CN109544611B - Binocular vision stereo matching method and system based on bit characteristics - Google Patents
Binocular vision stereo matching method and system based on bit characteristics Download PDFInfo
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Abstract
A binocular vision stereo matching method and system based on bit features comprises the steps of image acquisition, bit feature calculation, matching cost function calculation and optimal disparity value calculation. When the robustness of the matching cost is improved, the bit features of pixels in the peripheral area of the pixel points are calculated, so that a matching cost function with higher robustness can be constructed after the 1bit feature and the 2bit feature are combined, and the optimal parallax value of the pixel points is calculated according to the matching cost function.
Description
Technical Field
The invention relates to the field of binocular stereo vision, in particular to a binocular vision stereo matching method and system based on bit characteristics.
Background
It is known that light from a scene is collected in a human eye, a sophisticated imaging system, and is transmitted through a neural center to a brain containing hundreds of millions of neurons to be processed in parallel, thereby obtaining real-time, high-definition, accurate depth perception information. This enables the human adaptability to the environment to be greatly improved, and many complex actions can be completed: such as walking, sports, driving vehicles, and performing scientific experiments.
Computer vision is just the discipline of using a computer to simulate the human visual system in order to recover a 3D image from two planar images acquired. Currently, the level of computer stereo vision is far from the level of human binocular vision, and thus its research is still a very active neighborhood.
Binocular Stereo Vision (Binocular Stereo Vision) is an important form of computer Vision, and is a method for acquiring three-dimensional geometric information of an object by acquiring two images of the object to be measured from different positions by using imaging equipment based on a parallax principle and calculating position deviation between corresponding points of the images. Therefore, the real world is processed through the visual system of the simulator, the perception capability of the computer or the robot to the environment can be greatly enhanced for the research of stereo vision matching, the robot can better adapt to the environment and is more intelligent, and people can be better served. Through technical development for many years, binocular stereo vision has been applied in the neighborhoods of robot vision, aerial surveying and mapping, reverse engineering, military application, medical imaging, industrial detection and the like.
Currently, binocular stereo vision integrates images obtained by two image capturing devices and observes the difference between the images, so that a computer can obtain accurate depth information, establish the corresponding relation between features, and correspond mapping points of the same spatial physical point in different images, and the difference is generally called parallax (disparity). However, the most important and difficult problem in binocular stereo vision is stereo vision matching, i.e. finding matching corresponding points from different viewpoint images.
Disclosure of Invention
In view of this, the technical problem mainly solved by the present invention is how to find matching corresponding points from different viewpoint images to improve the accuracy of binocular vision stereo matching. In order to solve the technical problems, the application provides a binocular vision stereo matching method and system based on bit characteristics.
According to a first aspect, an embodiment provides a binocular vision stereo matching method based on a bit feature, including the following steps:
acquiring a first image and a second image under two viewpoints;
obtaining the bit characteristics of any pixel point in the first image and the bit characteristics of a plurality of pixel points in the second image;
constructing a matching cost function corresponding to the pixel point in the first image according to the bit characteristics of the pixel point in the first image and the bit characteristics of the pixel points in the second image;
and calculating the optimal parallax value of the pixel point in the first image according to the matching cost function.
The obtaining the bit feature of any pixel point in the first image comprises:
for any pixel point IL (y, x) in the first image, constructing a neighborhood B of the pixel point IL (y, x)1(y, x) such that
B1(y,x)＝{I1(i,j),i∈(y-b,y+b),j∈(x-b,x+b)}
Wherein, I1(i, j) is neighborhood B1In any pixel point in (y, x), i and y are row coordinates of the pixel point, j and x are column coordinates of the pixel point, and B is neighborhood B1Radius of (y, x);
according to neighborhood B1(y, x) constructing 1bit characteristic and 2bit characteristic of pixel point IL (y, x), and respectively expressing the characteristics as
Wherein, B1 1(y,x)、B2 1(y, x) are respectively the 1bit characteristic and 2bit characteristic of the pixel point IL (y, x), mu 'and sigma' are respectively the neighborhoods B1The mean and variance of the gray levels of (y, x).
