CN105469428B - A kind of detection method of small target based on morphologic filtering and SVD - Google Patents

A kind of detection method of small target based on morphologic filtering and SVD Download PDF

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CN105469428B
CN105469428B CN201510837688.3A CN201510837688A CN105469428B CN 105469428 B CN105469428 B CN 105469428B CN 201510837688 A CN201510837688 A CN 201510837688A CN 105469428 B CN105469428 B CN 105469428B
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CN105469428A (en
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王敏
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Hohai University HHU
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G06T2207/20036—Morphological image processing
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract

The invention discloses a kind of detection method of small target based on morphologic filtering and SVD, step 1：Background suppression, noise remove, image sequence after being pre-processed are carried out by morphologic filtering target enhancing algorithm；Step 2：Read in NmaxImage sequence obtained by width, carries out frame number estimation, obtains frame number N to be treated；Step 3：The image that N+1 width image forms is merged into 2-D data, its autocorrelation matrix is sought and SVD is carried out to its autocorrelation matrix；Step 4：Suitable feature vector reconstructed image sequence is selected, obtains new characteristic image sequence；Step 5：To reconstructed image sequence into row threshold division, from background in isolated original image Weak target position；Step 6：Each image in sequence is modified respectively；Step 7：N is replaced into NmaxAfterwards, repeat steps 2 through 7.The method of morphologic filtering and singular value decomposition is effectively combined by the present invention is detected the Weak target in video, and the calculating time is short, and detection efficiency is high, and accuracy and robustness are all relatively good.

