CN113011498B - Feature point extraction and matching methods, systems and media based on color images - Google Patents

Abstract

The application discloses a characteristic point extraction and matching method, a characteristic point extraction and matching system and a characteristic point extraction and matching medium based on a color image, wherein the characteristic point extraction and matching method comprises the following steps: acquiring a plurality of color images, converting the color images into gray level images and HSV images, and constructing a gray level image pyramid and an HSV color image pyramid; extracting key points from the gray level map of each layer of the gray level image pyramid, and acquiring the direction of each key point; acquiring descriptors corresponding to the key points according to the HSV color image pyramid, and acquiring feature points according to the key points and the descriptors; after the feature points are obtained, feature point matching is carried out on two color images in the plurality of color images. The application can make the characteristic points contain the color information of the image by acquiring the descriptors, improves the information richness of the characteristic points, and can be widely applied to the field of computer vision.

Description

Feature point extraction and matching methods, systems and media based on color images

Technical field

The invention relates to the field of computer vision, and in particular to a feature point extraction and matching method, system and medium based on color images.

Background technique

The extraction and matching of image feature points plays a very important role in the field of computer vision. It is widely used in face recognition, target recognition and tracking, image registration and correction, visual SLAM and three-dimensional reconstruction. Feature points refer to some representative areas in the image. These areas contain information related to computing tasks. Therefore, the selection of feature points will directly affect the later computing tasks. The selection and design of image feature points usually need to meet the following properties: 1. Repeatability (Repeatability), that is, the same feature point can be extracted in multiple similar images; 2. Distinguishability (Distinguishability), That is, different feature points have different expressions and are easy to distinguish; 3. Locality, that is, a certain feature point is only related to a certain small area in the image; 4. Efficiency, that is, the number of feature points is moderate And the extraction and matching time is short. According to different extraction strategies, feature points are mainly divided into point features, line features and surface features. Since it is relatively difficult to extract line features and surface features, current image feature point extraction mainly uses point features.

At present, most feature extraction and description algorithms use grayscale images as processing objects. Although this can reduce the impact of illumination changes to a certain extent and improve the speed of feature extraction, it ignores the color information of the image. We live in a colorful world, and most images contain rich color information. This color information is of great help to the extraction and matching of image feature points. Therefore, the feature extraction and matching algorithm of color images is of great help. Very practical significance.

Contents of the invention

In order to solve one of the technical problems existing in the prior art at least to a certain extent, the purpose of the present invention is to provide a feature point extraction and matching method, system and medium based on color images.

The technical solution adopted by the present invention is:

A feature point extraction and matching method based on color images, including the following steps:

Obtain multiple color images, convert the color images into grayscale images and HSV images, and construct a grayscale image pyramid and an HSV color image pyramid;

Extract key points from the grayscale image of each layer of the grayscale image pyramid, and obtain the direction of each key point;

Obtain descriptors corresponding to the key points according to the HSV color image pyramid, and obtain feature points according to the key points and the descriptors;

After obtaining the feature points, perform feature point matching on two color images among the multiple color images.

Further, extracting key points from the grayscale image of each layer of the grayscale image pyramid includes:

Divide the grayscale image of each layer of the grayscale image pyramid into n regions;

Use the FAST-N0 algorithm to extract a preset number of key points in each of the regions;

The extracted key points are screened using the quadtree splitting method to remove edge effects.

Further, the key point is determined by comparing the gray value of the pixel point P and the pixel points adjacent to the pixel point P;

The comparison formula is:

Among them, I _i is the gray value of the pixel point adjacent to the pixel point P. If N>N ₀ , the pixel point P is considered to be a key point, and N ₀ and T are both threshold values.

Further, obtaining the direction of each key point includes:

Using the key point as the center, obtain a disk area with a radius of r pixels;

Calculate the gray centroid C of the disk area;

If the grayscale centroid C does not coincide with the geometric center O of the disk area, then the direction angle θ of the key point is given by the vector express;

Among them, the expression of the grayscale centroid C is:

The expression of direction angle θ is:

θ=atan2(m ₀₁ , m ₁₀ ).

Further, the descriptor is a BRIEF-32 descriptor, the BRIEF-32 descriptor is a 256-bit binary vector, and each bit in the binary vector is determined by the color similarity of any two pixel blocks in the circular area. ;

The circular area is centered on the key point and has a radius of m pixels;

The pixel block is an area obtained in the circular area in a preset manner.