The obtaining bit characteristics of a plurality of pixel points in the second image comprises:
for a plurality of pixel points IR (y, x-d) in the second image, constructing a neighborhood B of the plurality of pixel points IR (y, x-d)2(y, x-d) in such a manner that
B2(y,x-d)＝{I2(i,j),i∈(y-b,y+b),j∈(x-d-b,x-d+b)}
Wherein, I2(i, j) is neighborhood B2(y, x-d), d is the parallax value of the pixel in the row direction and belongs to {0,1max}，dmaxIs a preset maximum disparity value;
according to neighborhood B2(y, x-d) construction of 1bit feature of pixel point IR (y, x-d)And 2bit characteristics, respectively formulated as
Wherein, B1 2(y,x-d)、B2 2(y, x-d) 1bit and 2bit features of the pixel IR (y, x-d), respectively, with μ ", σ" being neighborhoods B, respectively2The mean and variance of the gray levels of (y, x-d).
The constructing a matching cost function corresponding to the pixel point in the first image according to the bit characteristics of the pixel point in the first image and the bit characteristics of the pixel points in the second image comprises:
according to 1bit characteristic B1 1(y,x)、B1 2(y, x-d) and 2bit feature B2 1(y,x-d)、、B2 2(y, x-d) calculating an estimate of the cost between the pixel IL (y, x) and the pixels IR (y, x-d)
Carrying out weighted average calculation on the cost estimation value c (y, x, d) to obtain a matching cost function corresponding to the pixel point IL (y, x)
Wherein M, N are neighborhoods B respectively1(y, x) or neighborhood B2The number of row pixels and the number of column pixels of (y, x-d).
The calculating the optimal parallax value of the pixel point in the first image according to the matching cost function includes:
in the value range {0, 1.. multidot., d of parallax dmaxAnd (4) calculating a matching cost function C (y, x, d) in the system, obtaining the parallax with the minimum function value, and taking the parallax as the optimal parallax value d.
Constructing neighborhood B of pixel point IL (y, x)1After (y, x), the method further comprises: for neighborhood B1Performing band-pass filtering processing on each pixel point in (y, x), and calculating to obtain neighborhood B according to the band-pass filtering processing result2A gray mean μ 'and a gray variance σ' of (y, x-d);
constructing neighborhood B of a plurality of pixel points IR (y, x-d)2After (y, x-d), the method further comprises the following steps: for neighborhood B2Performing band-pass filtering processing on each pixel point in (y, x-d), and calculating to obtain neighborhood B according to the band-pass filtering processing result2The mean μ "and variance σ" of gray levels of (y, x-d);
the band-pass filtering process comprises one or more of color intensity filtering, gray intensity filtering, and gradient filtering of the pixel points.
According to a second aspect, an embodiment provides an image stereo matching method, including the following steps:
acquiring images of at least two viewpoints;
and performing stereo matching on each pixel point in one image by the binocular vision stereo matching method in the first aspect to respectively obtain the optimal parallax value of each pixel point.
According to a third aspect, an embodiment provides a binocular vision stereo matching system based on bit features, including:
a memory for storing a program;
a processor for implementing the method described in the first aspect by executing the program stored by the memory.
According to a fourth aspect, an embodiment provides an image stereo matching system, comprising:
a memory for storing a program;
a processor for implementing the method of the second aspect by executing the program stored in the memory.
According to a fifth aspect, an embodiment provides a computer-readable storage medium, characterized in that it comprises a program which is executable by a processor to implement the method of the first or second aspect.
The beneficial effect of this application is:
according to the binocular vision stereo matching method and system based on the bit features, the method comprises the steps of image acquisition, bit feature calculation, matching cost function calculation and optimal parallax value calculation. When the robustness of the matching cost is improved, the bit characteristics of pixels in the peripheral region (namely, the neighborhood) of the pixel point are calculated, so that a matching cost function with higher robustness can be formed after the 1bit characteristic and the 2bit characteristic are combined, and the optimal parallax value of the pixel point is calculated according to the matching cost function.
