CN104504726A - Method and device for detecting target from image - Google Patents

Method and device for detecting target from image Download PDF

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CN104504726A
CN104504726A CN201510024884.9A CN201510024884A CN104504726A CN 104504726 A CN104504726 A CN 104504726A CN 201510024884 A CN201510024884 A CN 201510024884A CN 104504726 A CN104504726 A CN 104504726A
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pixel
matrix
background
inverse matrix
inverse
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CN104504726B (en
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张兵
杨斌
高连如
孙旭
李聪
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Institute of Remote Sensing and Digital Earth of CAS
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Institute of Remote Sensing and Digital Earth of CAS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing

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Abstract

The invention provides a method and device for detecting a target from an image. An inverse matrix of a background autocorrelation matrix of an nth image in an image to be detected is calculated by utilizing an inverse matrix of a background autocorrelation matrix of a (n-1)th image in the image to be detected; the two background matrixes have the very strong similarity so that a traditional matrix inversion manner does not need to be used for obtaining the inverse matrix of the background autocorrelation matrix of the nth image, and the inverse matrix of the background autocorrelation matrix of the nth image can be obtained by carrying out simple operation on the inverse matrix of the background autocorrelation matrix of the (n-1)th image, and furthermore, the calculation amount of calculating the inverse matrix can be reduced.

Description

A kind of method of the detection of a target from image and device
Technical field
The application relates to electronic information field, particularly relates to a kind of method and device of the detection of a target from image.
Background technology
From image, detect interested target is an important technology in image processing field.Such as, from high-spectrum remote sensing, interested atural object is detected.From image, the method for the detection of a target comprises the mode of overall situation detection and partial detection.
Wherein, after overall situation detection needs to receive image, carry out background estimating again, therefore, be unfavorable for realizing the real-time detection to the target in image.Partial detection then can utilize that pixel to be measured is a certain closes on scope (such as, the n neighborhood of pixel to be measured) in pixel, the background of pixel to be measured is estimated, therefore, all receive can carry out target detection without the need to Waiting Graph picture, be conducive to realizing real-time detection.
But, just because of partial detection needs the estimation different pixels to be measured all being carried out to background, namely matrix inversion operation is carried out to the result of Background statistics different each time, so partial detection exists the large problem of operand.
Summary of the invention
This application provides a kind of method and device of the detection of a target from image, object is the problem that the operand of solution partial detection existence is large.
To achieve these goals, this application provides following technical scheme:
A method for the detection of a target from image, comprising:
After receiving the n-th pixel in image to be detected, utilize the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, calculate the inverse matrix of the background autocorrelation matrix of described n-th pixel, the background matrix of described (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before thereof, the background matrix of described n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before thereof, wherein, n and K is the integer of the pixel total quantity being less than described image to be detected, and n>K>1,
According to the inverse matrix of the background autocorrelation matrix of described n-th pixel, calculate the result of detection of described n-th pixel.
Alternatively, the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel utilized in described image to be detected, calculates the inverse matrix of the background autocorrelation matrix of described n-th pixel, comprising:
The inverse matrix of the background autocorrelation matrix of foundation matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculate the inverse matrix of the first background autocorrelation matrix, described first background matrix is the matrix formed after increasing the value of described n-th pixel in the background matrix of described (n-1)th pixel;
According to the inverse matrix of described Sherman-Morrison principle and described first background autocorrelation matrix, calculate the inverse matrix of the second background autocorrelation matrix, described second background matrix is the matrix formed after removing the value of K pixel before described n-th pixel in described first background matrix.
Alternatively, the inverse matrix of the background autocorrelation matrix of described foundation Sherman-Morrison principle and described (n-1)th pixel, calculates the inverse matrix of the first background autocorrelation matrix, comprising:
Utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel.
Alternatively, the described inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculates the inverse matrix of the second background autocorrelation matrix, comprising:
Utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel.
