CN106780297A - Image high registration accuracy method under scene and Varying Illumination - Google Patents
Image high registration accuracy method under scene and Varying Illumination Download PDFInfo
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Abstract
The present invention relates to a kind of image high registration accuracy method under scene and Varying Illumination, step includes:Camera reorientation, the initialization of L and F, illumination correction, the correction of camera geometric position.The present invention proposes multiple dimensioned low-rank conspicuousness detection method, and by with the collaboration conspicuousness priori based on GMM, the detection of multiple dimensioned low-rank conspicuousness is generalized in multiple image collaboration conspicuousness detection, to detect the same or analogous region occurred in multiple image.Compared with traditional conspicuousness detection method, the multiple dimensioned super-pixel blending algorithm of low-rank proposed by the invention solves the problems, such as that scale selection is difficult, and achieves more reliable conspicuousness testing result.
Description
Technical field
The invention belongs to image registration field, it is related to a kind of towards matching somebody with somebody to high level image high accuracy under scene and illumination variation
Quasi- method.The rapid relocation technology of camera is applied to the image high accuracy that scene and illumination all changes and matched somebody with somebody by this method
In quasi- problem, with the 1 principal direction image occurred with last time observed image in camera repositioning process and 6 auxiliary directional images pair
Present image high registration accuracy is target.
Background technology
The background technology being related in the present invention has:
(1) change detection (Change Detection):Change detection is the important preprocessing step of high-rise vision application.
In detection steps and decision rule in change detection, CDNet provides an extensive REF video data set for reality,
And good ranking is kept in change detection algorithm.Many algorithms based on most recently newly proposition, such as SOBS, SC_SOBS,
Sub SENSE etc., the background modeling in CDNet is one of most successful strategy.The development that other are worth mentioning is included based on change
Change the three-dimensional voxel (3Dvoxel) of detection and changed with the urban scale structure of range data using multiple panorama sketch and visit
Survey.In order to solve the influence of illumination variation and camera motion, it is aobvious with luminosity that current classic method is conceived to space exploration
The change of work, and the implicit delicate change that must be processed as noise.However, occurring in space and light varience not at some
On important small yardstick, the hypothesis on this basis largely limits its ability for delicate texture variations detection.
Our invention shows that low rank analysis (low-rank analysis) can be used to decompose the sparse change in several pictures.
(2) color continuity (Color Constancy):In change detection, quick color continuity extensively should
Use correction illumination variation.Due to the requirement of real-time speed, most of change detection methods can only be connected with simple static state color
Continuous property treating method, so limits the ability of the frequent and violent illumination variation of its tolerance.In addition, intrinsic image can
For the difference of different illumination under correction Same Scene.Recently, the decomposition of intrinsic image has been further extended various
Part, including the shape of decomposition, illumination, reflectivity etc. from one or plurality of pictures.But, these nearest development
Or needing complicated majorized function, or needs to capture plurality of pictures under intensive lighting condition, this causes them not
Inexpensive, end-to-end, close-grained change detection can be directly applied for.
(3) geometric correction (Geometry Correction):Geometric correction is that another is visited in the change with robustness
Indispensable part in survey.Conventional method for rigid scene includes similitude, affine or projective transformation.For non-
Rigid dynamic scene, light stream can be used to correct the deviation of the camera solid having dislocation.Many photographs are considered in our invention
The condition of express contract beam, employs the SIFT streams of extension to carry out geometric correction.
The content of the invention
Accurately detecting, will to the deficiency of finegrained image change in high level scene for prior art for the present invention
Camera reorientation is applied in fine change detection, by obtaining scene, illumination variation under image, there is provided it is a kind of end-to-end
The new technology for ensureing change detection robustness and accuracy from coarse to fine, is conducive to further improving to there is delicate fine texture to become
Change detectivity.
Therefore, the present invention is adopted the following technical scheme that, mainly comprise the following steps:
1. camera reorientation:
Collect the scene observation sample of different time.7 pictures are gathered in one scenario:1 ambient lighting and 6 are fixed
Illuminated to side.These pictures are stored as column vector.
Given X is as follows as above-mentioned 7 the constituted matrixes of illumination:
Wherein, 1≤k≤K, K are the number (taking K=6 here) of side illumination orientations.
