CN117975179A

CN117975179A - Image processing method and device and electronic equipment

Info

Publication number: CN117975179A
Application number: CN202211312678.4A
Authority: CN
Inventors: 杨东玥; 王贵东; 吴涛
Original assignee: Beijing Zitiao Network Technology Co Ltd
Current assignee: Beijing Zitiao Network Technology Co Ltd
Priority date: 2022-10-25
Filing date: 2022-10-25
Publication date: 2024-05-03

Abstract

The embodiment of the invention discloses an image processing method, an image processing device and electronic equipment; an image processing method, comprising: determining at least two feature points corresponding to an image to be processed; determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment; and screening the at least two feature points according to the target feature screening scale to obtain screened feature points. The image processing method, the device and the electronic equipment are used for realizing reasonable screening of high-quality characteristic points with uniform spatial distribution.

Description

Image processing method and device and electronic equipment

Technical Field

The disclosure relates to the technical field of image processing, and in particular relates to an image processing method, an image processing device and electronic equipment.

Background

Current vision-based positioning techniques, navigation techniques, etc. utilize image-to-image matching relationships to perform motion state estimation. The matching effect between the images is influenced by a feature detection algorithm and a matching method and also related to the quality of the feature points; extracting high-quality feature points is helpful to improve the accuracy, efficiency and stability of image matching.

One of the conditions required for the high-quality feature points is that the distribution is as uniform as possible, i.e. the high-quality feature points cannot be concentrated in a certain region in the image, and a certain distance between the points needs to be ensured, so that dense distribution is avoided. Therefore, after feature extraction, the feature points need to be screened to retain high-quality feature points with relatively uniform distribution.

Disclosure of Invention

This disclosure is provided in part to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

The embodiment of the disclosure provides an image processing method, an image processing device and electronic equipment, which are used for reasonably screening high-quality feature points with uniform spatial distribution.

In a first aspect, an embodiment of the present disclosure provides an image processing method, including: determining at least two feature points corresponding to an image to be processed; determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment; and screening the at least two feature points according to the target feature screening scale to obtain screened feature points.

In a second aspect, embodiments of the present disclosure provide yet another image processing method, including: determining at least two feature points and feature screening scales corresponding to the image to be processed; taking the feature screening scale as a standard deviation to generate a Gaussian function; and screening the at least two characteristic points for multiple times according to the Gaussian function.

In a third aspect, an embodiment of the present disclosure provides an image processing apparatus including: the determining unit is used for determining at least two characteristic points corresponding to the image to be processed; the determining unit is further used for determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment; and the screening unit is used for screening the at least two characteristic points according to the target characteristic screening scale to obtain screened characteristic points.

In a fourth aspect, an embodiment of the present disclosure provides still another image processing apparatus, including: the determining unit is used for determining at least two characteristic points and characteristic screening scales corresponding to the image to be processed; the generation unit is used for generating a Gaussian function by taking the characteristic screening scale as a standard deviation; and the screening unit is used for carrying out multiple screening on the at least two characteristic points according to the Gaussian function.

In a fifth aspect, embodiments of the present disclosure provide an electronic device, including: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the image processing method as described in the first aspect or the second aspect.

In a sixth aspect, an embodiment of the present disclosure provides a computer readable medium having stored thereon a computer program which, when executed by a processor, implements the image processing method according to the first or second aspect.

According to the image processing method, the image processing device and the electronic equipment, the fact that the image is limited by the resolution is considered, the resolution is related to the optical parameters of the image acquisition equipment, and therefore the optical parameters of the image acquisition equipment influence the distance between the feature points. Based on the feature screening scale related to the image information and the feature point information and/or the feature screening scale related to the optical parameter of the image acquisition equipment is used as priori information, the screening scale of the feature point is determined, and the feature point is screened based on the screening scale of the feature point; the screening of the characteristic points avoids the influence caused by resolution ratio, and further reasonable screening of high-quality characteristic points with even spatial distribution is realized.

And performing repeated screening on at least two characteristic points through a Gaussian function generated based on the characteristic screening scale, so as to realize iterative screening of the characteristic points and keep the distribution of the characteristic points as uniform as possible; the characteristic points with large response and small distance can be reserved, and rough screening is avoided, so that high-quality characteristic points with uniform spatial distribution are reasonably screened out.

Drawings

The above and other features, advantages, and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. The same or similar reference numbers will be used throughout the drawings to refer to the same or like elements. It should be understood that the figures are schematic and that elements and components are not necessarily drawn to scale.

FIG. 1 is a flow chart of one embodiment of an image processing method according to the present disclosure;

FIG. 2 is a flow chart of another embodiment of an image processing method according to the present disclosure;

FIG. 3 is a flow chart of another embodiment of an image processing method according to the present disclosure;

FIG. 4 is a flow chart of another embodiment of an image processing method according to the present disclosure;

fig. 5 is a schematic structural view of an embodiment of an image processing apparatus according to the present disclosure;

Fig. 6 is a schematic structural view of still another embodiment of an image processing apparatus according to the present disclosure;

FIG. 7 is an exemplary system architecture to which an image processing method of one embodiment of the present disclosure may be applied;

fig. 8 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.