Drawings
FIG. 1 is a flow chart of a binocular vision stereo matching method;
FIG. 2 is a flow chart of calculating bit features and matching cost functions;
FIG. 3 is a flow chart of a method of image stereo matching;
fig. 4 is a schematic diagram of a stereo matching system.
Detailed Description
The present invention will be described in further detail with reference to the following detailed description and accompanying drawings. Wherein like elements in different embodiments are numbered with like associated elements. In the following description, numerous details are set forth in order to provide a better understanding of the present application. However, those skilled in the art will readily recognize that some of the features may be omitted from the various embodiments, or may be replaced with other elements, materials, methods. In some cases, operations related to the present application are not shown or described in the specification, so as to avoid the core part of the present application being overwhelmed by excessive description, and it is not necessary for those skilled in the art to describe these related operations in detail, so that they can fully understand the related operations according to the description in the specification and the general technical knowledge of the present field.
Furthermore, the features, operations, or characteristics described in the specification may be combined in any suitable manner to form various embodiments. Also, various steps or actions in the description of the method may be transposed or transposed in order, as will be apparent to one of ordinary skill in the art. Thus, the various sequences in the specification and drawings are for the purpose of describing certain embodiments only and are not intended to imply a required sequence unless otherwise indicated where such sequence must be followed.
The numbering of the components as such, e.g., "first", "second", etc., is used herein only to distinguish the objects as described, and does not have any sequential or technical meaning. The term "connected" and "coupled" when used in this application, unless otherwise indicated, includes both direct and indirect connections (couplings).
In binocular vision stereo matching, a key problem is to find matching points in left and right images to obtain the horizontal position difference of corresponding pixels in the two images, which is also called as parallax, so that the depth of the pixel point can be further calculated.
Pixel points which are not at the same depth can have the same color, texture, gradient and the like, so that the pixel points often cause the occurrence of mismatching during stereo matching, thereby further causing great error in parallax calculation and greatly influencing the application of binocular vision in depth measurement. To overcome this problem, in the existing stereo matching method for binocular images, the pixel points in the peripheral region of the pixel points are generally used to estimate the pixel points, for example, the problem of finding a matching block in motion search of video coding, and the matching problem of directly solving sad (absolute difference) are solved. However, in these methods, the weighting of the matching cost of the pixel can still only be calculated by using the above features having no direct relation with parallax, such as color, texture, and gradient, and therefore these methods have great robustness. In order to improve the robustness of the matching cost, the method is based on the technology of the prior method, the bit feature of the pixel in the peripheral region (namely the neighborhood) of the pixel point is calculated, and the 1bit feature and the 2bit feature can be combined to form the matching cost function with higher robustness, so that the optimal parallax value of the pixel point is calculated according to the matching cost function, the problem of mismatching during stereo matching can be effectively solved, the matching corresponding point can be accurately found in different viewpoint images, and the precision of stereo matching can be improved.
The first embodiment is as follows:
referring to fig. 1, the present application discloses a binocular vision stereo matching method based on bit feature, which includes steps S110-S140, which are described below respectively.
Step S110, a first image and a second image under two viewpoints are acquired. In one embodiment, the stereo matching object is imaged by a binocular camera, and since the binocular camera forms two imaging viewpoints, an image acquired by one of the cameras may be referred to as a first image IL, and an image acquired by the other camera may be referred to as a second image IR.
Step S120, the bit characteristics of any pixel point in the first image and the bit characteristics of a plurality of pixel points in the second image are obtained. In one embodiment, see FIG. 2, the step S120 may include steps S121-S124, which are described below.
Step S121, for any pixel point IL (y, x) in the first image IL, constructing a neighborhood B of the pixel point IL (y, x)1(y, x) such that
B1(y,x)＝{I1(i,j),i∈(y-b,y+b),j∈(x-b,x+b)} (1-1)
Wherein, I1(i, j) is neighborhood B1In any pixel point in (y, x), i and y are row coordinates of the pixel point, j and x are column coordinates of the pixel point, and B is neighborhood B1Radius of (y, x).