Current camera is reoriented to and observes close angles and positions with last time, by observed with last time multiple
DSL is adjusted roughly after comparing, and obtains current observing matrix Y:
Camera relocates step:
A () initializes Current camera to rational position so that this position is big enough in comprising real target
Region.IcRepresent posture and the position of photo current correspondence camera;
B () keeps a rectangle frame R for bluenessbIn IcCenter, a navigation rectangle frame R for redrRepresent Current camera
Posture is poor with the relative geometry of target area.Posture and the position of dynamic adjustment camera are until there is following equation to set up:
Rr=HRb
Wherein H is by IcAnd XELThe unit matrix for calculating.
The initialization of 2.L (illumination difference) and F (camera geometric correction stream):
L represents illumination difference, and F represents the geometric correction stream of camera.
By X, Y for being given in (1), it is assumed that there is image xiAnd yi:Wherein, i=0 interval scales EL images, 1≤i≤K interval scales
DSL images.Global linear photometric calibration matrix is obtainedWith an offset vectorFormula:
Wherein,WithDifference representative image xiAnd yiMatching SIFT feature RGB color.(*) is defined specification illumination
Model, can solve under the form of closing.
3. it is normal to perceive illumination correction:
Based on Lambertian reflection model (Lambertian Reflectance Model):
Ip=∫ < np, w > ρpL(w)dw
Wherein, Ip、npAnd ρpColor, normality and the albedo of pixel p are represented respectively, and L (w) is grading function.
By to XFMiddle addition virtual optical Lv() corrects its illumination deviation with Current observation Y, obtains equation below:
Wherein,It is the color after pixel p correction,It is to add the color increment produced after virtual luminosity.
Therefore, the luminosity for balancing X and Y with following function is poor, function minimization that will be below:
Wherein, LiIt is that the spatial variations of X and Y under i-th illumination normally perceive illumination difference.The first half of formula is encouragedWith yiLuminosity uniformity.It is variable, wherein 0≤Cp≤ 1 is the possibility of pixel p change.After formula
Half part encourages the continuity in virtual optical space, wpqRepresent the similitude of p and q, p~q and represent that p is adjacent with q.
4. camera geometric position correction:
Illumination difference L is perceived according to normali, a new luminosity X being corrected can be obtainedL.By extending SIFT frames
Frame, corrects its energy function as follows:
Wherein,It is identical with (3).
So far, by Pictures location, the illumination that can obtain to above-mentioned steps being shot with last time almost complete identical
Picture, can carry out detection and find its fine and closely woven texture variations.
Proposed by the invention merges to carry out conspicuousness and cooperate with what conspicuousness was detected by the multiple dimensioned super-pixel of low-rank
Technology is mainly included the following steps that:
1) detection of the conspicuousness of single scale;
2) fusion of multiple dimensioned conspicuousness;
3) refinement of conspicuousness;
The present invention achieves more reliable conspicuousness testing result, helps further to improve prior art to conspicuousness inspection
The disposal ability of survey.
Advantages and positive effects of the present invention:
The present invention proposes multiple dimensioned low-rank conspicuousness detection method, and by with the collaboration conspicuousness elder generation based on GMM
Test, the detection of multiple dimensioned low-rank conspicuousness is generalized in multiple image collaboration conspicuousness detection, occur to detect in multiple image
Same or analogous region.Compared with traditional conspicuousness detection method, the multiple dimensioned super-pixel of low-rank proposed by the invention
Blending algorithm solves the problems, such as that scale selection is difficult, and achieves more reliable conspicuousness testing result.
Brief description of the drawings
Fig. 1:Image high registration accuracy flow chart
Fig. 2:Summer Palace figure of buddha slight change detection figure
Fig. 3:Camera resets bitmap
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings and by specific embodiment, and following examples are descriptive
, it is not limited, it is impossible to which protection scope of the present invention is limited with this.
A kind of image high registration accuracy method under scene and Varying Illumination, step is as follows:
1. camera reorientation:
Collect the scene observation sample of different time.7 pictures are gathered in one scenario:1 ambient lighting and 6 are fixed
Illuminated to side.These pictures are stored as column vector.
Given X is as follows as above-mentioned 7 the constituted matrixes of illumination:
Wherein, 1≤k≤K, K are the number (taking K=6 here) of side illumination orientations.
