CN113643341B - Different-scale target image registration method based on resolution self-adaptation - Google Patents

Different-scale target image registration method based on resolution self-adaptation Download PDF

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CN113643341B
CN113643341B CN202111184265.8A CN202111184265A CN113643341B CN 113643341 B CN113643341 B CN 113643341B CN 202111184265 A CN202111184265 A CN 202111184265A CN 113643341 B CN113643341 B CN 113643341B
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CN113643341A (en
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邓科
梁倩云
夏伟
张霄
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Sichuan University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/337—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving reference images or patches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/40—Scaling the whole image or part thereof
    • G06T3/4007—Interpolation-based scaling, e.g. bilinear interpolation
    • 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
    • G06T2207/10016—Video; Image sequence

Abstract

The invention relates to a different-scale target image registration method based on resolution self-adaptation, belonging to the field of image processing; extracting the pixel points of an image with the pixel point number of mn, and regarding the pixel points as mn-order finite rank operatorsA(ii) a After the image is amplified, if the number of the pixels of the original image cannot completely fill the amplified image, that is, the pixels of the amplified image need to be filled, and the image is scaled based on the lagrange interpolation method: for finite rank operatorAInterpolating by rows to obtain finite rank operatorBFor finite rank operatorBFinally obtaining a finite rank operator by column interpolationCFinite rank operator to be obtainedCFeeding back to the amplified image to obtain a Lagrange interpolation scaled image; and performing feature point registration by using an SIFT image registration method, and the like. The Lagrange interpolation method is combined with SIFT image registration and scale scaling to be used for registration scaling processing of the same automobile different-scale conditions in the video images, so that the pixel difference of image registration is obviously reduced.