Further, the BRIEF-32 descriptor is obtained in the following way:

256 pairs of pixel points are obtained in the circular area, the coordinates of each pixel point are (xi _, y _i ), i=1, 2,..., 512, forming a matrix D:

In order to ensure the rotation invariance of the descriptor, the matrix D is rotated by the direction angle θ of the key point:

D _θ =R _θ D

Among them, R _θ is the rotation matrix of the direction angle θ of the key point:

D _θ is a matrix composed of the coordinates of the pixel points after rotation. Assume that the coordinates of a pair of pixel points are (x′ _i1 , y′ _i1 ), (x′ _i2 , y′ _i2 ), and the i-th coordinate of the descriptor. Bit Des _i corresponds;

Calculate the pixel average value of the pixel patch with (x′ _i1 , y′ _i1 ), (x′ _i2 , y′ _i2 ) as the center and w pixels as the radius in the three monochromatic channel images of HSV, and calculate Methods as below:

Calculate the color similarity of these two pixel blocks:

Among them, Cdist _i is the color difference, Bdist _i is the brightness difference;

Then the i-th bit Des _i of the descriptor is defined as:

Among them, ε _c and ε _B are the thresholds of color difference and brightness difference respectively. When Cdist _i and Bdist _i are both less than the threshold, it means that the colors of the two pixel blocks are similar, and the corresponding bit value of the descriptor is 1. Otherwise, it means that the colors of the two pixel blocks are similar. Different, the corresponding bit of the descriptor has a value of 0;

After operating on 256 pairs of pixels, a 256-bit binary vector is obtained as the R-BRIEF descriptor.

Further, the grayscale image is obtained by the following formula:

I _Gray =(I _R *30+I _G *59+I _B *11+50)/100

Among them, I _Gray , I _R , I _G , and I _R are the grayscale image and the pixel values of the three channels of R, G, and B respectively;

Feature point matching is performed on two color images, including:

Use brute force matching method to match feature points of two color images;

The K nearest neighbor algorithm is used to filter out false matches, and the random sampling consistency algorithm is used to remove false matches.

Another technical solution adopted by the present invention is:

A feature point extraction and matching system based on color images, including:

An image conversion module, used to acquire multiple color images, convert the color images into grayscale images and HSV images, and construct a grayscale image pyramid and an HSV color image pyramid;

A key point extraction module, used to extract key points from the grayscale image of each layer of the grayscale image pyramid, and obtain the direction of each key point;

A descriptor acquisition module, configured to acquire descriptors corresponding to the key points according to the HSV color image pyramid, and obtain feature points according to the key points and the descriptors;

The feature point matching module is used to perform feature point matching on two color images among multiple color images after obtaining the feature points.

Another technical solution adopted by the present invention is:

A feature point extraction and matching system based on color images, including:

at least one processor;

At least one memory for storing at least one program;

When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

Another technical solution adopted by the present invention is:

A storage medium in which a processor-executable program is stored, and the processor-executable program is used to perform the method as described above when executed by the processor.

The beneficial effects of the present invention are: by obtaining the descriptor, the present invention causes the feature points to contain the color information of the image, thereby improving the information richness of the feature points.

Description of the drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following is an introduction to the accompanying drawings of the embodiments of the present invention or the relevant technical solutions in the prior art. It should be understood that the drawings in the following introduction are only In order to facilitate and clearly describe some embodiments of the technical solutions of the present invention, those skilled in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 is a step flow chart of an ORB feature point extraction and matching method based on color images in an embodiment of the present invention;

Figure 2 is a schematic diagram of key point extraction in the embodiment of the present invention;

Figure 3 is a schematic diagram of key point screening using the quadtree splitting method in an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and cannot be understood as limiting the present invention. The step numbers in the following embodiments are only set for the convenience of explanation. The order between the steps is not limited in any way. The execution order of each step in the embodiments can be adapted according to the understanding of those skilled in the art. sexual adjustment.

In the description of the present invention, it should be understood that orientation descriptions, such as up, down, front, back, left, right, etc., are based on the orientation or position relationships shown in the drawings and are only In order to facilitate the description of the present invention and simplify the description, it is not intended to indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore should not be construed as a limitation of the present invention.

In the description of the present invention, several means one or more, plural means two or more, greater than, less than, more than, etc. are understood to exclude the original number, and above, below, within, etc. are understood to include the original number. If there is a description of first and second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of indicated technical features or implicitly indicating the order of indicated technical features. relation.