Current camera is reoriented to and observes close angles and positions with last time, by observed with last time multiple
DSL is adjusted roughly after comparing, and obtains current observing matrix Y:
Camera relocates step:
A () initializes Current camera to rational position so that this position is big enough in comprising real target
Region.IcRepresent posture and the position of photo current correspondence camera;
B () keeps a rectangle frame R for bluenessbIn IcCenter, a navigation rectangle frame R for redrRepresent Current camera
Posture is poor with the relative geometry of target area.Posture and the position of dynamic adjustment camera are until there is following equation to set up:
Rr=HRb
Wherein H is by IcAnd XELThe unit matrix for calculating.
The initialization of 2.L (illumination difference) and F (camera geometric correction stream):
L represents illumination difference, and F represents the geometric correction stream of camera.
By X, Y for being given in (1), it is assumed that there is image xiAnd yi:Wherein, i=0 interval scales EL images, 1≤i≤K interval scales
DSL images.Global linear photometric calibration matrix is obtainedWith an offset vectorFormula:
Wherein,WithDifference representative image xiAnd yiMatching SIFT feature RGB color.(*) is defined specification illumination
Model, can solve under the form of closing.
3. it is normal to perceive illumination correction:
Based on Lambertian reflection model (Lambertian Reflectance Model):
Ip=∫ < np, w > ρpL(w)dw
Wherein, Ip、npAnd ρpColor, normality and the albedo of pixel p are represented respectively, and L (w) is grading function.
By to XFMiddle addition virtual optical Lv() corrects its illumination deviation with Current observation Y, obtains equation below:
Wherein,It is the color after pixel p correction,It is to add the color increment produced after virtual luminosity.
Therefore, the luminosity for balancing X and Y with following function is poor, function minimization that will be below:
Wherein, LiIt is that the spatial variations of X and Y under i-th illumination normally perceive illumination difference.The first half of formula is encouragedWith yiLuminosity uniformity.It is variable, wherein 0≤Cp≤ 1 is the possibility of pixel p change.After formula
Half part encourages the continuity in virtual optical space, wpqRepresent the similitude of p and q, p~q and represent that p is adjacent with q.
4. camera geometric position correction:
Illumination difference L is perceived according to normali, a new luminosity X being corrected can be obtainedL.By extending SIFT frames
Frame, corrects its energy function as follows:
Wherein,It is identical with (3).
The image significance detection method of low-rank Multiscale Fusion is to be based on low-rank Multiscale Fusion method, will be registered
Two good images, with it, so as to the conspicuousness for different zones is detected.
Its operating procedure is as follows:
Step S1:Prepare two images of reference picture and present image, and be loaded into the method.
Step S2:Present image is carried out the initialization of illumination, light stream, is processed by present image and reference picture,
Current state hypograph is processed into image of the last state hypograph same light according under, it is preliminary eliminate before and after image irradiation twice
Influence.
Step S3:Current state image is carried out into illumination correction, last state hypograph to current state figure below is calculated
The transformation matrix of picture, by present image plus change moment matrix, is finally reached the effect of normal illumination correction.
Step S4:Present image is carried out into geometric correction, two side-play amounts of image is calculated, and side-play amount is applied to
In present image, the effect of camera geometric correction is finally realized.