Description

A kind of detection method of small target based on morphologic filtering and SVD
Technical field
This method belongs to video analysis field, and in particular to a kind of Dim targets detection based on morphologic filtering and SVD Method.
Background technology
Status of the detection of Weak target in modern war is self-evident, becomes satellite remote sensing, high energy at present The core technology of the realm informations such as physics, low latitude early warning and precise guidance processing.Since the pixel number of Weak target is seldom, Lack the structural information of target, it is seldom for splitting the information utilized with detection algorithm.And sensor receive target strength compared with Weak, noise and background clutter interference are stronger, make the signal-to-noise ratio of image reduce, so we should make good use of target in sequence image Continuity and systematicness detect target.All the time, how Weak target inter-frame information is preferably utilized, improves detection Reliability and efficiency, are the emphasis of Dim targets detection.
Suppress mainly to concentrate on Dynamic Programming and shape with Dim targets detection problem, a few thing of early stage for background State estimation technique increases the detectability of target, still, poor performance may be presented in the case of low signal-to-noise ratio.Mesh Before, someone will be used for Dim targets detection the methods of morphologic filtering, genetic algorithm, neural network algorithm, wavelet transformation. But under complex background, target point is easily flooded by noise, realize that the reliability detection of target and identification difficulty are larger.This Outside, it is difficult to meet good detection performance under conditions of data throughout is big, requirement of real-time is high.
The content of the invention
Goal of the invention：In view of the problems of the existing technology, the present invention provides one kind calculate the time it is short, detection accurately Detection method of small target based on morphologic filtering and singular value decomposition (hereinafter referred SVD).
The content of the invention：The present invention provides a kind of detection method of small target based on morphologic filtering and SVD, including with Lower step：
Step 1：Video sequence to be detected is inputted, background suppression is carried out by morphologic filtering target enhancing algorithm, is made an uproar Sound removes, image sequence after being pre-processed；
Step 2：From pretreated image sequence, N is read inmaxThe image sequence of width image composition, carries out frame number and estimates Meter, obtains frame number N to be treated；
Step 3：Read in N+1 width images, including the latter picture of N two field pictures to be treated and N two field pictures, and by N+ The image of 1 width image composition is merged into 2-D data, seeks its autocorrelation matrix and carries out SVD to its autocorrelation matrix；
Step 4：Intermediate characteristic value character pair vector reconstruction image sequence is selected, obtains reconstructed image sequence；
Step 5：To reconstructed image sequence into row threshold division, from background in isolated original image Weak target position Put；
Step 6：Location between frames amendment and frame are carried out respectively to each image in the reconstructed image sequence that is obtained in step 4 Interior position correction；
Step 7：N is replaced into NmaxAfterwards, repeat steps 2 through 7, until NmaxLast width in the image sequence of width image composition Result is exported after the completion of image procossing.
Further, morphologic filtering target enhancing algorithm is used as structural element using circle in the step 1.This is for extensive Multiple image polluted by noise can produce preferable filter effect.More preferable filter effect can so be obtained.
Further, morphologic filtering target enhancing algorithm is utilized in the step 1 first to each frame in video to be detected Image carries out closed operation, then to have inside the image that can insert between sand holes noise or will not be formed the region of degeneration rectangle into Row opening operation.
Further, the method for frame number estimation is in the step 2：Read in NmaxThe image sequence of width image composition, to reading in All images carry out two-by-two difference operation obtain Nmax- 1 frame image sequence, then to NmaxEach width in -1 frame image sequence The summation of image total pixel, then with the N tried to achievemax- 1 pixel and respectively divided by correspondence image width and height obtain Nmax- 1 assessment Value, by Nmax- 1 assessed value is averaging to NmaxTwo field picture changes the value X of severe degree, finally by image change severe degree X Substitute into formulaIn, it is to be treated to obtain this Number of image frames N, wherein, NmaxRepresent maximum processing frame number, NminRepresent minimum treat frame number.
Further, the maximum processing frame number Nmax25 are set to, minimum treat frame number NminIt is set to 5.
Further, in the step 5 using maximum entropy method to image into row threshold division.
Further, modification method is in the step 6：Gray scale maximum is found in current search window, then by ash Center iterative search of the coordinate of maximum as next search window is spent, untill final search window is constant, this When search window center position be target location.
Beneficial effect：Compared with prior art, the present invention effectively ties the method for morphologic filtering and singular value decomposition Conjunction is detected the Weak target in video, and not only the calculating time is short, and detection efficiency is high, and accuracy and robustness all compare Preferably.
Brief description of the drawings
Fig. 1 is the work flow diagram of the present invention.