Similarly, for a number of pixels IR (y, x-d) in the second image IR, a neighborhood B of the number of pixels IR (y, x-d) is constructed2(y, x-d) in such a manner that
B2(y,x-d)＝{I2(i,j),i∈(y-b,y+b),j∈(x-d-b,x-d+b)} (1-2)
Wherein, I2(i, j) is neighborhood B2(y, x-d) of arbitrary pixelsPoint, d is the parallax value of the pixel point in the row direction and d is belonged to {0,1max}，dmaxIs a preset maximum disparity value.
It should be noted that, in the present embodiment, when searching for a matching point in the left and right images, the defined parallax is a horizontal position difference of corresponding pixels in the two images, and therefore, when searching for a pixel corresponding to the pixel IL (y, x) in the first image IL in the second image IR, the pixel should be searched for at the horizontal position of the second image IR, and some pixels IR (y, x-d) at the horizontal position are obtained.
In step S122, in order to improve the quality of the first image and the second image and obtain a bit feature with good robustness, in this embodiment, filtering processing is performed on pixels in the field.
In one embodiment, a neighborhood B of pixel point IL (y, x) is constructed1After (y, x), the method further comprises: for neighborhood B1Performing band-pass filtering processing on each pixel point in (y, x), and calculating to obtain neighborhood B according to the band-pass filtering processing result2The gray mean μ 'and the gray variance σ' of (y, x-d).
In another embodiment, a neighborhood B of several pixel points IR (y, x-d) is constructed2After (y, x-d), the method further comprises the following steps: for neighborhood B2Performing band-pass filtering processing on each pixel point in (y, x-d), and calculating to obtain neighborhood B according to the band-pass filtering processing result2The gray mean μ "and the gray variance σ" of (y, x-d).
It should be noted that the band-pass filtering process herein includes one or more of color intensity filtering, gray intensity filtering, and gradient filtering of the pixel, and since the band-pass filtering process is a prior art, detailed description thereof is not repeated here.
Step S123, according to the neighborhood B1(y, x) constructing 1bit characteristic and 2bit characteristic of pixel point IL (y, x), and respectively expressing the characteristics as
Wherein, B1 1(y,x)、B2 1(y, x) are respectively the 1bit characteristic and 2bit characteristic of the pixel point IL (y, x), mu 'and sigma' are respectively the neighborhoods B1The mean and variance of the gray levels of (y, x).
It should be noted that, the so-called 1Bit feature is to perform band-pass filtering on the field, then calculate the mean value of the field, and make its gray value 1 for the pixel points greater than the mean value; for the pixel point less than the mean value, the gray value of the pixel point is 0.
Step S124, according to the neighborhood B2(y, x-d) constructing 1bit characteristic and 2bit characteristic of IR (y, x-d) of pixel point, and respectively formulating as
Wherein, B1 2(y,x-d)、B2 2(y, x-d) 1bit and 2bit features of the pixel IR (y, x-d), respectively, with μ ", σ" being neighborhoods B, respectively2The mean and variance of the gray levels of (y, x-d).
Step S130, constructing a matching cost function corresponding to the pixel point IL (y, x) in the first image according to the bit characteristics of the pixel point IL (y, x) in the first image and the bit characteristics of the plurality of pixel points IR (y, x-d) in the second image. In one embodiment, see FIG. 2, the step S130 may include steps S131-S132, which are described below.
Step S131, according to the 1bit characteristic B1 1(y,x)、B1 2(y, x-d) and 2bit feature B2 1(y,x-d)、、B2 2(y, x-d) calculating an estimate of the cost between the pixel IL (y, x) and the pixels IR (y, x-d)
Wherein the operator
Representing an exclusive or operation; b is1 1(y, x, I, j) represents pixel point I in the neighborhood of pixel point IL (y, x)1(i, j) a 1bit feature; b is1 2(y, x-d, I, j) represents pixel point I in the neighborhood of pixel point IR (y, x-d)2(i, j) a 1bit feature; b is2 1(y, x, I, j) represents pixel point I in the neighborhood of pixel point IL (y, x)1(ii) a 2bit feature of (i, j); b is2 2(y, x-d, I, j) represents pixel point I in the neighborhood of pixel point IR (y, x-d)2(i, j) 2bit feature.