Claims (2)
1. a kind of image high registration accuracy method under scene and Varying Illumination, step is as follows:
(1) the scene observation sample of different time is collected, N pictures are gathered in one scenario:1 ambient lighting and N-1 are individual fixed
Illuminated to side, these pictures are stored as column vector,
Given X is as follows as the constituted matrix of above-mentioned N number of illumination:
Wherein, 1≤k≤K, K are the number of side illumination orientations,
Current camera is reoriented to and observed close angles and positions with last time, by multiple the DSL ratios observed with last time
To rear rough adjustment, current observing matrix Y is obtained:
(2) the initialization of L and F:
L represents illumination difference, and F represents the geometric correction stream of camera,
By X, Y for being given in (1), it is assumed that there is image xiAnd yi:Wherein, i=0 interval scales EL images, 1≤i≤K interval scales DSL figures
Picture, has obtained global linear photometric calibration matrixWith an offset vectorFormula:
Wherein,WithDifference representative image xiAnd yiMatching SIFT feature RGB color, (*) is defined specification illumination mould
Type, can solve under the form of closing;
(3) it is normal to perceive illumination correction:
Based on Lambertian reflection model:
Ip=∫ < np, w > ρpL(w)dw
Wherein, Ip、npAnd ρpColor, normality and the albedo of pixel p are represented respectively, and L (w) is grading function,
By to XFMiddle addition virtual optical Lv() corrects its illumination deviation with Current observation Y, obtains equation below:
Wherein,It is the color after pixel p correction,It is to add the color increment produced after virtual luminosity,
The luminosity for balancing X and Y with following function is poor, function minimization that will be below:
Wherein, LiIt is that the spatial variations of X and Y under i-th illumination normally perceive illumination difference, the first half of formula is encouragedWith
yiLuminosity uniformity,It is variable, wherein 0≤Cp≤ 1 be pixel p change possibility, formula it is latter half of
Divide the continuity for encouraging virtual optical space, wpqRepresent the similitude of p and q, p~q and represent that p is adjacent with q,
(4) camera geometric position correction:
Illumination difference L is perceived according to normali, a new luminosity X being corrected can be obtainedL, by extending SIFT frameworks, repair
Just its energy function is as follows:
Wherein,It is identical with (3),
Pictures location, the identical picture of illumination for obtaining being shot with last time by above-mentioned steps, carry out detection and find that its is thin
Close texture variations.
2. the image high registration accuracy method under scene according to claim 1 and Varying Illumination, it is characterised in that:
Described camera reorientation step:
A () initializes Current camera to rational position so that this position is big enough in comprising real target area,
IcRepresent posture and the position of photo current correspondence camera;
B () keeps a rectangle frame R for bluenessbIn IcCenter, a navigation rectangle frame R for redrRepresent Current camera posture
Relative geometry with target area is poor, and posture and the position of dynamic adjustment camera are until there is following equation to set up:
Rr=HRb
Wherein H is by IcAnd XELThe unit matrix for calculating.
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Cited By (8)
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CN108269276A (en) * | 2017-12-22 | 2018-07-10 | 天津大学 | One kind carries out scene based on image registration and is slightly variable detection method |
CN109579731A (en) * | 2018-11-28 | 2019-04-05 | 华中科技大学 | A method of executing 3 d surface topography measurement based on image co-registration |
CN110442153A (en) * | 2019-07-10 | 2019-11-12 | 佛山科学技术学院 | A kind of passive optical is dynamic to catch system video cameras Corrective control method and system |
CN110622213A (en) * | 2018-02-09 | 2019-12-27 | 百度时代网络技术(北京)有限公司 | System and method for depth localization and segmentation using 3D semantic maps |
CN110780743A (en) * | 2019-11-05 | 2020-02-11 | 聚好看科技股份有限公司 | VR (virtual reality) interaction method and VR equipment |
CN110827193A (en) * | 2019-10-21 | 2020-02-21 | 国家广播电视总局广播电视规划院 | Panoramic video saliency detection method based on multi-channel features |
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CN109579731A (en) * | 2018-11-28 | 2019-04-05 | 华中科技大学 | A method of executing 3 d surface topography measurement based on image co-registration |
CN110442153A (en) * | 2019-07-10 | 2019-11-12 | 佛山科学技术学院 | A kind of passive optical is dynamic to catch system video cameras Corrective control method and system |
CN110442153B (en) * | 2019-07-10 | 2022-03-25 | 佛山科学技术学院 | Camera correction control method and system for passive optical dynamic capturing system |
CN110827193A (en) * | 2019-10-21 | 2020-02-21 | 国家广播电视总局广播电视规划院 | Panoramic video saliency detection method based on multi-channel features |
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CN110780743A (en) * | 2019-11-05 | 2020-02-11 | 聚好看科技股份有限公司 | VR (virtual reality) interaction method and VR equipment |
CN112070831A (en) * | 2020-08-06 | 2020-12-11 | 天津大学 | Active camera repositioning method based on multi-plane joint pose estimation |
CN111882616A (en) * | 2020-09-28 | 2020-11-03 | 李斯特技术中心(上海)有限公司 | Method, device and system for correcting target detection result, electronic equipment and storage medium |
CN111882616B (en) * | 2020-09-28 | 2021-06-18 | 李斯特技术中心(上海)有限公司 | Method, device and system for correcting target detection result, electronic equipment and storage medium |
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