CN107507232A - Stereo Matching Algorithm based on multiple dimensioned iteration - Google Patents

Stereo Matching Algorithm based on multiple dimensioned iteration Download PDF

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CN107507232A
CN107507232A CN201710575150.9A CN201710575150A CN107507232A CN 107507232 A CN107507232 A CN 107507232A CN 201710575150 A CN201710575150 A CN 201710575150A CN 107507232 A CN107507232 A CN 107507232A
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scale layer
reference picture
disparity map
matching
target image
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CN107507232B (en
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朱程涛
李锵
滕建辅
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Tianjin University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G06T7/593—Depth or shape recovery from multiple images from stereo images
    • 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

Abstract

The invention discloses a kind of Stereo Matching Algorithm based on multiple dimensioned iteration, including：To reference picture IR_0And target image IT_0Carry out the multi-resolution decomposition based on gaussian filtering formula；To the n-th scale layer reference picture IR_nAnd target image IT_nCarry out, based on window polymerization Stereo matching, obtaining the reference disparity map D of current scale layerR_n, and to DR_nCarry out the reference disparity map after up-sampling generation up-samplingTo the reference picture I of the n-th 1 scale layersR_n‑1And target image IT_n‑1Carry out, based on window polymerization Stereo matching, obtaining the reference disparity map D of the n-th 1 scale layersR_n‑1；Obtain the reference disparity map flag bit M of the n-th 1 scale layersR_n‑1；In the n-th 1 scale layers, using blocking gradient Matching power flow and DR_n‑1、MR_n‑1Form new reference picture Matching power flow cR_n‑1；Repeat step.