It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.

It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.

The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

The technical scheme of the disclosure can be applied to various image processing scenes related to feature point extraction, and in some image processing scenes, matching between images may be needed, for example: visual-based localization scenes, navigation scenes, etc., and matching between images requires the use of feature points of the images, so that the image feature points are obtained by processing the images. In other image processing scenarios, it may be desirable to identify images, such as: identification of the target object, etc., feature points need to be extracted from the image in order to achieve more accurate identification of the target object.

Regardless of the image processing scene, when feature point extraction is performed, feature point screening is required to ensure that the final feature point is a high-quality feature point.

In the related art, feature point extraction is performed in a full graph range by adopting a feature point extraction algorithm. Feature point extraction algorithms, for example: harris (a corner extraction algorithm proposed by Harris), fast (Features fromaccelerated SEGMENT TEST, corner detection algorithm) and other feature point extraction algorithms.

In these related arts, a phenomenon in which the detected feature points are concentrated in a certain region in the image may occur depending on the content of the image. Furthermore, in the related application scene, if the distance between the feature points is relatively short, the calculated descriptors may have relatively high similarity, so that the feature points which are mismatched are very easy to search in the image to be matched; on the other hand, too concentrated feature points can cause less distribution of the feature points of other parts of the whole graph, so that larger deviation exists in the calculation of the whole pose of the whole graph.

Therefore, the related art performs feature point extraction in the whole graph range, so that the extracted feature points are unevenly distributed, and reasonable extraction or screening of high-quality feature points with even spatial distribution cannot be realized.

Based on this, according to the screening scheme of the feature points provided by the embodiment of the disclosure, on one hand, the image is considered to be limited by the resolution, and the resolution is related to the optical parameters of the image acquisition device, so that the optical parameters of the image acquisition device influence the distance between the feature points. Then, using the feature screening scale related to the image information and the feature point information and the feature screening scale related to the optical parameter of the image acquisition equipment as prior information, determining the screening scale of the feature point, and screening the feature point based on the screening scale of the feature point; the screening of the characteristic points avoids the influence caused by resolution ratio, and further reasonable screening of high-quality characteristic points with even spatial distribution is realized.

On the other hand, based on a Gaussian function generated by the feature screening scale, at least two feature points are screened for multiple times, iterative screening of the feature points is realized, and the distribution of the feature points is kept as uniform as possible; the characteristic points with large response and small distance can be reserved, and rough screening is avoided, so that high-quality characteristic points with uniform spatial distribution are reasonably screened out.

Referring to fig. 1, a flow of one embodiment of an image processing method according to the present disclosure is shown. The image processing method can be applied to a terminal device. The image processing method as shown in fig. 1 includes the steps of:

step 101, determining at least two feature points corresponding to an image to be processed.

In some application scenarios, the image to be processed is an image that needs to be image matched. In other application scenarios, the image to be processed is an image that requires identification of the target object.

In some embodiments, the number of images to be processed is 1, and then the images to be processed are processed accordingly. In other embodiments, if the number of images to be processed is greater than 1, the images to be processed are processed according to the same processing method.

In some embodiments, the plurality of feature points in step 101 are original feature points extracted based on the image to be processed, i.e., feature points that have not undergone any processing. In other embodiments, the plurality of feature points in step 101 are feature points subjected to a preliminary screening process. For example: 100 feature points are extracted based on the image to be processed, 10 feature points are deleted by performing primary screening on the 100 feature points, and the initially screened feature points are the left 90 feature points.

Thus, as an alternative embodiment, step 101 comprises: acquiring at least two initial feature points extracted from an image to be processed; determining characteristic point response values corresponding to at least two initial characteristic points respectively; and performing preliminary screening on the at least two initial feature points according to feature point response values respectively corresponding to the at least two initial feature points, and determining the preliminarily screened feature points as the at least two feature points of the image to be processed.

In some embodiments, the extraction of at least two initial feature points is based on a preset feature extraction algorithm. Feature extraction algorithms include, but are not limited to: FAST, harris, SIFT (Scale-INVARIANT FEATURE TRANSFORM ), SURF (Speeded Up Robust Features, accelerated robust features), and the like.

Further, the feature point response values corresponding to the at least two initial feature points respectively may be response values corresponding to the corresponding feature extraction algorithm. In other embodiments, the feature point response values employ Harris corner response values, regardless of the feature extraction algorithm employed.

In some embodiments, a feature point response value threshold is preset, and feature points with feature point response values smaller than or equal to the feature point response value threshold are deleted; and reserving the characteristic points with the characteristic point response values larger than the characteristic point response value threshold value. The feature point response value threshold is determined according to a corresponding feature point response value determining manner, for example: for Harris corner response values, corresponding Harris corner response thresholds are set, and specific values are not limited herein.