Fig. 2 (a) and Fig. 2 (b) is respectively to detect the 20th frame and the 50th two field picture in video；
Fig. 3 (a) and Fig. 3 (b) is respectively to the 20th frame and the 50th two field picture in detection video based on morphologic filtering method The result of middle Dim targets detection；
Fig. 4 (a) and Fig. 4 (b) is respectively that Weak target in the 20th frame in detection video and the 50th two field picture is examined based on SVD The result of survey；
Fig. 5 (a) and Fig. 5 (b) is respectively that the present invention examines Weak target in the 20th frame in detection video and the 50th two field picture The result of survey.
Embodiment
Below in conjunction with the accompanying drawings, the present invention is described in detail.
As shown in Figure 1, the present invention provides a kind of detection method of small target based on morphologic filtering and SVD, including Following steps：
Step 1：Video sequence to be detected is inputted, background suppression is carried out by morphologic filtering target enhancing algorithm, is made an uproar Sound removes, image sequence after being pre-processed；
In view of the detection of Weak target, it can more or less run into noise image and overlap to form knot with non-noise image The radius of group or some noise particles has exceeded the radius of non-noise particle, then the circle of correct radial may be selected in such case For shape as structural element, this can produce preferable filter effect for recovering image polluted by noise, be because：(1) circular circle Change acts on the effect that can play low-pass filtering；(2) rotating influence may not necessarily be considered using circular filter.
When determining the radius of circular configuration element, optimization method can be used, image and noise are considered as random process, led to Statistical analysis is crossed, to carrying out quantitative analysis by the Granule Images of noise pollution, statistical distribution parameter is asked for, obtains probability of occurrence most Big noise particles and not by the radius of noise pollution particle, it is not structural element half by the radius of noise pollution particle to choose Footpath, obtains optimum results.
In addition, may be both there are pepper shape noise (poor noise) in the sequence of Weak target, there is also sand holes noise (and to make an uproar Sound), it is so simple using opening operation or closed operation effect all without good, for compatible smooth noise and retain image border And other significant features, the present invention first carry out closed operation to each two field picture in video to be detected, then to can be into The region opening operation of row opening operation, can obtain more preferable filter effect, wherein the region that can carry out opening operation is to have and can fill out Enter inside the image between sand holes noise, the region of serious degeneration rectangle will not be formed.According to formula and definition, we are not rare Know, which has good translation invariance, incremental, idempotence and duality.These properties cause such a Wave filter practicality is closed-opened to open-close and feasibility is stronger, and the effect of acquirement is also more preferably.
Step 2：From pretreated image sequence, N is read inmaxThe image sequence of width image composition, carries out frame number and estimates Meter, obtains frame number N to be treated；
In Weak target sequence, different target needs the frame number disposably handled and differs, so needing to pass through certainly Adaptation is determined to obtain frame number N to be treated.When frame number is selected, first have to consider is image change speed, i.e., Less frame number is selected to handle when changing fast, on the contrary the more frame number of selection.So it needs to be determined that maximum when frame number is estimated Manage frame number NmaxWith minimum treat frame number Nmin, wherein, maximum processing frame number NmaxAs read in from pretreated image sequence Picture number Nmax.If NmaxVery big then algorithm does not have real-time, equally, if Nmin≤ 0, then carrying out singular value decomposition to it is also Not in all senses.In view of real-time and feasibility, maximum processing frame number Nmax25 are set to, minimum treat frame number NminIt is set to 5, calculate N with cosine functionmaxAnd NminBetween frame number estimation, independent variable is image change severe degree X.
Handled, handled as X ＞ 1.5 using maximum frame number, as 0.5 ＜ X ＜ 1.5 using minimum frame number as X ＜ 0.5 Handled using cosine function transient mode.Using trigonometric function knowledge, obtain image change severe degree X and handle frame number N's Formula is as follows：
Wherein, specific assessment algorithm is：In Nmax=25 and NminOn the premise of=5, the image of maximum frame number is read in first, Difference operation obtains N two-by-two for image progress to these framesmax- 1 frame image sequence, then to NmaxIt is every in -1 frame image sequence The summation of piece image total pixel, then with the N tried to achievemax- 1 pixel and respectively divided by correspondence image width and height obtain Nmax- 1 Assessed value, by Nmax- 1 assessed value, which is averaging, can be obtained by NmaxTwo field picture changes the value X of severe degree, finally substitutes into X Formula (1) obtains this number of image frames N to be treated.
Step 3：Read in N+1 width images, including the latter picture of N two field pictures to be treated and N two field pictures, and by N+ The image of 1 width image composition is merged into 2-D data, seeks its autocorrelation matrix and carries out SVD to its autocorrelation matrix；