Description

Stereo Matching Algorithm based on multiple dimensioned iteration
Technical field
The present invention relates to the Stereo matching in computer stereo vision field, available for the three-dimensional reconstruction of image, in medical science Guidance is provided in image, media device etc..
Background technology
Although the three-dimensional perception of human eye is very strong, the depth order of two objects easily can be correctly judged very much, But its accurate range information can not be obtained.However, using Stereo Matching Technology, by accurately demarcating, using suitable Computational methods, matching correctly in the case of can obtain the numerical informations such as point-device depth and position, this causes machine The work such as the accurate control of device people, reconstructing three-dimensional model can be completed preferably.
Stereo Matching Technology by carrying out matching treatment to reference picture and target image, finally give reference picture and Target image disparity map, the brightness value of each pixel represents the pixel in reference picture and target image in disparity map The difference of coordinate.Stereo Matching Algorithm is broadly divided into Global Algorithm, half Global Algorithm and local algorithm.Global Algorithm and half is entirely Office's algorithm is needed to carry out complicated constraint to the energy function of structure, and final matching result is obtained by the way of optimization, Algorithm complex is higher.Local algorithm need not build the bound term of energy function, therefore computation complexity is relatively low, be easy to real-time Application.Traditional local algorithm is normally based on the matching way of single yardstick, such algorithm do not consider reference picture with Target image is only matched, it is difficult to obtain preferable matching result on a yardstick in the difference of metric space.
As Stereo Matching Algorithm is in the extensive use in industry, sphere of life, high-precision matching way, which becomes, works as The focus of the research of lower stereovision technique.In recent years, the continuous development of theory of stereo vision is high-precision three-dimensional of acquisition With providing theoretical foundation.
The content of the invention
The present invention proposes a kind of Stereo Matching Algorithm based on multiple dimensioned iteration regarding to the issue above, with reference first to image and Target image carries out multiple dimensioned decomposition, and then corresponding reference picture and target image are changed since yardstick maximum layer Generation matching, finally obtains high-precision stereo matching results, technical scheme is as follows：
A kind of Stereo Matching Algorithm based on multiple dimensioned iteration, comprises the following steps：
(1) by reference picture IR_0The reference picture being designated as under the 0th scale layer, target image IT_0It is designated as under the 0th scale layer Target image, then respectively to IR_0And IT_0The multi-resolution decomposition based on gaussian filtering formula is carried out, obtains Nmax+ 1 different chi The reference picture I spent under layerR_nAnd NmaxTarget image I under+1 different scale layerT_n, wherein n is scale layer numbering and n ∈{0,1,2,…,Nmax}；
(2) current scale layer numbering n is initialized as NmaxEven n=Nmax；
(3) to current scale layer reference picture IR_nAnd target image IT_nUsing blocking gradient Matching power flow calculation Reference picture I is calculatedR_nMatching power flow gR_n, then to gR_nCarry out, based on window polymerization Stereo matching, obtaining current chi Spend the reference disparity map D of layerR_n, and to DR_nCarry out the reference disparity map after up-sampling generation up-sampling
(4) to the reference picture I of the (n-1)th scale layerR_n-1And target image IT_n-1Using blocking gradient Matching power flow meter Reference picture I is calculated in calculation modeR_n-1Matching power flow gR_n-1, then to gR_n-1Carry out based on window polymerization Stereo matching, Obtain the reference disparity map D of the (n-1)th scale layerR_n-1；
(5) the reference disparity map flag bit of the (n-1)th scale layer is set as MR_n-1, compareAnd DR_n-1The value of corresponding points, When the value of the two is equal, then it is 1 to refer to disparity map flag bit value accordingly, when the value of the two is unequal, then accordingly It is 0 with reference to disparity map flag bit value；
(6) in the (n-1)th scale layer, according to formula cR_n-1=[gR_n-1+|dn-1-DR_n-1|]·MR_n-1Calculate reference picture IR_n-1Renewal Matching power flow cR_n-1, wherein dn-1For the disparity search value of the (n-1)th scale layer reference picture, according to universe image Weight polymerization principle calculates the aggregate weight W of the reference picture of the (n-1)th scale layerR_n-1, by cR_n-1With WR_n-1Gathered after multiplication Matching is closed, the (n-1)th scale layer after final iteration is obtained and refers to disparity map
(7) judge n-1 value, match and terminate if n-1=0, it is right if n-1 ≠ 0Up-sampled, and will be upper Result after sampling is denoted asScale layer numbering n is subtracted 1 simultaneously；
(8) repeat step (4)-(7) are until n-1=0.
In a word, the present invention is proposed a kind of for traditional based on deficiency existing for single yardstick sectional perspective matching algorithm Based on the Stereo Matching Algorithm of multiple dimensioned iteration, the characteristic according to multi-scale image is iterated matching.The present invention can obtain More accurately Stereo matching effect, has a wide range of applications.
Brief description of the drawings
The Stereo Matching Algorithm flow chart based on multiple dimensioned iteration of Fig. 1 present invention.
Fig. 2 is traditional based on single yardstick sectional perspective matching algorithm and of the invention to standard testing image " Adirondack " is matched obtained contrast disparity map, and (a) is Adirondack left figures, and (b) is traditional single yardstick The left disparity map that sectional perspective matching algorithm obtains, (c) be the present invention obtain for left disparity map.
Fig. 3 is traditional based on single yardstick sectional perspective matching algorithm and of the invention to standard testing image " Recycle " is matched obtained contrast disparity map, and (a) is Recycle left figures, and (b) is that traditional single yardstick is locally vertical The left disparity map that body matching algorithm obtains, (c) be the present invention obtain for left disparity map.
Embodiment
Stereo Matching Algorithm of the invention based on multiple dimensioned iteration, is mainly made up of four parts：Reference picture and target figure The multi-resolution decomposition of picture, the Stereo matching of current scale layer and up-sampling, the Stereo matching of next scale layer, adjacent scale layer With Comparative result and Matching power flow renewal polymerization.Specific steps and principle are as follows：
101：Reference picture IR_0And target image IT_0Multi-resolution decomposition；
Using the traditional multi-resolution decomposition based on gaussian filtering formula of use to reference picture IR_0And target image IT_0 Multi-resolution decomposition is carried out, obtains a series of reference picture I under different scale layersR_nAnd target image IT_n(n=0,1, 2,…,Nmax, n be scale layer numbering, NmaxNumbered for out to out layer).