Thus, as an alternative embodiment, the feature point response value is a Harris corner response value. Compared with other response values, the Harris corner response value can better reflect the robustness of the feature points and the quality of the feature points so as to improve the quality of the finally screened feature points.

In some application scenarios, in order to improve the extraction efficiency of feature points, before feature extraction is performed based on an image to be processed, the image is segmented, and then feature extraction is performed based on the segmented image.

Thus, as an alternative embodiment, the image to be processed is an image subjected to image segmentation processing, and the at least two initial feature points are feature points extracted based on at least two image blocks.

In some embodiments, at least two image blocks are ordered, for example: numbering at least two image blocks starting from 0; therefore, when the feature points are extracted, the feature point extraction is sequentially carried out according to the serial number sequence of the image blocks, so that the efficiency and the precision of the feature point extraction are improved, and the situations of repetition or error extraction and the like are avoided.

Step 102, determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale.

The first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment.

In some embodiments, the image information to be processed includes: the size of the image to be processed, the characteristic point information includes: the number of the feature points and the number of the at least two feature points are preset.

The preset number of feature points may be a preset threshold of number of feature points, and is preset in combination with a specific application scenario, which is not limited herein.

Thus, the image processing method further includes: and determining a first feature screening scale according to the size of the image to be processed, the number of preset feature points and the number of at least two feature points.

In some embodiments, the size of the image to be processed may include: width and height of the image to be processed.

In some embodiments, the first feature screening scale is expressed as: Wherein w is the width of the image to be processed, h is the height of the image to be processed, f _p is the number of preset feature points, and f ₀ is the number of at least two feature points.

In some embodiments, the image capture device is a camera, and the optical parameters of the image capture device include: a point spread function and a half-width; the optical parameters may be provided by the camera module manufacturer. And, the optical parameter may be understood as a priori information from the viewpoint of the resolution of the optical camera.

Further, the image processing method further includes: and determining a second feature screening scale according to the point spread function and the half-width.

In some embodiments, the second feature screening scale may be expressed as: FWHM (PSF); wherein, PSF is totally called point spread function, representing point spread function, FWHM is totally called Full WIDTH HALF maximum, representing half width at half maximum.

It will be appreciated that if the target feature screening scale is determined from the first feature screening scale, then only the first feature screening scale need be determined. If the target feature screening scale is determined according to the second feature screening scale, only the second feature screening scale needs to be determined. If the target feature screening scale is determined according to the first feature screening scale and the second feature screening scale, the first feature screening scale and the second feature screening scale are determined.

Further, in combination with the first feature screening scale and/or the second feature screening scale, a target feature screening scale may be determined.

As an alternative embodiment, step 102 includes: determining a largest feature screening scale of the first feature screening scale and the second feature screening scale; and determining the target feature screening scale according to the maximum feature screening scale.

In some embodiments, the target feature screening scale is equal to the maximum feature screening scale.

In other embodiments, the target feature screening scale is the product of a maximum feature screening scale and a preset scale gain. The preset scale gain can be reasonably set according to different application scenes. For example, the setting may be based on experience or demand, which may be based on time consuming algorithms, memory usage, etc. It can be understood that the larger the scale gain, the faster the screening speed of the feature points; similarly, the smaller the scale gain, the smaller the screening speed of the feature points.

As another alternative embodiment, step 102 includes: and determining the first feature screening scale as a target feature screening scale. Or the target feature screening scale is the product of the first feature screening scale and a preset scale gain. Wherein the preset scale gain is as described with reference to the previous embodiments.

As yet another alternative embodiment, step 102 includes: and determining the second feature screening scale as a target feature screening scale. Or the target feature screening scale is the product of the second feature screening scale and a preset scale gain. Wherein the preset scale gain is as described with reference to the previous embodiments.

And step 103, screening at least two feature points according to the target feature screening scale to obtain screened feature points.

It can be understood that the selected feature points are the feature points which remain after the selection, among the at least two feature points.

In embodiments of the present disclosure, the resolution is related to the optical parameters of the image capturing device, considering that the image itself is constrained by the resolution, such that the optical parameters of the image capturing device affect the distance between the feature points. Based on the feature screening scale related to the image information and the feature point information and/or the feature screening scale related to the optical parameter of the image acquisition equipment is used as priori information, the screening scale of the feature point is determined, and the feature point is screened based on the screening scale of the feature point; the screening of the characteristic points avoids the influence caused by resolution ratio, and further reasonable screening of high-quality characteristic points with even spatial distribution is realized.

In some embodiments, the distance between the target feature point and the feature point in the neighborhood range may be calculated, then the feature point with the distance smaller than the target feature screening scale is deleted, and the feature point with the distance larger than the target feature screening scale is retained.

