Background clutter measurement method based on multi-directional difference hash algorithm

A background clutter and measurement method technology, applied in the field of computer vision, can solve the problems of inaccurate clutter measurement of complex clutter images, and achieve the effects of high prediction accuracy and unique calculation results

Active Publication Date: 2021-03-05
北京博睿维讯科技有限公司 +1
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of inaccurate measurement of complex clutter image clutter existing in the prior art. The present invention provides a background clutter measurement method based on the multi-directional difference Hash algorithm. The difference Hash algorithm is used to calculate the similarity between the target part and the background part in the image, so as to calculate the image background clutter degree of the entire image. The whole process does not involve the problem of threshold selection, and can accurately describe the background of complex clutter images Noise level

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  • Background clutter measurement method based on multi-directional difference hash algorithm
  • Background clutter measurement method based on multi-directional difference hash algorithm
  • Background clutter measurement method based on multi-directional difference hash algorithm

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[0032] In order to better illustrate the purpose and advantages of the present invention, the content of the invention will be further described below with reference to the accompanying drawings and examples.

[0033] In this example, as image 3 Taking the fifth image in the Search_2 database shown as an example, a background clutter measurement method based on a multi-directional difference Hash algorithm is disclosed, which specifically includes the following steps:

[0034] Step 1: Select the target part from the input image, such as image 3 As shown in a, the response scale of the target image (332×199) is determined; then the target image part in the input image is processed into black as the background image and divided into 380 small units, the scale of each unit in the horizontal and vertical directions It is 2 times the response scale of the target image, that is, 664×398. Denote the target image as T and the background cell as B j , where j=1,2,...,380. In the pr...

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Abstract

The invention relates to a method for measuring background clutter, in particular to a method for measuring background clutter based on a multi-directional difference Hash algorithm, which belongs to one of the key technologies in the field of computer vision. The present invention proposes a background clutter measurement method based on the multi-directional difference Hash algorithm, which uses the multi-directional difference Hash algorithm to characterize the similarity between the target image and the background image, and then obtains the image based on the multi-directional difference Hash algorithm of the entire image Background clutter scale, there is no threshold selection problem in the whole process, and the calculation result is unique. At the same time, a background clutter measurement method based on the multi-directional difference Hash algorithm disclosed by the present invention is used to conduct experiments on the Search_2 database, and the results show that the disclosed method Prediction accuracy with high target acquisition performance.

Description

technical field [0001] The invention relates to a background clutter measurement method, in particular to a background clutter measurement method based on a multi-directional difference Hash algorithm, which belongs to one of the key technologies in the field of computer vision. Background technique [0002] Image background clutter seriously affects the target acquisition performance, and the quantification of image background clutter plays an important role in the target acquisition performance. First, it can be used to predict target acquisition performance, such as acquisition probability, false positive probability, and search time. Second, it can also be used to develop correction models for existing electro-optical imaging system performance prediction models. [0003] How to improve the prediction accuracy of the target acquisition performance model can be specifically attributed to: accurately and effectively quantitatively characterize image clutter. The existing...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06K9/62
CPCG06T7/0002G06T2207/30168G06T2207/20021G06F18/22
Inventor 周肃宋勇赵宇飞张大勇王稳
Owner 北京博睿维讯科技有限公司
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