Wherein i, j are respectively the horizontal stroke of central pixel point (i, j), ordinate；(u, v) is pixel, chi centered on (i, j) Any pixel in the very little window for (2r1+1) × (2r2+1), u, v are respectively horizontal, ordinate；Γ (u, v) is picture in Gaussian kernel The weight of vegetarian refreshments (u, v).
102：Initialize current scale layer numbering n；
From NmaxScale layer proceeds by Iterative matching, and n is initialized as into NmaxEven n=Nmax。
103：To current scale layer (the n-th scale layer) reference picture IR_nAnd target image IT_nCarry out based on traditional window Mouth polymerization Stereo matching；
Gradient Matching power flow is blocked using what is arbitrarily commonly used in Stereo Matching Technology, obtains the reference parallax of current scale layer Scheme DR_n, and to DR_nCarry out the reference disparity map after up-sampling generation up-sampling
en(i,j,dn)=min (| ▽ IR_n(i,j)-▽IT_n(i,j-dn)|,2)
Wherein ▽ IR_n、▽IT_nThe respectively gradient of current scale layer (the n-th scale layer) reference picture, the ladder of target image Degree；en(i,j,dn) be current scale layer (the n-th scale layer) reference picture initial matching cost, dnFor current scale layer (n-th Scale layer) reference picture disparity search value, dnmin、dnmaxMinimum, the maximum of respectively corresponding disparity search；ω (x, y) is The pixel centered on (x, y), size are 11 × 11 polymerizing windows；fupRepresent up-sampling operation.
104：To the reference picture I of next scale layer (the (n-1)th scale layer)R_n-1And target image IT_n-1It is based on Traditional window polymerization Stereo matching；
Gradient Matching power flow is blocked using what is arbitrarily commonly used in Stereo Matching Technology, the reference for obtaining the (n-1)th scale layer regards Difference figure DR_n-1：
en-1(i,j,dn-1)=min (| ▽ IR_n-1(i,j)-▽IT_n-1(i,j-dn-1)|,2)
Wherein ▽ IR_n、▽IT_nThe respectively gradient of the (n-1)th scale layer reference picture, the gradient of target image；en-1(i, j,dn-1) be the (n-1)th scale layer reference picture initial matching cost, dn-1For the disparity search of the (n-1)th scale layer reference picture Value, dn-1min、dn-1maxMinimum, the maximum of respectively corresponding disparity search；
105：Calculate the reference disparity map flag bit M of the (n-1)th scale layerR_n-1；
CompareAnd DR_n-1Value, obtain the reference disparity map flag bit M of the (n-1)th scale layerR_n-1：
106：The (n-1)th scale layer after (the (n-1)th scale layer) iteration is calculated according to weight polymerization and refers to disparity map
cR_n-1(i,j,dn-1)=[min (| ▽ IR_n-1(i,j)-▽IT_n-1(i,j-dn-1)|,2)+|dn-1-DR_n-1(i,j) |]·MR_n-1(i,j)
Wherein cR_n-1Gradient Matching power flow and D are blocked to utilizeR_n-1、MR_n-1New (the (n-1)th scale layer) reference of composition Images match cost；WR_n-1For the aggregate weight of the reference picture of the (n-1)th scale layer, k1、k2For the (n-1)th scale layer reference picture Horizontal, ordinate variable, e is the nature truth of a matter；μ is zoom factor, is worth for 8.
107：Loop iteration matches；
Judge n-1 value：
Match and terminate if n-1=0；
It is right if n-1 ≠ 0Up-sampled, and the result after up-sampling is denoted asSimultaneously by scale layer Numbering n subtracts 1, and then repeat step (4)-(9) are until n-1=0.
(1)
(2) n=n-1 is made
At the end of loop iteration matches, obtainAs final multiple dimensioned Iterative matching result.
Tested below with specific to verify the feasibility of this method, it is described below：
Result of the test is that this method in CPU is Intel i7-3610QM, 2.3GHz, inside saves as 16G notebook computer Obtained by upper operation, operating system is Windows 7, and simulation software is 64 Matlab R2012b.Test the figure pair used ' Adirondack ', ' Recycle ' are that standardized test chart derives from http://vision.middlebury.edu/stereo/ eval3/。
From Fig. 2, Fig. 3 can be seen that it is traditional based on single yardstick sectional perspective matching algorithm in Fig. 2 (b) More error hiding be present in the background area of Adirondack disparity maps and the foreground area of Fig. 3 (b) Recycle disparity maps Point, and the present invention preferably eliminates the Mismatching point of above-mentioned zone by the way of based on multiple dimensioned Iterative matching, acquisition Disparity map matching effect is preferable.
It will be appreciated by those skilled in the art that accompanying drawing is the schematic diagram of a preferred embodiment, the embodiments of the present invention Sequence number is for illustration only, does not represent the quality of embodiment.
The foregoing is only presently preferred embodiments of the present invention, be not intended to limit the invention, it is all the present invention spirit and Within principle, any modification, equivalent substitution and improvements made etc., it should be included in the scope of the protection.
The idiographic flow of the present invention is as follows：
(1) by reference picture IR_0The reference picture being designated as under the 0th scale layer, target image IT_0It is designated as under the 0th scale layer Target image, then respectively to IR_0And IT_0The multi-resolution decomposition based on gaussian filtering formula is carried out, obtains Nmax+ 1 different chi The reference picture I spent under layerR_nAnd NmaxTarget image I under+1 different scale layerT_n, wherein n is scale layer numbering and n ∈{0,1,2,…,Nmax}；
(2) current scale layer numbering n is initialized as NmaxEven n=Nmax；
(3) to current scale layer reference picture IR_nAnd target image IT_nUsing blocking gradient Matching power flow calculation Reference picture I is calculatedR_nMatching power flow gR_n, then to gR_nCarry out polymerizeing Stereo matching based on traditional window, obtain The reference disparity map D of current scale layerR_n, and to DR_nCarry out the reference disparity map after up-sampling generation up-sampling
(4) to the reference picture I of the (n-1)th scale layerR_n-1And target image IT_n-1Using blocking gradient Matching power flow meter Reference picture I is calculated in calculation modeR_n-1Matching power flow gR_n-1, then to gR_n-1Carry out vertical based on traditional window polymerization Body matches, and obtains the reference disparity map D of the (n-1)th scale layerR_n-1；
(5) the reference disparity map flag bit of the (n-1)th scale layer is set as MR_n-1, compareAnd DR_n-1The value of corresponding points, When the value of the two is equal, then it is 1 to refer to disparity map flag bit value accordingly, when the value of the two is unequal, then accordingly It is 0 with reference to disparity map flag bit value；
(6) in the (n-1)th scale layer, according to formula cR_n-1=[gR_n-1+|dn-1-DR_n-1|]·MR_n-1Calculate reference picture IR_n-1Renewal Matching power flow cR_n-1, wherein dn-1For the disparity search value of the (n-1)th scale layer reference picture, according to universe image Weight polymerization principle calculates the aggregate weight W of the reference picture of the (n-1)th scale layerR_n-1, by cR_n-1With WR_n-1Gathered after multiplication Matching is closed, the (n-1)th scale layer after final iteration is obtained and refers to disparity map
(7) judge n-1 value, match and terminate if n-1=0, it is right if n-1 ≠ 0Up-sampled, and will be upper Result after sampling is denoted asScale layer numbering n is subtracted 1 simultaneously；
(8) repeat step (4)-(7) are until n-1=0.