The distance between the feature points may be a manhattan distance or an euclidean distance, and the corresponding distance is calculated by using a corresponding distance calculation method based on coordinates of the feature points when calculating the distance between the feature points, which is not limited in the embodiments of the present disclosure.

The target feature point may be a feature point having the largest feature point response value among the currently remaining feature points; and before each screening, determining the feature point with the largest feature point response value in the current residual feature points, and then carrying out feature screening based on the feature point and the target feature screening scale. The feature point response value of the feature point may be determined before screening or during screening, and may be Harris corner response value.

In other embodiments, iterative screening may also be implemented using target feature screening metrics.

Thus, as an alternative embodiment, step 103 comprises: taking the target feature screening scale as a standard deviation to generate a Gaussian function; and screening the at least two characteristic points for multiple times according to the Gaussian function.

In some embodiments, the gaussian function may be a two-dimensional gaussian function that is a symmetric two-dimensional gaussian function with a mean of 0 and a standard deviation is required.

In some embodiments, after the gaussian function is generated, a maximum normalization process may be performed, that is, capping of the gaussian function is performed as a maximum normalization process, so as to facilitate subsequent processing.

Thus, the size of the two-dimensional gaussian function can generally be equal to or greater than 2 times the standard deviation plus 1. For example: assuming a standard deviation of 3, the gaussian window size is preferably 7*7, which is used as a priori information for the next feature screening.

It will be appreciated that the relevant techniques for gaussian functions are well known in the art and will not be described in detail herein.

Further, as an alternative embodiment, the multiple filtering of at least two feature points according to a gaussian function includes: acquiring a characteristic point response value matrix corresponding to at least two characteristic points, wherein the characteristic point response value matrix comprises characteristic point response values of all the characteristic points; determining a Gaussian matrix according to the Gaussian function; and screening at least two characteristic points for multiple times according to the characteristic point response value matrix, the Gaussian matrix and the preset gain.

The feature point response value matrix corresponding to at least two feature points, which may also be referred to as a feature point response graph, includes feature point response values of each feature point, and each feature point is represented by a pixel abscissa and a pixel ordinate of the feature point, and is a matrix with the same size as the image to be processed.

In some embodiments, the gaussian matrix, i.e. the gaussian window described in the previous embodiments, is also a fixed size matrix.

In some embodiments, the preset gain may be configured according to the noise level. For example, the greater the noise level, the greater the gain; the lower the noise level, the smaller the gain.

In some embodiments, the noise level may be determined based on noise information, which may be external prior information, and the specific acquisition mode is not limited herein.

Furthermore, according to the characteristic point response value matrix, the Gaussian matrix and the preset gain, iterative screening can be conducted on at least two characteristic points for multiple times.

As an alternative embodiment, any one screening process of the multiple screens includes: determining a neighborhood characteristic point response value matrix based on the target characteristic points, the number of rows and columns of the Gaussian matrix and the characteristic point response value matrix; the target feature points are feature points with the largest feature point response value in the feature points to be screened currently, the number of rows and columns of the neighborhood feature point response value matrix is the same as that of the Gaussian matrix, and the neighborhood feature point response value matrix comprises feature point response values of the neighborhood feature points of the target feature points; the neighborhood characteristic point response value matrix is differenced with a product matrix of the Gaussian matrix and a preset gain, and a residual error matrix is obtained; determining target matrix elements in a residual matrix, and setting the target matrix elements as preset values so as to delete characteristic points corresponding to the target matrix elements from an image to be processed; the target matrix element is smaller than the preset value.

In this embodiment, in any one screening process, it is necessary to determine a target feature point, where the target feature point is a feature point having a maximum feature point response value among feature points to be screened currently.

And then, according to the row and column number of the Gaussian matrix, finding a neighborhood characteristic point response value matrix corresponding to the target characteristic point in the characteristic point response value matrix. For example, assuming that the gaussian matrix is 7*7, a neighborhood feature point response value matrix of 7*7 is extracted from the feature point response value matrix with the feature point response value as the center.

Next, feature points may be screened based on the neighborhood feature point response value matrix, and in the embodiment of the present disclosure, a direct screening manner is not adopted, but an indirect screening manner is adopted. Specifically, the neighborhood characteristic point response value matrix is subjected to difference with a Gaussian matrix and a product matrix of a preset gain, and a residual matrix is obtained.

For example, assuming that the gaussian matrix is a, the neighborhood feature point response value matrix is B, and the preset gain is G, the residual matrix is B-g×a, and the residual matrix corresponds to the understanding of the feature point response value matrix, which may also be referred to as a residual map.

Further, based on the residual matrix, the values of matrix elements therein should meet a preset value condition. Thus, the target matrix element whose matrix element is smaller than the preset value is determined first, and its value is set to the preset value. And if the matrix element is a preset value, the characteristic point corresponding to the matrix element is deleted.

