insufficient real-time effect, and high dimension of feature descriptors, and achieve the effect of increasing real-time performance.

Active Publication Date: 2013-10-02
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0010] Although SIFT has been proven to be the most effective local feature detection method at present, due to the high dimensionality of the feature descriptors generated by it,

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  • SIFT image matching method based on module value difference mirror image invariant property
  • SIFT image matching method based on module value difference mirror image invariant property
  • SIFT image matching method based on module value difference mirror image invariant property

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[0035] Further, the present invention is a SIFT method based on image invariance of modulus difference, and its specific implementation steps are as follows:

[0036] The preliminary extraction of feature points includes two parts: one is the construction of scale pyramids, and the other is feature point extraction.

[0037] Since the Gaussian function is the only function that implements scale transformation, the Gaussian function is used to act on the input image to establish a scale pyramid.

[0038] If the input image is I(x,y), G(x,y,σ) is the Gaussian kernel function, where σ is the scale. Use G(x,y,σ) to perform convolution operation on I(x,y), and by changing the scale σ, the multi-scale space of the image is obtained. which is

[0039] L(x,y,σ)=G(x,y,σ)*I(x,y)

[0040] among them, G ( x , y , σ ) = 1 2 π δ 2 e - ( x 2 + y 2 ) / 2 σ 2 .

[0041] In order to achieve efficient calculations, the Gauss...

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Abstract

The invention discloses an SIFT (Scale Invariant Feature Transform) image matching method based on a module value difference mirror image invariant property, which mainly solves the problems that an image matching method is higher in timeliness requirement, and a matching error appears due to the fact that a target is subjected to mirror image turning during a movement course in the existing tracking and recognition technology. As for situations that mirror image matching is weak and the timeliness is poor in the existing method, the method provides an efficient mirror image transformation processing direction, so that mirror image transformation is overcome and an effect of dimensionality reduction is achieved. The method comprises the steps that image information is input; a feature point is extracted; the gradient strength and a direction of the feature point are computed; a principal direction is determined; and coordinates of the feature point are rotated to the principal direction; a 16*16 neighborhood pixel is divided into 16 seed points; every two axisymmetric seed points are subtracted and subjected to modulus taking; eight seed points are obtained; each seed point is drawn into a four-direction histogram; and a 8*4=32 dimensional descriptor is formed finally. The mirror image transformation problem of the matching method is solved, and the original 128-dimensional vector descriptor is reduced to be 32-dimensional, so that the timeliness of the method is improved greatly.

Description

technical field [0001] The invention relates to an image matching method in the field of computer vision, belonging to the field of image information processing. Background technique [0002] Computer vision is a simulation of biological vision using computers and related equipment, and its main task is to process the collected pictures or videos. Computer vision is a challenging and important area of ​​research in both engineering and science. [0003] Image matching is a fundamental technique in computer vision. Image matching, that is, the process of identifying the same point between two or more images through a certain matching method, can be mainly divided into grayscale-based matching and feature-based matching. Among them, SIFT (Scale Invariant Feature Transform), that is, scale invariant feature transformation, is currently the most widely used matching method in this field. [0004] SIFT matching looks for extreme points in the spatial scale, and extracts its po...

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Application Information

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IPC IPC(8): G06K9/46G06T7/00
Inventor 黄治同李嫣纪越峰
Owner BEIJING UNIV OF POSTS & TELECOMM
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