In the description of the present invention, unless otherwise explicitly limited, words such as setting, installation, and connection should be understood in a broad sense. Those skilled in the art can reasonably determine the specific meaning of the above words in the present invention in combination with the specific content of the technical solution.

As shown in Figure 1, this embodiment provides a color image-based ORB feature point extraction and matching method, which includes the following steps:

S1. Acquire multiple color images, convert the color images into grayscale images and HSV images, and build a grayscale image pyramid and an HSV color image pyramid.

Convert RGB images to grayscale and HSV images, and build image pyramids respectively. By establishing an image pyramid, feature points (feature points are composed of key points and descriptors) are extracted at each layer of the pyramid, thereby forming a scale space and ensuring the scale invariance of the feature points.

The grayscale image is obtained according to the following formula:

I _Gray =(I _R *30+I _G *59+I _B *11+50)/100

Among them, I _Gray , _IR , I _G , and _IR are the grayscale image and the pixel values of the three channels of R, G, and B respectively.

S2. Extract key points from the grayscale image of each layer of the grayscale image pyramid.

FAST key points are extracted from each layer of the grayscale image pyramid; FAST key points are corner points that are determined by comparing the gray value of a certain pixel point with its adjacent points.

As shown in Figure 2, taking a certain pixel point p in the grayscale image (let its grayscale value be I _p ) as the center of the circle, there are a total of 16 pixels on the circle with a radius of 3 pixel units. Let its grayscale The degree value is I _i (i=1, 2,...,16):

in:

In this embodiment, the threshold value T=0.2I _p is taken. If N>N ₀ , point p is considered as the key point. N ₀ is usually 12 or 9. In this embodiment, N ₀ =9 is taken.

In order to reduce the edge effect, the feature points should be distributed evenly throughout the image as much as possible. Before extracting the key points, the grayscale image should be divided into multiple small areas of 30*30, and features should be extracted in each small area. point. Assume that a total of M ₀ feature points are extracted from the entire image, and the expected number of extracted feature points is M ₁ , then the condition M ₀ > M ₁ should be met.

After the key points are extracted, the quadtree splitting method is used to screen the extracted feature points. As shown in Figure 3, the entire image is first used as the root node for quadtree splitting. At the end of each split, the number of feature points in each leaf node is counted: if the number of feature points in a certain leaf node is zero, then the number of feature points in a certain leaf node is zero. The leaf node stops splitting; if the number of feature points in a certain leaf node is 1, then the leaf node stops splitting, and the leaf node counter _M2 is increased by 1; if there are more than 1 feature points in a certain leaf node, then the leaf node The leaf node continues the next split until the condition M ₂ > M ₁ is met to stop all splits. Finally, the Non-Maximum Suppression (NMS) algorithm is used to retain an optimal feature point in each leaf node and delete other redundant feature points.

S3. Obtain the direction of each key point.

The grayscale centroid method is used to calculate the direction for each key point. The calculation method is as follows:

With the key point as the center, select a disk area Patch with a radius of r pixels, and calculate the center point with the gray value of the image block as the weight, that is, the gray centroid C:

in,

Assuming that the gray-scale centroid C and the geometric center O of the disk do not coincide, the direction of the key point can be determined by the vector The direction angle θ represents:

θ=atan2(m ₀₁ , m ₁₀ ).

S4. Obtain descriptors corresponding to key points based on the HSV color image pyramid, and obtain feature points based on key points and descriptors.

The R-BRIEF descriptor of each key point is calculated in the HSV color space. Each bit in the descriptor is determined based on the color similarity of any two pixel blocks around the key point. The calculation process is as follows:

First, Gaussian smoothing is performed on the images of the three channels of HSV to reduce the impact of noise on the feature descriptor. The Gaussian filter window size is set to 9*9 pixel blocks, and the variance is set to 2.

This embodiment selects the BRIEF-32 descriptor, that is, a 256-bit binary vector is used to describe a feature point. In an image block of 31*31 pixels with the key point as the center, 256 pairs of pixels are selected using machine learning, and the coordinates of each pixel are set to (x _i , y _i ), i=1, 2,... ., 512, forming matrix D:

In order to ensure the rotation invariance of the feature point descriptor, the D matrix needs to be rotated at the direction angle θ of the feature point:

D _θ =R _θ D

where R _θ is the rotation matrix of the direction angle θ of the feature point:

D _θ is a matrix composed of the coordinates of the pixel points after rotation. Assume that the coordinates of a pair of pixel points are (x′ _i1 , y′ _i1 ), (x′ _i2 , y′ _i2 ), and the i-th coordinate of the descriptor. Bit Des _i corresponds. Calculate the average pixel value of the disk-shaped pixel patch with a center of (x′ _i1 , y′ _i1 ), (x′ _i2 , y′ _i2 ) and a radius of 2 pixels in the three monochromatic channel images of HSV. , the calculation method is as follows:

Calculate the color similarity of these two pixel blocks:

Among them, Cdist _i is the color difference, and Bdist _i is the brightness difference.