The image sequence A of N+1 width image composition is the three-dimensional data set of one { x, y, z }, and wherein x represents the row of image Number, y represent the columns of image, and z represents the frame number of sequence.If matrix Bq×zFor the two-dimensional development of A, wherein q=x × y, then B is x The matrix of × y rows z row.Under normal circumstances, the value of x × y is bigger, so matrix Bq×zData volume be not suitable for directly using To carry out singular value decomposition.If Bq×zAutocorrelation matrix be Cq×z, then Cq×z=B 'q×zBq×z, wherein, B 'q×zIt is Bq×zTurn Put, so Matrix Cq×zIt is the square formation that row and column is equal to z.At this time, to Matrix Cq×zCarry out SVD.In order to realize to Weak target Detection, image sequence to every piece image and its before does above-mentioned singular value decomposition.
Step 4：Intermediate characteristic value character pair vector reconstruction image sequence is selected, obtains reconstructed image sequence；
According to singular value decomposition as a result, selecting suitable feature vector to carry out corresponding image sequence reconstruct, after reconstruct Image sequenceBe q rows i row matrix, Vz×iIt is that intermediate characteristic value corresponds to spy Sign vector, can incite somebody to actionRemap as image arrayR represents the sum of the singular value obtained in step 3, and i represents strange The label of different value, selects different i to obtain different reconstructed images.Wherein, background image sequence accounts for whole image to be detected The overwhelming majority of sequence, the image of the obtained corresponding feature vector reconstruct of main characteristic value will reflect the information of background. Noise image sequence is the random process of one group of two dimension, the image of the corresponding feature vector reconstruct of obtained minimum characteristic value because And it can reflect noise information.Target image sequence is continuous motion change in whole image sequence, obtained intermediate features The image of value reconstruct will be strengthened mobile Weak target.
Step 5：To the reconstructed image sequence that is obtained in step 4 into row threshold division, the isolated original image from background The position of middle Weak target；
Wherein, use maximum entropy method to image into row threshold division in step 5, mainly using in reconstructed image sequence The intensity profile density function of each image defines the comentropy of image, and difference is proposed according to the difference of hypothesis or the different of visual angle Entropy criterion, finally by optimize the criterion obtain threshold value.The comentropy of image has reacted the overall looks of image.If in image It is comprising target, then maximum in target and the divisible place's information content (i.e. entropy) of background.Contain the multi-grey image of multiple target in a width In, a gray scale is certainly existed, using this gray scale as threshold value, image can be made to obtain optimal binarization segmentation.So as to fulfill mesh The separation of mark and background, obtains its approximate location.
Step 6：Location between frames amendment and frame are carried out respectively to each image in the reconstructed image sequence that is obtained in step 4 Interior position correction, to achieve the purpose that to detect Weak target；
In order to improve the accuracy of target location estimation, a two field picture and its need during image sequence to use this under treatment Secondary testing result is modified target location.
Specific method is the difference of different twice testing results before and after, if in certain threshold value, updates mesh Test position is marked, otherwise it is assumed that this processing is exceptional value, testing result is given up.Threshold value herein passes through maximum entropy method Obtain.
Singular value decomposition is image sequence, therefore not only includes the data of present image in whole data space, together When also include before N width view data.The target location obtained after reconstruct can reflect that current goal is gone over to a certain extent Position, rather than in present image target position, so also need in frame carry out target location amendment.
The basic principle of correction algorithm is：Gray scale maximum is found in current search window, then coordinate will be made herein For the center iterative search of next search window, untill final search window is constant, at this moment search window center Position is target location.
Combining interframe after SVD processing can remain the Dim targets detection rate of image sequence with the joint-detection in frame 90% or so, this largely meets the real-time and feasibility requirement of image procossing.
Step 7：N is replaced into NmaxAfterwards, repeat steps 2 through 7, until NmaxLast width in the image sequence of width image composition Result is exported after the completion of image procossing.
As shown in Fig. 2 (a) and Fig. 2 (b), to the infrared weak of actual photographed under vs2010+opencv2.4.3 running environment Small object sequence image is tested, and totally 100 frame, image size are 200 × 256 pixels to sequence image, and target sizes are about 2 × 2 pixels, signal-to-noise ratio are contrast 7% within 2, and background be sky and cloud layer the 20th two field pictures of selection (for Fig. 2 (a)) and the 50 frames (Fig. 2 (b)) are based respectively on morphologic filtering, are detected based on SVD and method provided by the invention.
As shown in Fig. 3 (a)~Fig. 5 (b), morphologic filtering method processing speed is very fast, but can not accurately reach weak The purpose of small target deteection, also detects decoy；SVD can preferably realize prominent Weak target, and can keep higher inspection Survey rate, but the tracking to target location is not too preferable, and in addition detection calculations amount is big, and speed is slower.And side proposed by the present invention Not only verification and measurement ratio is high for method, but also the speed handled is fast, and robustness and real-time are all relatively good.