Alternatively, described after receiving the n-th pixel in image to be detected, before utilizing the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, also comprise:
Obtain the inverse matrix of the background autocorrelation matrix of front K pixel in described image to be detected;
The detailed process of the inverse matrix of the background autocorrelation matrix of front K pixel in the described image to be detected of described acquisition comprises:
Successively to a described front K pixel, calculate wherein, for first pixel, S 1 -1=I.
Alternatively, the inverse matrix of the described background autocorrelation matrix according to described n-th pixel, calculates the result of detection of described n-th pixel, comprising:
Utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of described n-th pixel, d is the spectrum of target.
A device for the detection of a target from image, comprising:
First computing module, for after receiving the n-th pixel in image to be detected, utilize the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, calculate the inverse matrix of the background autocorrelation matrix of described n-th pixel, the background matrix of described (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before thereof, the background matrix of described n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before thereof, wherein, n and K is the integer of the pixel total quantity being less than described image to be detected, and n>K>1,
Target detection module, for the inverse matrix of the background autocorrelation matrix according to described n-th pixel, calculates the result of detection of described n-th pixel.
Alternatively, described first computing module comprises:
First computing unit, for the inverse matrix of the background autocorrelation matrix according to matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculate the inverse matrix of the first background autocorrelation matrix, described first background matrix is the matrix formed after increasing the value of described n-th pixel in the background matrix of described (n-1)th pixel;
Second computing unit, for the inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculate the inverse matrix of the second background autocorrelation matrix, described second background matrix is the matrix formed after removing the value of K pixel before described n-th pixel in described first background matrix.
Alternatively, described first computing unit is used for the inverse matrix of the background autocorrelation matrix of foundation matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculates the inverse matrix of the first background autocorrelation matrix, comprising:
Described first computing unit specifically for, utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel.
Alternatively, described second computing unit is used for the inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculates the inverse matrix of the second background autocorrelation matrix, comprising:
Described second computing unit specifically for, utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel.
Alternatively, also comprise:
Second computing module, for at described first computing module after receiving the n-th pixel in image to be detected, before utilizing the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, obtain the inverse matrix of the background autocorrelation matrix of front K pixel in described image to be detected, the detailed process of the inverse matrix of the background autocorrelation matrix of front K pixel in the described image to be detected of described acquisition comprises:
Successively to a described front K pixel, calculate wherein, for first pixel, S -1the scope of=β I, β is the biquadratic of the zero degree side to 10 of 10.
Alternatively, described target detection module is used for the inverse matrix according to the background autocorrelation matrix of described n-th pixel, calculates the result of detection of described n-th pixel, comprising:
Described target detection module specifically for, utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of described n-th pixel, d is the spectrum of target.
The method of the detection of a target from image described in the application and device, utilize the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in image to be detected, calculate the inverse matrix of the background autocorrelation matrix of the n-th pixel in image to be detected, the inverse matrix recycling the background autocorrelation matrix of the n-th pixel calculates the result of detection of described n-th pixel, because the background matrix of (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before, and, the background matrix of the n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before, visible, the background matrix of the n-th pixel is compared with the background matrix of (n-1)th pixel, eliminate the value of a pixel of foremost in the background matrix of (n-1)th pixel, and add the value of the n-th pixel, therefore, two background matrixs have very strong similarity, so, between two background matrixs similarity this condition strong restriction under, can without the need to the inverse matrix adopting the mode of traditional matrix inversion to obtain the background autocorrelation matrix of the n-th pixel, and only can carry out by the inverse matrix of the background autocorrelation matrix to (n-1)th pixel the inverse matrix that simple operation can obtain the background autocorrelation matrix of the n-th pixel, therefore, the calculated amount calculating inverse matrix can be greatly reduced.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the application, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
The process flow diagram of Fig. 1 a kind of method of the detection of a target from image disclosed in the embodiment of the present application;
The schematic diagram of Fig. 2 background matrix of the n-th pixel and (n-1)th pixel disclosed in the embodiment of the present application;
Fig. 3 is the process flow diagram of the method method of the embodiment of the present application another detection of a target from image disclosed;
Fig. 4 is the process flow diagram of the method method of the embodiment of the present application another detection of a target from image disclosed;
Fig. 5 is high-spectrum remote sensing to be detected;
Fig. 6 is that disclosed in use the embodiment of the present application, from image, the method for the detection of a target carries out the schematic diagram of the result of target detection;
Fig. 7 is the disclosed structural representation of planting the device of the detection of a target from image of the embodiment of the present application.