Then the i-th bit Des _i of the descriptor is defined according to the following method:

Among them, ε _c and ε _B are the thresholds of color difference and brightness difference respectively. When Cdist _i and Bdist _i are both less than the threshold, it means that the colors of the two pixel blocks are similar, and the corresponding bit value of the descriptor is 1. Otherwise, it means that the colors of the two pixel blocks are similar. Different, the corresponding bit of the descriptor has a value of 0.

After performing the above operation on 256 pairs of pixel points, a 256-bit binary vector can be obtained, which is the R-BRIEF descriptor of the feature point.

S5. After obtaining the feature points, perform feature point matching on two color images among the multiple color images.

The Brute-Froce Matcher method is used to match the feature points of the two images, then the K-Nearest Neighbor (K-NN) algorithm is used to initially filter out false matches, and finally the Random Sampling Consistency Algorithm (RANSAC) is used to further remove false matches.

The brute force matching method used in this embodiment is to determine the degree of similarity between two feature points based on the Hamming distance between descriptors. For each feature point in image _Pi Measure it and each feature point in image P _j /> Hamming distance between them, after sorting, select the nearest feature point as its matching point.

Since feature matching based only on Hamming distance will result in a large number of erroneous matches, and erroneous matching results will have a huge impact on later calculations and processing, it is necessary to filter the matching results. The present invention first uses K nearest neighbors (K -NN) algorithm initially filters out some false matches, that is, the ratio of the closest distance to the next closest distance between two feature point descriptors is used as the basis for judgment. When the ratio is greater than a certain threshold, the matching position is considered to be a correct match, otherwise it is regarded as a correct match. Incorrect matching will be discarded; finally, some local noise points will be removed through the Random Sampling Consistency Algorithm (RANSAC), and the last remaining paired points will be regarded as the correct matching result.

To sum up, compared with the existing technology, this embodiment has the following beneficial effects:

(1) The feature point extraction method proposed in this embodiment uses color images as input. Compared with most existing feature point extraction algorithms based on grayscale images, the feature points extracted by the present invention include the characteristics of the image. Color information improves the information richness of feature points.

(2) This embodiment proposes a new binary feature point description method based on color similarity in HSV color space. The image color information is added to the feature point descriptor without affecting the feature point matching speed. It is more distinguishable and is conducive to feature point matching.

(3) This embodiment improves the existing ORB feature extraction and matching algorithm and improves the noise resistance of feature points.

This embodiment also provides a feature point extraction and matching system based on color images, including:

An image conversion module, used to acquire multiple color images, convert the color images into grayscale images and HSV images, and construct a grayscale image pyramid and an HSV color image pyramid;

A key point extraction module, used to extract key points from the grayscale image of each layer of the grayscale image pyramid, and obtain the direction of each key point;

A descriptor acquisition module, configured to acquire descriptors corresponding to the key points according to the HSV color image pyramid, and obtain feature points according to the key points and the descriptors;

The feature point matching module is used to perform feature point matching on two color images among multiple color images after obtaining the feature points.

The feature point extraction and matching system based on color images in this embodiment can execute the feature point extraction and matching method based on color images provided by the method embodiments of the present invention, and can execute any combination of implementation steps of the method embodiments. , possessing the corresponding functions and beneficial effects of this method.

This embodiment also provides a feature point extraction and matching system based on color images, including:

at least one processor;

At least one memory for storing at least one program;

When the at least one program is executed by the at least one processor, the at least one processor implements the above method.

The feature point extraction and matching system based on color images in this embodiment can execute the feature point extraction and matching method based on color images provided by the method embodiments of the present invention, and can execute any combination of implementation steps of the method embodiments. , possessing the corresponding functions and beneficial effects of this method.

The embodiment of the present application also discloses a computer program product or computer program. The computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the method shown in FIG. 1 .

This embodiment also provides a storage medium that stores instructions or programs that can execute a color image-based feature point extraction and matching method provided by the method embodiment of the present invention. When the instructions or programs are run, Any combination of implementation steps of the method embodiments has the corresponding functions and beneficial effects of the method.

