Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Foreign object detection method for millimeter-wave images based on block-mixed Gaussian low-rank matrix factorization

A low-rank matrix, mixed Gaussian technology, applied in the field of detection of hidden objects carried by the human body, can solve the problems of weak scattering echo, low imaging quality, low detection accuracy, etc., to improve integrity, improve detection effect, and improve detection. performance effect

Active Publication Date: 2021-01-05
XIDIAN UNIV +1
View PDF7 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to propose a millimeter-wave image foreign object detection method based on block-mixed Gaussian low-rank matrix decomposition to solve the problem of low imaging quality caused by the weak scattering echo of hidden objects in the prior art, and the problem of the gray value of hidden objects and the human body. When similar, its detection accuracy is low

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Foreign object detection method for millimeter-wave images based on block-mixed Gaussian low-rank matrix factorization
  • Foreign object detection method for millimeter-wave images based on block-mixed Gaussian low-rank matrix factorization
  • Foreign object detection method for millimeter-wave images based on block-mixed Gaussian low-rank matrix factorization

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0028] The implementation and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0029] refer to figure 1 , the implementation steps of the present invention are as follows:

[0030] Step 1. De-dry the human body image and divide it into six regions according to the proportion of body parts.

[0031] (1a) Perform binary segmentation on the original mmWave human body image:

[0032] Commonly used threshold-based segmentation methods include the peak-to-valley method of the gray histogram, the minimum error method, the maximum inter-class variance method, and the fixed threshold method. In this example, but not limited to the fixed threshold method, the original image is first divided by 255 , normalized to 0-1, and then select a threshold value of 0.15, and compare the normalized image with the threshold value: set the pixel points greater than the threshold value to 1, set the pixel points smaller than ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a method for detecting a hidden object in a millimeter-wave human body image based on block mixture Gaussian low-rank matrix decomposition.The method mainly solves the problemsof low imaging quality caused by weak scatter echo of the hidden object and low detection accuracy of a gray scale value of the hidden object and a human body similarity in the prior art. An implementing scheme of the method comprises the following steps of 1, removingabnormal points in an imaging region background in an original millimeter-wave human body image, and dividing the human body imageinto six parts according to proportions of human body parts; 2, decomposing all regions of thehuman body based on a block mixture Gaussian low-rank matrix decomposition algorithm to obtain a low-rankpart and a sparse part; and 3, binaryzing the sparse part by using a typology method, and removing small noise points to obtain a final detection result graph. The method increases the detection rateof various complicated small targets in the millimeter-wave human body image without a large number of training samples, so that the detected hidden object is more complete; and the method can be used for detecting hidden objects carried by people in public places such as an airport and a bus station.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a method for detecting concealed objects carried by a human body, which can be used for detecting concealed objects carried by a human body in public places such as airports and stations. Background technique [0002] In recent years, millimeter wave technology has been used at home and abroad to detect concealed objects carried by the human body in public places such as airports and stations, such as water bottles, lighters, pistols, flashlights, detonators, knives, explosives, drugs, etc. Since traditional metal detectors are difficult to detect non-metallic and small objects, X-rays are not only harmful to the human body, but also cannot detect foreign objects carried by people with loose clothes. The millimeter-wave radar can image at close range, not only can penetrate clothing, but also has no radiation damage to the human body. It is currently a popular imag...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G01V8/00
CPCG01V8/005
Inventor 王新林刘振赵英海毛莎莎焦昶哲缑水平
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products