Embodiment
The embodiment of the present application discloses method and the device of the detection of a target from image, can be applied in the target detection of high-spectrum remote sensing.
Below in conjunction with the accompanying drawing in the embodiment of the present application, be clearly and completely described the technical scheme in the embodiment of the present application, obviously, described embodiment is only some embodiments of the present application, instead of whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of the application's protection.
A kind of method of the detection of a target from image disclosed in the embodiment of the present application, as shown in Figure 1, comprises the following steps:
S101: after receiving the n-th pixel in image to be detected, utilizes the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, calculates the inverse matrix of the background autocorrelation matrix of described n-th pixel;
Wherein, the pass of background matrix and background autocorrelation matrix is: suppose that background matrix is X={x 1, x 2... x n, i.e. the two-dimensional matrix of capable, the N row of the L of N number of pixel composition, then the autocorrelation matrix of this background is XX t, also can be expressed as it is that a L ties up square formation.
In the present embodiment, the background matrix of described (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before thereof, and the background matrix of described n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before thereof, as shown in Figure 2.Wherein, n and K is the integer of the pixel total quantity being less than described image to be detected, and n>K>1.
As can be seen from Figure 2, the background matrix of the n-th pixel, compared with the background matrix of (n-1)th pixel, has deducted the value of a pixel of foremost in the background matrix of (n-1)th pixel, and has added the value of the n-th pixel.
S102: according to the inverse matrix of the background autocorrelation matrix of described n-th pixel, calculates the result of detection of described n-th pixel.
Whether, such as high-spectrum remote sensing, target can be often interested atural object, and in the present embodiment, the result of detection of the n-th pixel is specifically as follows, indicate the n-th pixel to be the result of interested atural object.
In existing target detection technology, in order to improve the real-time of target detection, often adopt partial detection algorithm detection of a target from image, and partial detection algorithm needs the inverse matrix all calculating its background autocorrelation matrix for each pixel, therefore, compared with Global Algorithm, add calculated amount undoubtedly, especially for high-spectrum remote sensing, the calculated amount of increase is huge.
And the method described in the present embodiment, also a kind of localized target probe algorithm can be regarded as, therefore, the real-time of target detection can be ensured, what is more important, the method is with value (such as spectrum) component as a setting of pixel to be detected and the pixel of K-1 before in the way selection image of flowing water, participate in the calculating of the inverse matrix of background autocorrelation matrix, because the process receiving image to be detected is the process of the pixel received successively in image to be detected, so, for the pixel that each receives, its background matrix only need according to the reception order of pixel, the value of a new pixel is added in the background matrix of a upper pixel, and the value deducting the pixel received at first in the background matrix of a pixel is formed, like this, both ensure that the spatial coherence of background matrix and pixel to be measured, the background matrix of the pixel be currently received is made again to be easy to obtain.The more important thing is, for the background matrix of the pixel be currently received, no longer need to invert to it by classic method, and only need the simple computation of the inverse matrix by the background autocorrelation matrix to a upper pixel, just can replace the inverse matrix of the background autocorrelation matrix of the pixel obtaining being currently received, this calculated amount that will greatly reduce inverse matrix and invert.
For high-spectrum remote sensing, method described in the present embodiment can realize real-time detection, further, calculated amount, compared with overall probe algorithm, does not increase significantly, so, on the basis ensureing real-time, hardware without the need to fairly large upgrading, thus without the need to significantly increasing the power consumption of hardware, therefore, be conducive to realizing detecting the real-time target in-orbit of high-spectrum remote sensing.
The method of the embodiment of the present application another detection of a target from image disclosed, as shown in Figure 3, comprises the following steps:
S301: after receiving the n-th pixel in image to be detected, the inverse matrix of the background autocorrelation matrix of foundation matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculates the inverse matrix of the first background autocorrelation matrix;
Wherein, described first background matrix is the matrix formed after increasing the value of described n-th pixel in the background matrix of described (n-1)th pixel.