In some embodiments, the preset value may be 0; or other constraint values, not limited herein.

Since multiple iterative filtering is required, after each iterative filtering, relevant information can be recorded, so as to judge whether the iteration needs to be stopped or not or continue to execute the iterative process according to the relevant information.

Thus, as an alternative embodiment, the arbitrary screening process further comprises: in response to detecting that the target feature points do not belong to the feature points in the preset feature point statistics table, recording the target feature points into the preset feature point statistics table, and adding 1 to the number of the feature points; the method comprises the steps of screening, wherein the number of characteristic points is preset, and a preset characteristic point statistical table is empty in the first screening; and determining the current residual characteristic points as the finally screened characteristic points in response to the fact that the number of the characteristic points reaches the preset number of the characteristic points.

In this embodiment, a feature point statistics table is preset, which may be used to record the target feature points that have been used for screening, so that the number of feature points may be counted based on the feature point statistics table to determine whether the iteration is ended based on the number of feature points.

In the feature point statistics table, the coordinates of each feature point can be recorded, and when whether the target feature point belongs to the feature point statistics table is detected, the coordinates of each feature point in the feature point statistics table are compared with the coordinates of the target feature point, so that detection can be realized.

The number of preset feature points may be configured in conjunction with a specific application scenario, which is not limited herein by a specific value.

Correspondingly, if the number of the detected feature points does not reach the preset number of the feature points, continuing the next screening process.

In some embodiments, the iteration number determination may be combined in addition to the determination from the point of view of the number of feature points.

As an alternative embodiment, the image processing method further includes: and displaying prompt information for indicating that the preset gain is unreasonable in response to the detection that the screening times of the feature points are greater than or equal to the preset screening times.

The preset screening times can take a larger value, so that the screening times generally cannot reach the preset screening times, and the preset screening times are used for preventing infinite iteration loops.

In such an embodiment, the number of screens may be increased by 1 after each iteration; judging whether the screening times of the current feature points are larger than or equal to preset screening times; if yes, displaying prompt information for indicating that the preset gain is unreasonable; if not, continuing iteration.

In some embodiments, the hint information may be, for example: the preset gain is unreasonable and needs to be adjusted.

By adopting the feature point iterative screening method provided by the embodiment of the disclosure, the feature points in a large range are not screened directly according to the target feature screening scale, but are screened indirectly in a small range by utilizing the operation between matrixes, so that the distribution of the feature points can be kept as uniform as possible; the characteristic points with large response and small distance can be reserved, and rough screening is avoided, so that high-quality characteristic points with uniform spatial distribution are reasonably screened out.

With further reference to fig. 2 and 3, the foregoing feature screening scheme is an alternative flow in practical applications.

In fig. 2, for an input image, feature extraction is implemented by using a feature point extraction algorithm; and then calculating a characteristic point response value based on the extracted characteristic points. And then, retaining the characteristic points with the characteristic point response values larger than the preset response values, and realizing the preliminary screening of the characteristic points. And then counting the number of the characteristic points, and determining a characteristic point screening scale by combining the number of the characteristic points, the size information of the input image, a preset upper limit of the characteristic points and the optical parameters of the image acquisition equipment. And further, taking the feature point screening scale as a standard deviation, generating a two-dimensional Gaussian function, and carrying out maximum normalization processing. Further, feature screening is achieved by utilizing a two-dimensional Gaussian function, and an image and screened feature points are output.

In fig. 3, an iterative screening process of feature points is described, the inputs of which are: feature screening prior matrixes, namely Gaussian matrixes; the characteristic point response value matrix comprises coordinates and response values; the processing gain and the maximum iteration number K are preset.

Firstly, based on a characteristic point response value matrix and a preset processing gain, finding out a characteristic point with the maximum current characteristic point response value, and calculating a residual image based on the characteristic point and a characteristic screening priori matrix, namely, screening the characteristic points.

Then, the iteration number K is increased by 1, and whether the iteration number is smaller than the maximum iteration number Km is judged. If yes, updating a feature point statistical table; if not, the preset processing gain is not reasonable and needs to be adjusted.

Further, when the feature point statistical table is updated, judging whether feature point coordinates exist in the feature point statistical table, if so, continuing the next iteration; if Buddha, the number of the characteristic points is increased by 1, and whether the number of the characteristic points is smaller than the number of the preset characteristic points is judged. If yes, continuing the next iteration; if not, outputting the coordinates of the feature points after screening.

Referring to fig. 4, a flowchart of still another implementation of an image processing method according to an embodiment of the disclosure is provided, where the image processing method includes:

step 401, determining at least two feature points and feature screening scales corresponding to the image to be processed.

Wherein, the feature screening scale can be determined by referring to the flow shown in fig. 1; other embodiments may also be used to determine, for example: the determination based on noise information, the use of a preset feature screening scale, etc., is not limited herein.

