CN113112406B - Feature determination method and device, electronic equipment and storage medium - Google Patents
Feature determination method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a feature determination method, a feature determination device, electronic equipment and a storage medium, and belongs to the technical field of single molecule positioning imaging. The method comprises the following steps: constructing a Bezier curve according to the characteristic information of the sample points; determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned; and taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned. Compared with the existing Gaussian fitting and cubic spline interpolation method, the technical scheme has the advantages that more imaging information is obtained, memory resources are reduced, and a new thought is provided for multi-dimensional single-molecule positioning imaging.
Description
Technical Field
The embodiment of the invention relates to a single molecule positioning imaging technology, in particular to a characteristic determination method, a characteristic determination device, electronic equipment and a storage medium.
Background
With the refinement of the optical equipment process, the microscope in practical application has reached the theoretical resolution limit, and due to the optical diffraction effect, the image point on the imaging surface of the optical system is blurred, and the blurring degree is related to the wavelength of light waves and the aperture numerical value. In order to overcome the diffraction limit, various super-resolution microscopic imaging methods are proposed, including random optical reconstruction microscopy (STORM) and optically activated positioning microscopy (PALM), which use asynchronous fluorescence scintillation to realize single fluorescence molecule imaging, and finally acquire the three-dimensional position of each molecule.
In the existing super-resolution microscopic imaging method, a random optical reconstruction microscopy (STORM) and a light-activated positioning microscopy (PALM) realize the imaging of a single fluorescent molecule by using a mode of fluorescent asynchronous scintillation, and finally obtain the three-dimensional position of each molecule. In the three-dimensional imaging process, positional information on the x-y plane perpendicular to the optical axis can be easily obtained, but it is also necessary to use point spread function Engineering (PSF Engineering) with respect to the position on the z-axis. Generally, a cylindrical mirror is arranged on a light path to shape a point diffusion function, so that fluorescent molecules at different z-axis distances are in point diffusion shapes of different forms through an optical system in a camera, and point diffusion shapes corresponding to different heights are obtained and used as sample images; and in the monomolecular microscopic imaging, fitting the acquired image points of the monomolecular microscopic imaging with a sample image by using a maximum likelihood method so as to obtain the three-dimensional position information of the fluorescent molecules and further restore the three-dimensional space structure of the monomolecular. The most common fitting method is a gaussian fitting method, and the height and width of a gaussian function are used as influence factors, so that the position of a fluorescent molecule is calculated. In order to obtain more accuracy and better fitting results, spline interpolation is generally performed on the PSF point spread function before the experiment, and a cubic spline interpolation method (Cspline) is generally adopted.
The existing Gaussian fitting mode has the problems that fitting is fixed, image points with high-order aberration cannot be fitted, and each area has specific aberration for area-specific phase difference. Another approach uses cubic spline interpolation, by fitting the image to a function with the largest cube, also because there are inevitable short plates with such cubic spline interpolation: first, due to the continuity of the cubic function, which cannot fit to scatter data, processing scatter data also requires translating measured interpolated data to the voxel grid. Second, since the cubic spline fitting method has coefficients to be calculated for each order at each interpolation point, the number of coefficients to be calculated increases when the interpolation number is increased to improve the fitting quality, and the number of such calculations increases exponentially, for example, 196 × 3 is calculated for 14 × 14 pixel regions 3 The spline coefficients, namely 5292 matrix operations, bear a huge burden on the processor in the fitting operation of 500 z-axis imaging numbers. Thirdly, cubic spline fitting can only be performed on three-dimensional information of an image at present, and higher dimensional information is difficult to realize due to the limitation of memory, for example, under the condition that the refractive indexes of objective lens oil and a cover glass are not matched, astigmatism change caused by the change of fluorescent molecules and the distance between glass slides is difficult to process.
Disclosure of Invention
The invention provides a feature determination method, a feature determination device, an electronic device and a storage medium, which are used for acquiring multi-dimensional feature information of a single-molecule image.
In a first aspect, an embodiment of the present invention provides a feature determining method, including:
constructing a Bezier curve according to the characteristic information of the sample points;
determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned;
and taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned.
In a second aspect, an embodiment of the present invention further provides a feature determining apparatus, including:
the curve construction module is used for constructing a Bezier curve according to the characteristic information of the sample points;
the target sample image determining module is used for determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned;
and the characteristic information determining module is used for taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned.