Description

Different-scale target image registration method based on resolution self-adaptation
Technical Field
The invention belongs to the technical field of image data processing, and particularly relates to a different-scale target image registration method based on resolution self-adaptation.
Background
In real life, the screenshot of two front and rear video vehicles with different sizes is calculated mainly by searching the same characteristic points on the front and rear video vehicles, and the calculated proportion is used for amplifying or reducing the picture. The problem mainly exists that the scale calculated by the corresponding feature points is not necessarily capable of scaling the picture to a proper size, and is either generally enlarged or generally reduced.
For example: the method comprises the steps that a video file shot by an intersection monitoring probe obtains frame-by-frame automobile moving pictures, obviously, a closer frame image of the same automobile in different frames is slightly larger, a farther frame image of the same automobile in different frames is slightly smaller, and people hope to adaptively reduce a larger image in the front frame and the rear frame to the size same as that of a smaller image or adaptively enlarge the smaller image to the size same as that of the larger image. Due to the fact that the size of the same automobile is not consistent under different time frames, an image transformation algorithm needs to be established to stretch target images with different distances to proper sizes.
The existing image scaling method based on SIFT image registration comprises the following steps:
using SIFT image registration to obtain feature points of the image; obtaining the distance between every two characteristic points by using every two connecting lines of all the characteristic points, and averaging the distances to obtain an average proportion; and zooming the picture by using the proportion obtained by the method. However, the picture scaling effect of this method is not ideal, and the pixel value accuracy after picture scaling is low.
Therefore, at present, a method for registering different-scale target images based on resolution self-adaptation needs to be designed to solve the above problems.
Disclosure of Invention
The invention aims to provide a different-scale target image registration method based on resolution self-adaptation, which is used for solving the technical problems in the prior art, such as: using SIFT image registration to obtain feature points of the image; obtaining the distance between every two characteristic points by using every two connecting lines of all the characteristic points, and averaging the distances to obtain an average proportion; and zooming the picture by using the proportion obtained by the method. However, the picture scaling effect of this method is not ideal, and the pixel value accuracy after picture scaling is low.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a different-scale target image registration method based on resolution self-adaptation comprises the following steps:
s1, acquiring image data: acquiring two images, and if the two images are different in size, zooming in and out; wherein, the number of the pixel points is
Figure 313DEST_PATH_IMAGE001
The pixel points of the image are extracted and regarded as
Figure DEST_PATH_IMAGE002
Order limited rank operator
Figure DEST_PATH_IMAGE003
(ii) a After the image is enlarged, if the number of the pixels of the original image cannot completely fill the enlarged image, that is, the pixels of the enlarged image need to be filled, and the process proceeds to step S2;
s2, image scaling based on the Lagrange interpolation method: assuming that the magnified image requires
Figure 791552DEST_PATH_IMAGE004
For each pixel point, firstly, finite rank operator is subjected
Figure 135946DEST_PATH_IMAGE003
Interpolating by rows to obtain finite rank operator
Figure DEST_PATH_IMAGE005
Then to finite rank operator
Figure 102765DEST_PATH_IMAGE005
Finally obtaining a finite rank operator by column interpolation
Figure 202308DEST_PATH_IMAGE006
Finite rank operator to be obtained
Figure 621788DEST_PATH_IMAGE006
Feedback to amplificationObtaining a Lagrange interpolation method scaled image from the later image;
s3, SIFT registration: and (4) carrying out feature point registration on the Lagrange interpolation method scaled image in the step S2 by using an SIFT image registration method, carrying out pairwise matching calculation on the obtained feature points to calculate the proportion, then solving the average value, and then carrying out image scaling on the image based on the Lagrange interpolation method once again according to the average proportion to obtain the target scaled image with different scales.
Further, in step S2, finite rank operator is subjected to
Figure 769872DEST_PATH_IMAGE003
Interpolating by rows to obtain finite rank operator
Figure 450252DEST_PATH_IMAGE005
The method comprises the following specific steps:
is provided with
Figure DEST_PATH_IMAGE007
For finite rank operators
Figure 861642DEST_PATH_IMAGE003
In the case of a composite material, for example,
Figure 565156DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE009
therefore, finite rank operator
Figure 283975DEST_PATH_IMAGE005
In the form:
Figure 22124DEST_PATH_IMAGE010
。
further, in step S2, finite rank operator is subjected to
Figure 463470DEST_PATH_IMAGE005
Finally obtaining a finite rank operator by column interpolation
Figure 654280DEST_PATH_IMAGE006
The method comprises the following specific steps:
order to
Figure 100002_DEST_PATH_IMAGE011
；
Finite rank operator
Figure 347429DEST_PATH_IMAGE006
：
Figure 736822DEST_PATH_IMAGE012
Wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE013
。
compared with the prior art, the invention has the beneficial effects that:
the invention has the innovation points that the registration and scaling processing of the same automobile in different scales in the video images is carried out by combining the Lagrange interpolation method with SIFT image registration and scale scaling, so that the pixel difference of image registration is obviously reduced, and the problem that a smaller or larger image cannot be scaled in place is solved.
Drawings
Fig. 1 is a schematic flow chart of an embodiment of the present application.
Fig. 2 is a schematic diagram illustrating the effect of different scaling of the front and rear frames of a gray car captured by the 9 th and 10 th frames in a video image sequence according to the embodiment of the present application.
Fig. 3 is a schematic diagram illustrating the effect of different scaling of the front and rear frames of a black car captured by the 30 th and 31 th frames in the same video image sequence according to the embodiment of the present application.