The determination of at least two feature points is described with reference to the previous embodiments and will not be repeated here.

And step 402, taking the feature screening scale as a standard deviation, and generating a Gaussian function.

And step 403, screening the at least two characteristic points for multiple times according to the Gaussian function.

In embodiments of the present disclosure, feature point screening may be improved in two ways, on the one hand, by the improvement of the computation of feature screening metrics and on the other hand, by the improvement of the feature point iterative screening process. And, these two aspects can be used in combination, also can be used alone. Thus, embodiments of the present disclosure may protect three schemes: the characteristic point screening scheme is realized based on the calculation mode of the improved characteristic point screening scale, the characteristic point screening scheme is realized based on the improved characteristic point iterative screening process, and the characteristic point screening scheme is realized jointly based on the calculation mode of the improved characteristic point screening scale and the improved characteristic point iterative screening process.

Thus, the embodiments of steps 402 and 403 may be referred to the description of the previous embodiments.

Thus, step 403 may include: acquiring a characteristic point response value matrix corresponding to at least two characteristic points, wherein the characteristic point response value matrix comprises characteristic point response values of all the characteristic points; determining a Gaussian matrix according to the Gaussian function; and screening at least two characteristic points for multiple times according to the characteristic point response value matrix, the Gaussian matrix and the preset gain.

And, any one screening process of the plurality of screening includes: determining a neighborhood characteristic point response value matrix based on the target characteristic points, the number of rows and columns of the Gaussian matrix and the characteristic point response value matrix; the target feature points are feature points with the largest feature point response value in the feature points to be screened currently, the number of rows and columns of the neighborhood feature point response value matrix is the same as that of the Gaussian matrix, and the neighborhood feature point response value matrix comprises feature point response values of the neighborhood feature points of the target feature points; the neighborhood characteristic point response value matrix is differenced with a product matrix of the Gaussian matrix and a preset gain, and a residual error matrix is obtained; determining target matrix elements in the residual matrix, and setting the target matrix elements as preset values so as to delete characteristic points corresponding to the target matrix elements from the image to be processed; the target matrix element is less than a preset value.

The optional screening process further comprises: in response to detecting that the target feature points do not belong to the feature points in the preset feature point statistics table, recording the target feature points into the preset feature point statistics table, and adding 1 to the number of the feature points; the method comprises the steps of screening, wherein the number of characteristic points is preset, and a preset characteristic point statistical table is empty in the first screening; and determining the current residual characteristic points as the finally screened characteristic points in response to the fact that the number of the characteristic points reaches the preset number of the characteristic points.

The image processing method further includes: and displaying prompt information for indicating that the preset gain is unreasonable in response to the detection that the screening times of the feature points are greater than or equal to the preset screening times.

The detailed implementation of each step of the flow shown in fig. 4 is not repeated here for brevity of the description.

With further reference to fig. 5, as an implementation of the method shown in fig. 1, the present disclosure provides an embodiment of an image processing apparatus, which corresponds to the image processing method embodiment shown in fig. 1, and which is particularly applicable to various electronic devices.

As shown in fig. 5, the image processing apparatus of the present embodiment includes:

a first determining unit 501, configured to determine at least two feature points corresponding to an image to be processed; the first determining unit 501 is further configured to determine a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment; and the first screening unit 502 is configured to screen the at least two feature points according to the target feature screening scale, so as to obtain screened feature points.

In some embodiments, the image information to be processed includes: the size of the image to be processed; the feature point information includes: presetting the number of feature points and the number of the at least two feature points; the first determining unit 501 is further configured to determine the first feature screening scale according to the size of the image to be processed, the number of preset feature points, and the number of at least two feature points.

In some embodiments, the optical parameters include: a point spread function and a half-width; the image processing method further includes: the first determining unit 501 is further configured to determine the second feature screening scale according to the point spread function and the half-width.

In some embodiments, the first determining unit 501 is further configured to determine a largest feature screening scale of the first feature screening scale and the second feature screening scale; and determining the target feature screening scale according to the maximum feature screening scale.

In some embodiments, the first filtering unit 502 is further configured to generate a gaussian function using the target feature filtering scale as a standard deviation; and screening the at least two characteristic points for multiple times according to the Gaussian function.

In some embodiments, the first screening unit 502 is further to: acquiring a characteristic point response value matrix corresponding to the at least two characteristic points, wherein the characteristic point response value matrix comprises characteristic point response values of all the characteristic points; determining a Gaussian matrix according to the Gaussian function; and screening the at least two characteristic points for multiple times according to the characteristic point response value matrix, the Gaussian matrix and the preset gain.