In a third aspect, an embodiment of the present invention further provides an electronic device, including:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method for feature determination as provided by any of the embodiments of the invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the feature determination method provided in any embodiment of the present invention.
According to the invention, a Bezier curve is constructed according to the characteristic information of the sample points, then a target sample image of the image to be positioned is determined according to the points on the Bezier curve, the relation between the sample points and the sample image and the image to be positioned, and further the characteristic information of the sample points corresponding to the target sample image is used as the characteristic information of the points corresponding to the image to be positioned. Compared with the conventional cubic spline interpolation method, the technical scheme has the advantages that more imaging information is acquired, the memory resources are reduced, and a new thought is provided for multi-dimensional single-molecule positioning imaging.
Drawings
Fig. 1A is a flowchart of a feature determination method according to an embodiment of the present invention;
FIG. 1B is a sample image according to one embodiment of the present invention;
fig. 2 is a flowchart of a feature determining method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a feature determination apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1A is a flowchart of a feature determination method according to an embodiment of the present invention, which is applicable to the case of single molecule image positioning, and the method may be executed by a feature determination apparatus, which may be implemented by software/hardware, and may be integrated in an electronic device carrying a feature determination function, such as a server.
As shown in fig. 1A, the method may specifically include:
and S110, constructing a Bezier curve according to the characteristic information of the sample points.
In this embodiment, the fluorescent beads are used as templates, the fluorescent beads are placed on a microscope, and three-dimensional positions of the fluorescent beads are moved by adjusting a Z-axis displacement stage to obtain point spread images at different positions as sample images. Optionally, in this embodiment, the fluorescent beads at different positions are used as sample points, the sample points correspond to the sample images one to one, and the feature information of different sample points is different. Optionally, the sample image is obtained as follows:
(1) Moving the high-precision z-axis displacement table to acquire samples layer by layer;
(2) The laser box (laser box) integrated by high-power high-precision lasers integrates the lasers with various wavelengths on one excitation channel;
(3) The total reflection focusing system acquires the fine displacement of the objective table, and forms a closed loop feedback adjustment and repair error by using the FPGA;
(4) Exciting laser to irradiate fluorescent molecules to enable electrons of the fluorescent molecules to generate energy level transition, and then radiating low-energy emission light;
(5) Placing a long-focus cylindrical mirror on the optical path to perform point spread function engineering on the imaging point, and performing three-dimensional shaping;
(6) A sample image is acquired using a Scientific Complementary Metal-Oxide-Semiconductor (sCMOS) imager for high resolution, rapid imaging, and, for example, fig. 1B shows a sample image of some fluorescent molecule imaging.
Furthermore, a sample image can be obtained through a vector wave model algorithm.
The feature information is a feature of the molecule during the imaging process, and may be, for example, position information, aberration information due to mismatch between the refractive index of the molecule and the refractive index of the slide, and chromatic aberration information due to a difference in wavelength of light.
In this embodiment, a sample point in a sample image is used as a fixed point, and a bezier curve is constructed by a B-spline basis function based on feature information of the sample point.
And S120, determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned.
The image to be positioned refers to an image obtained after single-molecule imaging, namely an actual image; the sample points correspond to the sample images one to one. The target sample image is a predicted image of the image to be located.
In this embodiment, first, a preset number of candidate points are determined from points on the bezier curve, where the preset number is set by a person skilled in the art according to actual needs. And then, determining corresponding sample points according to the candidate points, and further determining candidate images of the candidate points according to the relation between the sample points and the sample images.
After determining the candidate images of the candidate points, according to the distance between the candidate images and the images to be positioned, wherein the distance represents the similarity between the candidate images and the images to be positioned; and then determining a target sample image of the image to be positioned from the candidate images. Specifically, an absolute difference or a root mean square between the candidate image and the image to be positioned may be calculated as a distance between the candidate image and the image to be positioned, and if the distance is equal to a set distance threshold, an image in the corresponding candidate image may be used as a target sample image of the image to be positioned. Wherein, the distance threshold is set by a person skilled in the art according to actual conditions.
And S130, taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned.