Step S132, carrying out weighted average calculation on the cost estimation value c (y, x, d) to obtain a matching cost function corresponding to the pixel point IL (y, x)
Wherein M, N are neighborhoods B respectively1(y, x) or neighborhood B2The number of row pixels and the number of column pixels of (y, x-d).
Step S140, calculating an optimal disparity value of the pixel point IL (y, x) in the first image according to the matching cost function C (y, x, d).
In one embodiment, the range of values for the disparity d is {0, 1., dmaxAnd (4) calculating a matching cost function C (y, x, d) in the system, obtaining the parallax with the minimum function value, and taking the parallax as the optimal parallax value d.
Correspondingly, the application also discloses a binocular vision stereo matching system 30 based on the bit characteristic. Referring to fig. 4, the system includes a memory 301 and a processor 302, wherein the memory 301 is used for storing programs, and the processor 302 is used for implementing the method described in steps S110-S140 by executing the programs stored in the memory 301.
Example two:
on the basis of the binocular vision stereo matching method in the first embodiment, the present embodiment further provides an image stereo matching method, please refer to fig. 3, which includes steps S210 to S220, which are described below.
In step S210, images of at least two viewpoints are acquired. In one embodiment, the stereo matching object may be imaged by a plurality of cameras, such that images from a plurality of viewpoints may be obtained.
Step S220, performing stereo matching on each pixel point in one of the images by using the binocular vision stereo matching method according to the embodiment, and obtaining an optimal disparity value of each pixel point respectively.
Those skilled in the art can understand that the binocular vision stereo matching method in the first embodiment obtains the optimal disparity value of one pixel point in an image, and a matching corresponding point in another image can be found according to the optimal disparity value, so that the optimal disparity values of all pixel points in the image can be continuously calculated according to the method, and thus, one-to-one stereo matching of the pixel points between two or more images can be realized, and further, the effect of stereo matching of the images is achieved.
Accordingly, the present application also discloses an image stereo matching system (which can be illustrated by the system 30 in fig. 4). Referring to fig. 4, the system includes a memory 301 and a processor 302, wherein the memory 301 is used for storing programs, and the processor 302 is used for implementing the method described in steps S210-S220 by executing the programs stored in the memory 301.
Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments may be implemented by hardware, or may be implemented by computer programs. When all or part of the functions of the above embodiments are implemented by a computer program, the program may be stored in a computer-readable storage medium, and the storage medium may include: a read only memory, a random access memory, a magnetic disk, an optical disk, a hard disk, etc., and the program is executed by a computer to realize the above functions. For example, the program may be stored in a memory of the device, and when the program in the memory is executed by the processor, all or part of the functions described above may be implemented. In addition, when all or part of the functions in the above embodiments are implemented by a computer program, the program may be stored in a storage medium such as a server, another computer, a magnetic disk, an optical disk, a flash disk, or a removable hard disk, and may be downloaded or copied to a memory of a local device, or may be version-updated in a system of the local device, and when the program in the memory is executed by a processor, all or part of the functions in the above embodiments may be implemented.
The present invention has been described in terms of specific examples, which are provided to aid understanding of the invention and are not intended to be limiting. For those skilled in the art to which the invention pertains, several simple deductions, modifications or substitutions may be made according to the idea of the invention.