In some alternative embodiments, the functions/operations noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending on the functionality/operations involved. Furthermore, the embodiments presented and described in the flow diagrams of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of a larger operation are performed independently.

Furthermore, although the present invention has been described in the context of functional modules, it should be understood that, unless stated to the contrary, one or more of the described functions and/or features may be integrated in a single physical device and/or or software modules, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion regarding the actual implementation of each module is not necessary to understand the invention. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be within the ordinary skill of an engineer, taking into account the properties, functions and internal relationships of the modules. Accordingly, a person skilled in the art using ordinary skills can implement the invention set forth in the claims without undue experimentation. It will also be understood that the specific concepts disclosed are illustrative only and are not intended to limit the scope of the invention, which is to be determined by the full scope of the appended claims and their equivalents.

If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered a sequenced list of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium, For use by, or in combination with, instruction execution systems, devices or devices (such as computer-based systems, systems including processors or other systems that can fetch instructions from and execute instructions from the instruction execution system, device or device) or equipment. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or more wires (electronic device), portable computer disk cartridges (magnetic device), random access memory (RAM), Read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, and subsequently edited, interpreted, or otherwise suitable as necessary. process to obtain the program electronically and then store it in computer memory.

It should be understood that various parts of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if it is implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a logic gate circuit with a logic gate circuit for implementing a logic function on a data signal. Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), etc.

In the above description of this specification, reference to the description of the terms "one embodiment/example", "another embodiment/example" or "certain embodiments/examples" etc. is meant to be described in connection with the embodiment or example Specific features, structures, materials, or characteristics are included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and purposes of the invention. The scope of the invention is defined by the claims and their equivalents.

The above is a detailed description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can also make various equivalent modifications or substitutions without violating the spirit of the present invention. Equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims (8)

1. The characteristic point extraction and matching method based on the color image is characterized by comprising the following steps of:

acquiring a plurality of color images, converting the color images into gray level images and HSV images, and constructing a gray level image pyramid and an HSV color image pyramid;

extracting key points from the gray level map of each layer of the gray level image pyramid, and acquiring the direction of each key point;

acquiring descriptors corresponding to the key points according to the HSV color image pyramid, and acquiring feature points according to the key points and the descriptors;

after the characteristic points are obtained, matching the characteristic points of two color images in the plurality of color images;

the descriptor is a BRIEF-32 descriptor, the BRIEF-32 descriptor is a 256-bit binary vector, and each bit in the binary vector is determined by the color similarity of any two pixel blocks in a circular area;

the circular area takes a key point as a center and has a radius of m pixels;

the pixel blocks are areas acquired in the circular area according to a preset mode;

the BRIEF-32 descriptor is obtained by:

256 pairs of pixel points are acquired in the circular area, and each pixel point has a coordinate of (x _i ,y _i ) I=1, 2, …,512, constituting matrix D:

in order to ensure the rotation invariance of the descriptors, the matrix D is subjected to rotation transformation according to the direction angle theta of the key points:

D _θ ＝R _θ D

wherein R is _θ A rotation matrix of the direction angle θ that is a key point:

D _θ a matrix of coordinates of the rotated pixel points is set such that a pair of the coordinates of the pixel points are (x' _i1 ,y′ _i1 ),(x′ _i2 ,y′ _i2 ) And Des at the ith bit of the descriptor _i Corresponding to the above;

calculated in HSV three monochromatic channel images respectively to (x' _i1 ,y′ _i1 ),(x′ _i2 ,y′ _i2 ) The pixel average value of the pixel block Patch with w pixels as radius is calculated as follows:

calculating the color similarity of the two pixel blocks:

wherein Cdist _i Bdist as color difference _i Is the brightness difference;

then the ith bit Des of the descriptor _i The definition is as follows:

wherein ε is _c 、ε _B The thresholds of color difference and brightness difference are respectively shown as Cdist _i And Bdist _i When the colors of the two pixel blocks are smaller than the threshold value, the color of the two pixel blocks is similar, the value of the corresponding bit of the descriptor is 1, otherwise, the colors of the two pixel blocks are different, and the value of the corresponding bit of the descriptor is 0;

after 256 pairs of pixel points are operated, a 256-bit binary vector is obtained and used as an R-BRIEF descriptor.