Claims (1)

1. a kind of Stereo Matching Algorithm based on multiple dimensioned iteration, comprises the following steps：
(1) by reference picture IR_0The reference picture being designated as under the 0th scale layer, target image IT_0The mesh being designated as under the 0th scale layer Logo image, then respectively to IR_0And IT_0The multi-resolution decomposition based on gaussian filtering formula is carried out, obtains Nmax+ 1 different scale layer Under reference picture IR_nAnd NmaxTarget image I under+1 different scale layerT_n, wherein n be scale layer number and n ∈ 0, 1,2,…,Nmax}；
(2) current scale layer numbering n is initialized as NmaxEven n=Nmax；
(3) to current scale layer reference picture IR_nAnd target image IT_nCalculated using gradient Matching power flow calculation is blocked Obtain reference picture IR_nMatching power flow gR_n, then to gR_nCarry out, based on window polymerization Stereo matching, obtaining current scale layer Reference disparity map DR_n, and to DR_nCarry out the reference disparity map after up-sampling generation up-sampling
(4) to the reference picture I of the (n-1)th scale layerR_n-1And target image IT_n-1Using blocking gradient Matching power flow calculating side Reference picture I is calculated in formulaR_n-1Matching power flow gR_n-1, then to gR_n-1Based on window polymerization Stereo matching, obtain The reference disparity map D of (n-1)th scale layerR_n-1；
(5) the reference disparity map flag bit of the (n-1)th scale layer is set as MR_n-1, compareAnd DR_n-1The value of corresponding points, when two When the value of person is equal, then it is 1 to refer to disparity map flag bit value accordingly, when the value of the two is unequal, then corresponding reference Disparity map flag bit value is 0；
(6) in the (n-1)th scale layer, according to formula cR_n-1=[gR_n-1+|dn-1-DR_n-1|]·MR_n-1Calculate reference picture IR_n-1's Update Matching power flow cR_n-1, wherein dn-1For the disparity search value of the (n-1)th scale layer reference picture, gathered according to universe image weights Close the aggregate weight W that principle calculates the reference picture of the (n-1)th scale layerR_n-1, by cR_n-1With WR_n-1Polymerization is carried out after multiplication Match somebody with somebody, obtain the (n-1)th scale layer after final iteration and refer to disparity map
(7) judge n-1 value, match and terminate if n-1=0, it is right if n-1 ≠ 0Up-sampled, and will up-sampling Result afterwards is denoted asScale layer numbering n is subtracted 1 simultaneously；
(8) repeat step (4)-(7) are until n-1=0.
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CN107564044A (en) * 2017-07-14 2018-01-09 天津大学 The Stereo Matching Algorithm of adaptive weighting polymerization based on parallax information
CN107564044B (en) * 2017-07-14 2020-08-14 天津大学 Stereo matching method based on parallax information and adaptive weight aggregation
CN109887021A (en) * 2019-01-19 2019-06-14 天津大学 Based on the random walk solid matching method across scale
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