Fig. 4 is a schematic diagram illustrating the effect of different scaling of the front and rear frames of a black car captured by the 40 th and 41 th frames in the same video image sequence according to the embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to fig. 1 to 4 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 1, a method for registration of an out-of-scale target image based on resolution adaptation is therefore proposed, which includes the following steps:
s1, acquiring image data: acquiring two images, and if the two images are different in size, zooming in and out; wherein, the number of the pixel points is
Figure 755594DEST_PATH_IMAGE001
The pixel points of the image are extracted and regarded as
Figure 496017DEST_PATH_IMAGE002
Order limited rank operator
Figure 789595DEST_PATH_IMAGE003
(ii) a After the image is enlarged, if the number of the pixels of the original image cannot completely fill the enlarged image, that is, the pixels of the enlarged image need to be filled, and the process proceeds to step S2;
s2, image scaling based on the Lagrange interpolation method: assuming that the magnified image requires
Figure 174440DEST_PATH_IMAGE004
For each pixel point, firstly, finite rank operator is subjected
Figure 456123DEST_PATH_IMAGE003
Interpolating by rows to obtain finite rank operator
Figure 621525DEST_PATH_IMAGE005
Then to finite rank operator
Figure 390898DEST_PATH_IMAGE005
Finally obtaining a finite rank operator by column interpolation
Figure 958146DEST_PATH_IMAGE006
Finite rank operator to be obtained
Figure 646616DEST_PATH_IMAGE006
Feeding back to the amplified image to obtain a Lagrange interpolation scaled image;
s3, SIFT registration: and (4) carrying out feature point registration on the Lagrange interpolation method scaled image in the step S2 by using an SIFT image registration method, carrying out pairwise matching calculation on the obtained feature points to calculate the proportion, then solving the average value, and then carrying out image scaling on the image based on the Lagrange interpolation method once again according to the average proportion to obtain the target scaled image with different scales.
Further, in step S2, finite rank operator is subjected to
Figure 299314DEST_PATH_IMAGE003
Interpolating by rows to obtain finite rank operator
Figure 137957DEST_PATH_IMAGE005
The method comprises the following specific steps:
is provided with
Figure 356449DEST_PATH_IMAGE007
For finite rank operators
Figure 419083DEST_PATH_IMAGE003
In the case of a composite material, for example,
Figure 231181DEST_PATH_IMAGE008
wherein the content of the first and second substances,
Figure 998149DEST_PATH_IMAGE009
therefore, finite rank operator
Figure 274409DEST_PATH_IMAGE005
In the form:
Figure 445628DEST_PATH_IMAGE010
。
further, in step S2, finite rank operator is subjected to
Figure 807339DEST_PATH_IMAGE005
Finally obtaining a finite rank operator by column interpolation
Figure 348304DEST_PATH_IMAGE006
The method comprises the following specific steps:
order to
Figure 682333DEST_PATH_IMAGE011
；
Finite rank operator
Figure 86770DEST_PATH_IMAGE006
：
Figure 998094DEST_PATH_IMAGE014
Wherein the content of the first and second substances,
Figure 513389DEST_PATH_IMAGE013
。
specifically, two frames of images before and after a gray car are captured in frames 9 and 10 of a video image sequence, the car occupies 248 × 243 pixels in the farther frame, and the car occupies 397 × 469 pixels in the closer frame, and the pixel values of the two are scaled to be approximately the same.
The result is shown in fig. 2, and the image registration by the method of the scheme of the application has almost no pixel difference; the SIFT image registration is adopted to have a long pixel difference of about-118 and a wide pixel difference of about 142, and the SIFT image registration is concretely characterized in that a windshield has a long pixel difference of about 21 and a wide pixel difference of about-60, a bumper has a long pixel difference of about-51 and a wide pixel difference of about-5, a vehicle lamp has a long pixel difference of about 27 and a wide pixel difference of about 39, and a vehicle head has a long pixel difference of about-86 and a wide pixel difference of about-10.
The 30 th and 31 th frames in the same video image sequence capture two frames of images of a black car, respectively occupy 254 x 254 pixels and 466 x 590 pixels, and the pixel values are scaled to be approximately the same.
The result is shown in fig. 3, and the image registration by the method of the scheme of the application has almost no pixel difference; the SIFT image registration is adopted, and a long pixel difference of about-22 and a wide pixel difference of about 3 exist, and the method is specifically characterized in that a windshield is provided with a long pixel difference of about 161 and a wide pixel difference of about 67, a bumper is provided with a long pixel difference of about 173 and a wide pixel difference of about-8, a vehicle lamp is provided with a long pixel difference of about 61 and a wide pixel difference of about 76, and a vehicle head is provided with a long pixel difference of about 42 and a wide pixel difference of about 65.
The 40 th and 41 th frames in the same video image sequence capture two frames of images of a black car, occupying 245 x 230 pixels and 398 x 422 pixels, respectively, and have pixel values scaled to be approximately as large.
The result is shown in fig. 4, and the image registration by the method of the scheme of the application has almost no pixel difference; the SIFT image registration is adopted to have a long pixel difference of about-90 and a wide pixel difference of about 20, and the SIFT image registration is concretely characterized in that a windshield has a long pixel difference of about-60 and a wide pixel difference of about 77, a bumper has a long pixel difference of about 54 and a wide pixel difference of about 54, a vehicle lamp has a long pixel difference of about 27 and a wide pixel difference of about 124, and a vehicle head has a long pixel difference of about-39 and a wide pixel difference of about 20.
Therefore, the Lagrange interpolation method is combined with SIFT image registration and scale scaling to be used for registration scaling processing of the same automobile in different scales in the video image, so that the pixel difference of image registration is remarkably reduced, and the problem that a smaller or larger image cannot be scaled in place is solved.
The above are preferred embodiments of the present invention, and all changes made according to the technical scheme of the present invention that produce functional effects do not exceed the scope of the technical scheme of the present invention belong to the protection scope of the present invention.