In the present embodiment, particularly, can utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel.
S302: according to the inverse matrix of described Sherman-Morrison principle and described first background autocorrelation matrix, calculate the inverse matrix of the second background autocorrelation matrix;
Wherein, described second background matrix is the matrix formed after removing the value of K pixel before described n-th pixel in described first background matrix.It should be noted that, K pixel before the n-th pixel refers to, from the n-th pixel, be designated as 1, and each pixel counting adds 1, is counted as the pixel of K.
In the present embodiment, particularly, can utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel.
S303: utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of the n-th pixel, d is the spectrum of target.
As can be seen from the process in the present embodiment, utilize the feature receiving image to be detected by pixel, namely can from the inverse matrix of the background autocorrelation matrix of a upper pixel by Sherman-Morrison principle, through simple plus and minus calculation, the inverse matrix of the background autocorrelation matrix of the pixel be currently received can be obtained, and all carry out inversion operation without the need to the background matrix for each pixel, thus greatly can reduce operand.
It is emphasized that, in actual applications, in order to meet the needs of real-time detection target, usually need to adopt traditional partial detection algorithm, because its operand is huge, so, can only upgrade to computing equipment, adopt large-scale processor, and this can increase the power consumption of target-detection unit undoubtedly, therefore, still be unfavorable for realizing target detection in-orbit, and the method described in the present embodiment, the ingenious feature receiving image to be detected in-orbit that make use of, greatly can reduce the calculated amount of equipment, thus be conducive to the realization of target detection in-orbit.
The method of the embodiment of the present application another detection of a target from image disclosed, as shown in Figure 4, comprises the following steps:
S401: arranging initial inverse matrix is S 1- 1=I, counter n=1;
S402: the pixel x receiving high-spectrum remote sensing to be detected n;
Usually, high spectrum image P is that L is capable, the two-dimensional array of M row, and wherein, L is wave band number, and M is pixel number, therefore the spectrum column vector of the corresponding pixel of each column data.In the case, I is L rank unit matrixs.
S403: judge n>K, if so, performs S406, if not, performs S404;
In the present embodiment, K can be L square.
S404: calculate S 1 - 1 = S 1 - 1 - S 1 - 1 x n x n T S 1 - 1 1 + x n T S 1 - 1 x n ;
S405:n=n+1; Return and perform S403;
S406: utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel;
S407: utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel;
S408: utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of the n-th pixel, d is the spectrum of target;
S409: the result of detection exporting the n-th pixel;
S410：n＝n+1；
S411: judge n=M, if so, then detection process terminates, and if not, returns and performs S402.
In the present embodiment, by K pixel before image to be detected only as the statistics of initial background matrix, and do not do detection output, until receive K+1 pixel, start the output result of detection by pixel, while guarantee target detection local algorithm detection accuracy, reduce computation complexity, be easy to realize and meet the time requirement that high-spectrum remote-sensing processes in-orbit in real time.
Fig. 5 is high-spectrum remote sensing to be detected, the result of Fig. 6 for using the method described in the present embodiment to carry out target detection, and wherein, most bright spot position is the target detected.
With said method embodiment accordingly, the embodiment of the present application also discloses a kind of device of the detection of a target from image, as shown in Figure 7, comprising:
First computing module 701, for after receiving the n-th pixel in image to be detected, utilize the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, calculate the inverse matrix of the background autocorrelation matrix of described n-th pixel, the background matrix of described (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before thereof, the background matrix of described n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before thereof, wherein, n and K is the integer of the pixel total quantity being less than described image to be detected, and n>K>1,
Target detection module 702, for the inverse matrix of the background autocorrelation matrix according to described n-th pixel, calculates the result of detection of described n-th pixel.