In some embodiments, any one of the multiple screening processes comprises: determining a neighborhood characteristic point response value matrix based on the target characteristic points, the row and column numbers of the Gaussian matrix and the characteristic point response value matrix; the target feature points are feature points with the largest feature point response values in the feature points to be screened currently, the number of rows and columns of the neighborhood feature point response value matrix is the same as that of the Gaussian matrix, and the neighborhood feature point response value matrix comprises feature point response values of neighborhood feature points of the target feature points; the neighborhood characteristic point response value matrix is subjected to difference with a product matrix of the Gaussian matrix and the preset gain, and a residual error matrix is obtained; determining target matrix elements in the residual matrix, and setting the target matrix elements as preset values so as to delete characteristic points corresponding to the target matrix elements from the image to be processed; the target matrix element is smaller than the preset value.

In some embodiments, the any one screening process further comprises: in response to detecting that a target feature point does not belong to a feature point in a preset feature point statistics table, recording the target feature point into the preset feature point statistics table, and adding 1 to the number of feature points; the feature point statistical table is used for selecting feature points according to the number of the feature points, wherein the number of the feature points is preset in the first screening process, and the preset feature point statistical table is empty; and determining the current residual characteristic points as the finally screened characteristic points in response to the fact that the number of the characteristic points reaches the preset number of the characteristic points.

In some embodiments, the first screening unit 502 is further configured to: and displaying prompt information for indicating that the preset gain is unreasonable in response to the detection that the screening times of the feature points are larger than or equal to the preset screening times.

Referring to fig. 6, as an implementation of the method shown in fig. 4, the present disclosure provides an embodiment of an image processing apparatus, which corresponds to the image processing method embodiment shown in fig. 4, and which is particularly applicable to various electronic devices.

As shown in fig. 6, the image processing apparatus of the present embodiment includes: a second determining unit 601, configured to determine at least two feature points and feature screening scales corresponding to the image to be processed; a generating unit 602, configured to generate a gaussian function by using the feature screening scale as a standard deviation; and a second screening unit 603, configured to perform multiple screening on the at least two feature points according to the gaussian function.

In some embodiments, the second screening unit 603 is further configured to: acquiring a characteristic point response value matrix corresponding to the at least two characteristic points, wherein the characteristic point response value matrix comprises characteristic point response values of all the characteristic points; determining a Gaussian matrix according to the Gaussian function; and screening the at least two characteristic points for multiple times according to the characteristic point response value matrix, the Gaussian matrix and the preset gain.

In some embodiments, the second screening unit 603 is further configured to: and displaying prompt information for indicating that the preset gain is unreasonable in response to the detection that the screening times of the feature points are larger than or equal to the preset screening times.

Referring to fig. 7, fig. 7 illustrates an exemplary system architecture in which an image processing method of an embodiment of the present disclosure may be applied.

As shown in fig. 7, the system architecture may include terminal devices 701, 702, 703, a network 704, and a server 707. Network 704 may be the medium used to provide communications links between terminal devices 701, 702, 703 and server 707. The network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

Terminal devices 701, 702, 703 may interact with a server 707 through a network 704 to receive or send messages and the like. Various client applications, such as a web browser application, a search class application, a news information class application, may be installed on the terminal devices 701, 702, 703. The client applications in the terminal devices 701, 702, 703 may receive the instruction of the user and perform the corresponding functions according to the instruction of the user, for example, adding the corresponding information to the information according to the instruction of the user.

The terminal devices 701, 702, 703 may be hardware or software. When the terminal devices 701, 702, 703 are hardware, they may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like. When the terminal devices 701, 702, 703 are software, they can be installed in the above-listed electronic devices. Which may be implemented as multiple software or software modules (e.g., software or software modules for providing distributed services) or as a single software or software module. The present invention is not particularly limited herein.

The server 707 may be a server that provides various services, for example, receives information acquisition requests sent by the terminal devices 701, 702, 703, and acquires presentation information corresponding to the information acquisition requests in various ways according to the information acquisition requests. And related data showing the information is transmitted to the terminal devices 701, 702, 703.

It should be noted that the image processing method provided by the embodiment of the present disclosure may be performed by a terminal device, and accordingly, the image processing apparatus may be provided in the terminal devices 701, 702, 703. Further, the image processing method provided by the embodiment of the present disclosure may also be executed by the server 707, and accordingly, the image processing apparatus may be provided in the server 707.

It should be understood that the number of terminal devices, networks and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to fig. 8, a schematic diagram of a configuration of an electronic device (e.g., a terminal device or server in fig. 7) suitable for use in implementing embodiments of the present disclosure is shown. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 8 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 8, the electronic device may include a processing means (e.g., a central processor, a graphics processor, etc.) 801 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 708 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

In general, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, and the like; an output device 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, etc.; storage 808 including, for example, magnetic tape, hard disk, etc.; communication means 809. The communication means 809 may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 shows an electronic device having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via communication device 809, or installed from storage device 808, or installed from ROM 802. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 801.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining at least two feature points corresponding to an image to be processed; determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment; and screening the at least two feature points according to the target feature screening scale to obtain screened feature points.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. 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 upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. The names of the units do not constitute limitations on the units themselves in some cases, and for example, the first determination unit 501 may also be described as "a unit that determines at least two feature points corresponding to an image to be processed".