Claims (3)

1. A different-scale target image registration method based on resolution self-adaptation is characterized by comprising the following steps:
s1, acquiring image data: acquiring two images, and if the two images are different in size, zooming in and out; wherein, the number of the pixel points is
Figure DEST_PATH_IMAGE001
The pixel points of the image are extracted and regarded as
Figure 759670DEST_PATH_IMAGE002
Order limited rank operator
Figure 880073DEST_PATH_IMAGE003
(ii) a After the image is enlarged, if the number of the pixels of the original image cannot completely fill the enlarged image, that is, the pixels of the enlarged image need to be filled, and the process proceeds to step S2;
s2, image scaling based on the Lagrange interpolation method: assuming that the magnified image requires
Figure DEST_PATH_IMAGE004
For each pixel point, firstly, finite rank operator is subjected
Figure 253286DEST_PATH_IMAGE005
Interpolating by rows to obtain finite rank operator
Figure DEST_PATH_IMAGE006
Then to finite rank operator
Figure 117337DEST_PATH_IMAGE006
Finally obtaining a finite rank operator by column interpolation
Figure 525184DEST_PATH_IMAGE007
Finite rank operator to be obtained
Figure 613226DEST_PATH_IMAGE007
Feeding back to the amplified image to obtain a Lagrange interpolation scaled image;
s3, SIFT registration: and (4) carrying out feature point registration on the Lagrange interpolation method scaled image in the step S2 by using an SIFT image registration method, carrying out pairwise matching calculation on the obtained feature points to calculate the proportion, then solving the average value, and then carrying out image scaling on the image based on the Lagrange interpolation method once again according to the average proportion to obtain the target scaled image with different scales.
2. The method for registration of different-scale target images based on resolution adaptation as claimed in claim 1, wherein in step S2, finite rank operator is applied
Figure 83521DEST_PATH_IMAGE003
Interpolating by rows to obtain finite rank operator
Figure 548001DEST_PATH_IMAGE006
The method comprises the following specific steps:
is provided with
Figure 46241DEST_PATH_IMAGE008
For finite rank operators
Figure 508446DEST_PATH_IMAGE003
In the case of a composite material, for example,
Figure DEST_PATH_IMAGE009
wherein the content of the first and second substances,
Figure 856251DEST_PATH_IMAGE010
therefore, finite rank operator
Figure 858842DEST_PATH_IMAGE006
In the form:
Figure DEST_PATH_IMAGE011
。
3. the method for registration of different-scale target images based on resolution adaptation as claimed in claim 2, wherein in step S2, finite rank operator is applied
Figure 851068DEST_PATH_IMAGE006
Finally obtaining a finite rank operator by column interpolation
Figure 874388DEST_PATH_IMAGE007
The method comprises the following specific steps:
order to
Figure 319276DEST_PATH_IMAGE012
；
Finite rank operator
Figure 859979DEST_PATH_IMAGE007
：
Figure DEST_PATH_IMAGE013
Wherein the content of the first and second substances,
Figure 300187DEST_PATH_IMAGE014
。
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