Alternatively, in the present embodiment, can also comprise: the second computing module 703, for at described first computing module after receiving the n-th pixel in image to be detected, before utilizing the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, obtain the inverse matrix of the background autocorrelation matrix of front K pixel in described image to be detected;
Wherein, the detailed process of the inverse matrix of the background autocorrelation matrix of front K pixel in the described image to be detected of described acquisition comprises:
Successively to a described front K pixel, calculate wherein, for first pixel, S -1the scope of=β I, β is the biquadratic of the zero degree side to 10 of 10.
Further, first computing module can specifically comprise: the first computing unit, for the inverse matrix of the background autocorrelation matrix according to matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculate the inverse matrix of the first background autocorrelation matrix, described first background matrix is the matrix formed after increasing the value of described n-th pixel in the background matrix of described (n-1)th pixel, and second computing unit, for the inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculate the inverse matrix of the second background autocorrelation matrix, described second background matrix is the matrix formed after removing the value of K pixel before described n-th pixel in described first background matrix.
Wherein, the inverse matrix of the background autocorrelation matrix of the first computing unit foundation matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, the specific implementation calculating the inverse matrix of the first background autocorrelation matrix can be: utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel.
Second computing unit is according to the inverse matrix of described Sherman-Morrison principle and described first background autocorrelation matrix, and the specific implementation calculating the inverse matrix of the second background autocorrelation matrix can be: utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel.
Further, target detection module is according to the inverse matrix of the background autocorrelation matrix of described n-th pixel, and the specific implementation calculating the result of detection of described n-th pixel can be: utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of described n-th pixel, d is the spectrum of target.
Device described in the present embodiment, can reduce computation complexity while guarantee target detection local algorithm detection accuracy, is easy to realize and meets the time requirement that high-spectrum remote-sensing processes in-orbit in real time.
In actual applications, device described in the present embodiment can be arranged in FPGA, realizes above-mentioned functions by FPGA.
If the function described in the embodiment of the present application method using the form of SFU software functional unit realize and as independently production marketing or use time, can be stored in a computing equipment read/write memory medium.Based on such understanding, the part of the part that the embodiment of the present application contributes to prior art or this technical scheme can embody with the form of software product, this software product is stored in a storage medium, comprising some instructions in order to make a computing equipment (can be personal computer, server, mobile computing device or the network equipment etc.) perform all or part of step of method described in each embodiment of the application.And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (ROM, Read-OnlyMemory), random access memory (RAM, Random Access Memory), magnetic disc or CD etc. various can be program code stored medium.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiment, between each embodiment same or similar part mutually see.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the application.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein when not departing from the spirit or scope of the application, can realize in other embodiments.Therefore, the application can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (12)

1. the method for the detection of a target from image, is characterized in that, comprising:
After receiving the n-th pixel in image to be detected, utilize the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, calculate the inverse matrix of the background autocorrelation matrix of described n-th pixel, the background matrix of described (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before thereof, the background matrix of described n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before thereof, wherein, n and K is the integer of the pixel total quantity being less than described image to be detected, and n>K>1,
According to the inverse matrix of the background autocorrelation matrix of described n-th pixel, calculate the result of detection of described n-th pixel.
2. method according to claim 1, is characterized in that, the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel utilized in described image to be detected, calculates the inverse matrix of the background autocorrelation matrix of described n-th pixel, comprising:
The inverse matrix of the background autocorrelation matrix of foundation matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculate the inverse matrix of the first background autocorrelation matrix, described first background matrix is the matrix formed after increasing the value of described n-th pixel in the background matrix of described (n-1)th pixel;
According to the inverse matrix of described Sherman-Morrison principle and described first background autocorrelation matrix, calculate the inverse matrix of the second background autocorrelation matrix, described second background matrix is the matrix formed after removing the value of K pixel before described n-th pixel in described first background matrix.
3. method according to claim 2, is characterized in that, the inverse matrix of the background autocorrelation matrix of described foundation Sherman-Morrison principle and described (n-1)th pixel, calculates the inverse matrix of the first background autocorrelation matrix, comprising:
Utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel.
4. according to the method in claim 2 or 3, it is characterized in that, the described inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculates the inverse matrix of the second background autocorrelation matrix, comprising:
Utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel.