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).

Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.

Claims

1. An image processing method, comprising:

Determining at least two feature points corresponding to an image to be processed;

Determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment;

And screening the at least two feature points according to the target feature screening scale to obtain screened feature points.

2. The image processing method according to claim 1, wherein the image information to be processed includes: the size of the image to be processed; the feature point information includes: presetting the number of feature points and the number of the at least two feature points; the image processing method further includes:

And determining the first feature screening scale according to the size of the image to be processed, the number of preset feature points and the number of the at least two feature points.

3. The image processing method according to claim 1, wherein the optical parameters include: a point spread function and a half-width; the image processing method further includes:

And determining the second feature screening scale according to the point spread function and the half-width.

4. The image processing method according to claim 1, wherein the determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale comprises:

determining a largest feature screening scale of the first feature screening scale and the second feature screening scale;

And determining the target feature screening scale according to the maximum feature screening scale.

5. The image processing method according to claim 1, wherein the screening the at least two feature points according to the target feature screening scale includes:

Taking the target feature screening scale as a standard deviation to generate a Gaussian function;

and screening the at least two characteristic points for multiple times according to the Gaussian function.

6. The image processing method according to claim 5, wherein the performing a plurality of filtering on the at least two feature points according to the gaussian function includes:

Acquiring a characteristic point response value matrix corresponding to the at least two characteristic points, wherein the characteristic point response value matrix comprises characteristic point response values of all the characteristic points;

determining a Gaussian matrix according to the Gaussian function;

And screening the at least two characteristic points for multiple times according to the characteristic point response value matrix, the Gaussian matrix and the preset gain.

7. The image processing method according to claim 6, wherein any one screening process among the plurality of screens includes:

Determining a neighborhood characteristic point response value matrix based on the target characteristic points, the row and column numbers of the Gaussian matrix and the characteristic point response value matrix; the target feature points are feature points with the largest feature point response values in the feature points to be screened currently, the number of rows and columns of the neighborhood feature point response value matrix is the same as that of the Gaussian matrix, and the neighborhood feature point response value matrix comprises feature point response values of neighborhood feature points of the target feature points;

The neighborhood characteristic point response value matrix is subjected to difference with a product matrix of the Gaussian matrix and the preset gain, and a residual error matrix is obtained;

determining target matrix elements in the residual matrix, and setting the target matrix elements as preset values so as to delete characteristic points corresponding to the target matrix elements from the image to be processed; the target matrix element is smaller than the preset value.

8. The image processing method according to claim 7, wherein the arbitrary one-time filtering process further includes:

in response to detecting that a target feature point does not belong to a feature point in a preset feature point statistics table, recording the target feature point into the preset feature point statistics table, and adding 1 to the number of feature points; the feature point statistical table is used for selecting feature points according to the number of the feature points, wherein the number of the feature points is preset in the first screening process, and the preset feature point statistical table is empty;

And determining the current residual characteristic points as the finally screened characteristic points in response to the fact that the number of the characteristic points reaches the preset number of the characteristic points.

9. The image processing method according to claim 6, characterized in that the image processing method further comprises:

And displaying prompt information for indicating that the preset gain is unreasonable in response to the detection that the screening times of the feature points are larger than or equal to the preset screening times.

10. An image processing method, comprising:

determining at least two feature points and feature screening scales corresponding to the image to be processed;

taking the feature screening scale as a standard deviation to generate a Gaussian function;

11. The image processing method according to claim 10, wherein the performing a plurality of filtering on the at least two feature points according to the gaussian function includes:

determining a Gaussian matrix according to the Gaussian function;

12. The image processing method according to claim 11, wherein any one screening process of the plurality of screens includes:

13. The image processing method according to claim 12, wherein the arbitrary one-time filtering process further includes:

14. The image processing method according to claim 10, characterized in that the image processing method further comprises:

15. An image processing apparatus, comprising:

the determining unit is used for determining at least two characteristic points corresponding to the image to be processed;

The determining unit is further used for determining a target feature screening scale according to the first feature screening scale and/or the second feature screening scale; the first feature screening scale is related to the image information to be processed and the feature point information, and the second feature screening scale is related to the optical parameters of the image acquisition equipment;

And the screening unit is used for screening the at least two characteristic points according to the target characteristic screening scale to obtain screened characteristic points.

16. An image processing apparatus, comprising:

the determining unit is used for determining at least two characteristic points and characteristic screening scales corresponding to the image to be processed;

the generation unit is used for generating a Gaussian function by taking the characteristic screening scale as a standard deviation;

And the screening unit is used for carrying out multiple screening on the at least two characteristic points according to the Gaussian function.

17. An electronic device, comprising:

one or more processors;

Storage means for storing one or more programs,

When executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-14.

18. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1-14.