Claims (6)

  1. A kind of 1. detection method of small target based on morphologic filtering and SVD, it is characterised in that：Comprise the following steps：
    Step 1：Video sequence to be detected is inputted, background suppression is carried out by morphologic filtering target enhancing algorithm, noise is gone Remove, image sequence after being pre-processed；
    Step 2：From pretreated image sequence, N is read inmaxThe image sequence of width image composition, carries out frame number estimation, obtains To frame number N to be treated；
    Step 3：Read in N+1 width images, including the latter picture of N two field pictures to be treated and N two field pictures, and by N+1 width The image of image composition is merged into 2-D data, seeks its autocorrelation matrix and carries out SVD to its autocorrelation matrix；
    Step 4：Intermediate characteristic value character pair vector reconstruction image sequence is selected, obtains reconstructed image sequence；
    Step 5：To reconstructed image sequence into row threshold division, from background in isolated original image Weak target position；
    Step 6：Location between frames amendment and position in frame are carried out respectively to each image in the reconstructed image sequence that is obtained in step 4 Put amendment；
    Step 7：N is replaced into NmaxAfterwards, repeat steps 2 through 7, until NmaxLast piece image in the image sequence of width image composition Result is exported after the completion of processing；
    The method of frame number estimation is in the step 2：Read in NmaxThe image sequence of width image composition, to all images of reading Carry out difference operation two-by-two and obtain Nmax- 1 frame image sequence, then to NmaxThe total pixel of every piece image in -1 frame image sequence Summation, then with the N tried to achievemax- 1 pixel and respectively divided by correspondence image width and height obtain Nmax- 1 assessed value, by Nmax-1 A assessed value is averaging to NmaxTwo field picture changes the value X of severe degree, and image change severe degree X finally is substituted into formulaIn, obtain this number of image frames to be treated N, wherein, NmaxRepresent maximum processing frame number, NminRepresent minimum treat frame number.
  2. 2. the detection method of small target according to claim 1 based on morphologic filtering and SVD, it is characterised in that：Institute State morphologic filtering target enhancing algorithm in step 1 and structural element is used as using circle.
  3. 3. the detection method of small target according to claim 1 based on morphologic filtering and SVD, it is characterised in that：Institute State in step 1 and closed operation first is carried out to each two field picture in video to be detected using morphologic filtering target enhancing algorithm, so Opening operation is carried out to the region that has inside the image that can be inserted between sand holes noise or will not be formed degeneration rectangle afterwards.
  4. 4. the detection method of small target according to claim 1 based on morphologic filtering and SVD, it is characterised in that：Institute State maximum processing frame number Nmax25 are set to, minimum treat frame number NminIt is set to 5.
  5. 5. the detection method of small target according to claim 1 based on morphologic filtering and SVD, it is characterised in that：Institute State in step 5 using maximum entropy method to image into row threshold division.
  6. 6. the detection method of small target according to claim 1 based on morphologic filtering and SVD, it is characterised in that：Institute Stating modification method in step 6 is：In current search window find gray scale maximum, then using the coordinate of gray scale maximum as The center iterative search of next search window, untill final search window is constant, the at this moment position at search window center Put as target location.
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Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106570889A (en) * 2016-11-10 2017-04-19 河海大学 Detecting method for weak target in infrared video
CN108010064A (en) * 2017-11-03 2018-05-08 河海大学 Motor cell tracking based on active profile and Kalman filter
CN111047624A (en) * 2019-12-27 2020-04-21 成都英飞睿技术有限公司 Image dim target detection method, device, equipment and storage medium
CN112381738B (en) * 2020-11-23 2023-12-05 深圳企业云科技股份有限公司 Perspective image self-adaptive correction algorithm based on correlation operation
CN112686890B (en) * 2021-02-09 2024-02-20 浙江师范大学 Artificial board surface defect detection method based on singular value decomposition
CN113971815B (en) * 2021-10-28 2024-07-02 西安电子科技大学 Few-sample target detection method based on singular value decomposition feature enhancement

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750533A (en) * 2012-07-05 2012-10-24 重庆大学 Infrared small and weak target detection method based on morphological component sparse representation
CN103440502A (en) * 2013-09-06 2013-12-11 重庆大学 Infrared small-target detection method based on mixing Gauss and sparse representation

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102750533A (en) * 2012-07-05 2012-10-24 重庆大学 Infrared small and weak target detection method based on morphological component sparse representation
CN103440502A (en) * 2013-09-06 2013-12-11 重庆大学 Infrared small-target detection method based on mixing Gauss and sparse representation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Infrared dim small target detection algorithm based on NSCT and SVD;Ying Zhao et al;《AOPC 2015: Image Processing and Analysis》;20151014;第9675卷;全文 *
基于SVD背景抑制和粒子滤波的弱小目标检测;崔丽洁 等;《计算机应用研究》;20110430;第28卷(第4期);全文 *
基于背景抑制和灰度形态学的弱小目标检测;董宇星 等;《图像图形技术研究与应用》;20101231;全文 *

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