2. The method for extracting and matching feature points based on color images according to claim 1, wherein extracting key points in the gray scale map of each layer of the pyramid of gray scale images comprises:

dividing a gray scale image of each layer of the gray scale image pyramid into n areas;

using FAST-N ₀ The algorithm extracts a preset number of key points from each region;

and screening the extracted key points by adopting a quadtree splitting method to remove edge effects.

3. The method for extracting and matching feature points based on color images according to claim 2, wherein the key points are determined by comparing the gray values of the pixel points P and the pixels adjacent to the pixel points P;

the comparison formula is:

wherein I is _i For the gray value of the pixel adjacent to the pixel P, if N>N ₀ Consider the pixel point P as the key point N ₀ And T are both thresholds.

4. The method for extracting and matching feature points based on color images according to claim 1, wherein the step of obtaining the direction of each key point comprises:

taking the key point as a center to obtain a disc area with the radius of r pixels;

calculating a gray centroid C of the disc region;

if the gray centroid C does not coincide with the geometric center O of the disk region, the direction angle theta of the key point is defined by a vectorA representation;

the expression of the gray centroid C is as follows:

the expression of the direction angle θ is:

θ＝atan2(m ₀₁ ,m ₁₀ )。

5. the color image-based feature point extraction and matching method according to claim 1, wherein the gray scale map is obtained by the following formula:

I _Gray ＝(I _R *30+I _G *59+I _B *11+50)/100

wherein I is _Gray 、I _R 、I _G 、I _R The pixel values of the three channels are respectively a gray level diagram and R, G, B;

performing feature point matching on two color images, including:

carrying out characteristic point matching on the two color images by adopting a violent matching method;

the false matches are filtered using a K-nearest neighbor algorithm and removed using a random sampling consistency algorithm.

6. A color image-based feature point extraction and matching system, comprising:

the image conversion module is used for acquiring a plurality of color images, converting the color images into gray level images and HSV images, and constructing a gray level image pyramid and an HSV color image pyramid;

the key point extraction module is used for extracting key points from the gray level map of each layer of the gray level image pyramid and acquiring the direction of each key point;

the descriptor acquisition module is used for acquiring descriptors corresponding to the key points according to the HSV color image pyramid and acquiring feature points according to the key points and the descriptors;

the characteristic point matching module is used for matching the characteristic points of two color images in the plurality of color images after the characteristic points are obtained;

the descriptor is a BRIEF-32 descriptor, the BRIEF-32 descriptor is a 256-bit binary vector, and each bit in the binary vector is determined by the color similarity of any two pixel blocks in a circular area;

the circular area takes a key point as a center and has a radius of m pixels;

the pixel blocks are areas acquired in the circular area according to a preset mode;

the BRIEF-32 descriptor is obtained by:

256 pairs of pixel points are acquired in the circular area, and each pixel point has a coordinate of (x _i ,y _i ) I=1, 2, …,512, constituting matrix D:

in order to ensure the rotation invariance of the descriptors, the matrix D is subjected to rotation transformation according to the direction angle theta of the key points:

D _θ ＝R _θ D

wherein R is _θ A rotation matrix of the direction angle θ that is a key point:

D _θ a matrix of coordinates of the rotated pixel points is set such that a pair of the coordinates of the pixel points are (x' _i1 ,y′ _i1 ),(x′ _i2 ,y′ _i2 ) And Des at the ith bit of the descriptor _i Corresponding to the above;

calculated in HSV three monochromatic channel images respectively to (x' _i1 ,y′ _i1 ),(x′ _i2 ,y′ _i2 ) The pixel average value of the pixel block Patch with w pixels as radius is calculated as follows:

calculating the color similarity of the two pixel blocks:

wherein Cdist _i Bdist as color difference _i Is the brightness difference;

then the ith bit Des of the descriptor _i The definition is as follows:

wherein ε is _c 、ε _B The thresholds of color difference and brightness difference are respectively shown as Cdist _i And Bdist _i When the colors of the two pixel blocks are smaller than the threshold value, the color of the two pixel blocks is similar, the value of the corresponding bit of the descriptor is 1, otherwise, the colors of the two pixel blocks are different, and the value of the corresponding bit of the descriptor is 0;

after 256 pairs of pixel points are operated, a 256-bit binary vector is obtained and used as an R-BRIEF descriptor.

7. A color image-based feature point extraction and matching system, comprising:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of any one of claims 1-5.

8. A storage medium having stored therein a processor executable program, which when executed by a processor is adapted to carry out the method of any one of claims 1-5.