5. the method according to any one of Claims 1-4, is characterized in that, described after receiving the n-th pixel in image to be detected, before utilizing the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, also comprises:
Obtain the inverse matrix of the background autocorrelation matrix of front K pixel in described image to be detected;
The detailed process of the inverse matrix of the background autocorrelation matrix of front K pixel in the described image to be detected of described acquisition comprises:
Successively to a described front K pixel, calculate wherein, for first pixel, S 1 -1=I.
6. method according to claim 5, is characterized in that, the inverse matrix of the described background autocorrelation matrix according to described n-th pixel, calculates the result of detection of described n-th pixel, comprising:
Utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of described n-th pixel, d is the spectrum of target.
7. the device of the detection of a target from image, is characterized in that, comprising:
First computing module, for after receiving the n-th pixel in image to be detected, utilize the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, calculate the inverse matrix of the background autocorrelation matrix of described n-th pixel, the background matrix of described (n-1)th pixel comprises the value of described (n-1)th pixel and the pixel of K-1 before thereof, the background matrix of described n-th pixel comprises the value of described n-th pixel and the pixel of K-1 before thereof, wherein, n and K is the integer of the pixel total quantity being less than described image to be detected, and n>K>1,
Target detection module, for the inverse matrix of the background autocorrelation matrix according to described n-th pixel, calculates the result of detection of described n-th pixel.
8. device according to claim 7, is characterized in that, described first computing module comprises:
First computing unit, for the inverse matrix of the background autocorrelation matrix according to matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculate the inverse matrix of the first background autocorrelation matrix, described first background matrix is the matrix formed after increasing the value of described n-th pixel in the background matrix of described (n-1)th pixel;
Second computing unit, for the inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculate the inverse matrix of the second background autocorrelation matrix, described second background matrix is the matrix formed after removing the value of K pixel before described n-th pixel in described first background matrix.
9. device according to claim 8, it is characterized in that, described first computing unit is used for the inverse matrix of the background autocorrelation matrix of foundation matrix inversion lemma Sherman-Morrison principle and described (n-1)th pixel, calculates the inverse matrix of the first background autocorrelation matrix, comprising:
Described first computing unit specifically for, utilize calculate the inverse matrix of the first background autocorrelation matrix, wherein, C is the inverse matrix of the first background autocorrelation matrix, S 1 -1for the inverse matrix of the background autocorrelation matrix of described (n-1)th pixel, x nfor the vector value of described n-th pixel, x n tfor the transposition of the vector value of described n-th pixel.
10. device according to claim 8, is characterized in that, described second computing unit is used for the inverse matrix according to described Sherman-Morrison principle and described first background autocorrelation matrix, calculates the inverse matrix of the second background autocorrelation matrix, comprising:
Described second computing unit specifically for, utilize calculate the inverse matrix of the second background autocorrelation matrix, wherein, S 2 -1be the inverse matrix of the second background autocorrelation matrix, x n-Kfor the vector value of K pixel before described n-th pixel, x n-K tfor the transposition of the vector value of K pixel before described n-th pixel.
11. devices according to any one of claim 7 to 10, is characterized in that, also comprise:
Second computing module, for at described first computing module after receiving the n-th pixel in image to be detected, before utilizing the inverse matrix of the background autocorrelation matrix of (n-1)th pixel in described image to be detected, obtain the inverse matrix of the background autocorrelation matrix of front K pixel in described image to be detected, the detailed process of the inverse matrix of the background autocorrelation matrix of front K pixel in the described image to be detected of described acquisition comprises:
Successively to a described front K pixel, calculate wherein, for first pixel, S -1the scope of=β I, β is the biquadratic of the zero degree side to 10 of 10.
12. devices according to claim 11, is characterized in that, described target detection module is used for the inverse matrix according to the background autocorrelation matrix of described n-th pixel, calculates the result of detection of described n-th pixel, comprising:
Described target detection module specifically for, utilize calculate the result of detection of described n-th pixel, wherein, output (x n) be the result of detection of described n-th pixel, d is the spectrum of target.
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