Claims (8)
1. A binocular vision stereo matching method based on bit features is characterized by comprising the following steps:
acquiring a first image and a second image under two viewpoints;
obtaining the bit characteristic of any pixel point in the first image, including: for any pixel point IL (y, x) in the first image, constructing a neighborhood B of the pixel point IL (y, x)1(y, x) such that B1(y,x)＝{I1(I, j), I ∈ (y-b, y + b), j ∈ (x-b, x + b) }, wherein I1(i, j) is neighborhood B1In any pixel point in (y, x), i and y are row coordinates of the pixel point, j and x are column coordinates of the pixel point, and B is neighborhood B1Radius of (y, x); according to neighborhood B1(y, x) constructing 1bit characteristic and 2bit characteristic of pixel point IL (y, x), and respectively expressing the characteristics as
Wherein, B1 1(y,x)、B2 1(y, x) are respectively the 1bit characteristic and 2bit characteristic of the pixel point IL (y, x), mu 'and sigma' are respectively the neighborhoods B1(y, x) mean and variance of gray levels;
obtaining bit characteristics of a plurality of pixel points in the second image, including: for a plurality of pixel points IR (y, x-d) in the second image, constructing a neighborhood B of the plurality of pixel points IR (y, x-d)2(y, x-d) so that B2(y,x-d)＝{I2(I, j), I ∈ (y-b, y + b), j ∈ (x-d-b, x-d + b) }, wherein I2(i, j) is neighborhood B2(y, x-d), d is the parallax value of the pixel in the row direction and belongs to {0,1max}，dmaxIs a preset maximum disparity value; according to neighborhood B2(y, x-d) constructing 1bit characteristic and 2bit characteristic of IR (y, x-d) of pixel point, and respectively formulating as
Wherein, B1 2(y,x-d)、B2 2(y, x-d) 1bit and 2bit features of the pixel IR (y, x-d), respectively, with μ ", σ" being neighborhoods B, respectively2(y, x-d) mean and variance of gray levels;
constructing a matching cost function corresponding to the pixel point in the first image according to the bit characteristics of the pixel point in the first image and the bit characteristics of the pixel points in the second image;
and calculating the optimal parallax value of the pixel point in the first image according to the matching cost function.
2. The binocular vision stereo matching method of claim 1, wherein the constructing of the matching cost function corresponding to the pixel point in the first image according to the bit feature of the pixel point in the first image and the bit features of the pixel points in the second image comprises:
according to 1bit characteristic B1 1(y,x)、B1 2(y, x-d) and 2bit feature B2 1(y,x-d)、、B2 2(y, x-d) calculating an estimate of the cost between the pixel IL (y, x) and the pixels IR (y, x-d)
Carrying out weighted average calculation on the cost estimation value c (y, x, d) to obtain a matching cost function corresponding to the pixel point IL (y, x)
Wherein M, N are neighborhoods B respectively1(y, x) or neighborhood B2The number of row pixels and the number of column pixels of (y, x-d).
3. The binocular vision stereo matching method of claim 2, wherein the calculating the optimal disparity value of the pixel point in the first image according to the matching cost function comprises:
in the value range {0, 1.. multidot., d of parallax dmaxAnd (4) calculating a matching cost function C (y, x, d) in the system, obtaining the parallax with the minimum function value, and taking the parallax as the optimal parallax value d.
4. The binocular vision stereo matching method of claim 1,
constructing neighborhood B of pixel point IL (y, x)1After (y, x), the method further comprises: for neighborhood B1Performing band-pass filtering processing on each pixel point in (y, x), and calculating to obtain neighborhood B according to the band-pass filtering processing result2Mean value of gray levels μ' and gray of (y, x-d)Degree variance σ';
constructing neighborhood B of a plurality of pixel points IR (y, x-d)2After (y, x-d), the method further comprises the following steps: for neighborhood B2Performing band-pass filtering processing on each pixel point in (y, x-d), and calculating to obtain neighborhood B according to the band-pass filtering processing result2The mean μ "and variance σ" of gray levels of (y, x-d);
the band-pass filtering process comprises one or more of color intensity filtering, gray intensity filtering, and gradient filtering of the pixel points.
5. An image stereo matching method is characterized by comprising the following steps:
acquiring images of at least two viewpoints;
the binocular vision stereo matching method of any one of claims 1 to 4, wherein stereo matching is performed on each pixel point in one image, and the optimal disparity value of each pixel point is obtained respectively.
6. The utility model provides a binocular vision stereo matching system based on bit characteristic which characterized in that includes:
a memory for storing a program;
a processor for implementing the method of any one of claims 1-4 by executing a program stored by the memory.
7. An image stereo matching system, comprising:
a memory for storing a program;
a processor for implementing the method as claimed in claim 5 by executing the program stored by the memory.
8. A computer-readable storage medium, characterized by comprising a program executable by a processor to implement the method of any one of claims 1-5.
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