In this embodiment, the feature information of the sample point corresponding to the target sample image is used as the feature information of the point corresponding to the image to be positioned. Illustratively, when the feature information is position information, the position information of the sample point corresponding to the target sample image is used as the position information of the point corresponding to the image to be positioned. Illustratively, when the characteristic information is aberration information, the aberration information of the sample point corresponding to the target sample image is taken as the aberration information of the point corresponding to the image to be positioned. Illustratively, when the feature information is color difference information, the color difference information of the sample point corresponding to the target sample image is used as the color difference information of the point corresponding to the image to be positioned.
According to the invention, a Bezier curve is constructed according to the characteristic information of the sample points, then the target sample image of the image to be positioned is determined according to the points on the Bezier curve, the relation between the sample points and the sample image and the image to be positioned, and further the characteristic information of the sample points corresponding to the target sample image is used as the characteristic information of the points corresponding to the image to be positioned. Compared with the existing Gaussian fitting and cubic spline interpolation method, the technical scheme has the advantages that more imaging information is obtained, memory resources are reduced, and a new thought is provided for multi-dimensional single-molecule positioning imaging.
Example two
Fig. 2 is a flowchart of a feature determining method provided by the second embodiment of the present invention, and on the basis of the second embodiment, an optional implementation is provided by optimizing "determining a target sample image of an image to be positioned according to a point on a bezier curve, a relationship between a sample point and the sample image, and the image to be positioned".
As shown in fig. 2, the method may specifically include:
and S210, constructing a Bezier curve according to the characteristic information of the sample points.
And S220, taking one point from the points on the Bezier curve as a target point.
In this embodiment, a point is randomly selected from points on the bezier curve as a target point.
And S230, determining a predicted image of the target point according to the relation between the sample point and the sample image.
In this embodiment, an image prediction model is constructed according to a relationship between a sample point and a sample image, and a target point is input to the prediction model to obtain a predicted image of the target point.
And S240, calculating the distance between the predicted image of the target point and the image to be positioned.
The distance is used for representing the similarity between a predicted image of the target point and the image to be positioned.
In this embodiment, a result obtained by subtracting the predicted image of the target point from the image to be positioned and taking the absolute value is used as the distance between the predicted image of the target point and the image to be positioned.
Optionally, the sum of the squares of the subtraction of each pixel point between the predicted image of the target point and the image to be positioned can be calculated, and the result of the sum can be used as the distance between the predicted image of the target point and the image to be positioned.
And S250, determining a target sample image of the image to be positioned according to the distance.
In this embodiment, if the distance is smaller than the preset value, the predicted image of the target point is used as the target sample image of the image to be positioned. Wherein, the preset value is set by the person skilled in the art according to the actual situation.
And if the distance is larger than the preset value, constructing a residual error function according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned. Specifically, for points on the bezier curve and an image prediction model determined according to the relationship between the sample point and the sample image, a predicted image corresponding to the points on the bezier curve is determined, and then a residual error function is constructed according to the predicted image corresponding to the points on the bezier curve and the image to be positioned.
It should be noted that the residual function may be constructed once, or may be reconstructed each time after the target point is determined again.
After the residual function is determined, based on the feature information of the target point, calculating a derivative of the residual function to determine a positional relationship between the next predicted point and the target point. The position relationship is used to represent the selection direction of the next predicted point, for example, the bezier curve is a quadratic function curve, and the next predicted point is selected on the right side of the target point or on the left side of the target point. For example, the first-order partial derivatives of the residual function may be calculated, the first-order partial derivatives of the function may be arranged in a matrix in a certain manner, the determinant thereof, i.e., jacobian, may be calculated, and the position relationship between the next predicted point and the target point may be determined according to the result of the jacobian. Further, for more accurate next prediction point, exemplarily, a black plug matrix of the residual function may also be calculated to determine a position relationship between the next prediction point and the target point; the black matrix is a square matrix composed of second-order partial derivatives of a multivariate real-valued function.
After the positional relationship between the next predicted point and the target point is determined, the next predicted point is determined as the target point from among points on the bezier curve according to the positional relationship between the next predicted point and the target point, and the process returns to S230.
And S260, taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned.
According to the technical scheme of the embodiment of the invention, one point is taken from the points on the Bezier curve to be used as a target point, then a predicted image of the target point is determined according to the relation between the sample point and the sample image, the distance between the predicted image of the target point and the image to be positioned is further calculated, and the target sample image of the image to be positioned is determined according to the distance. According to the technical scheme, the target point is introduced, the target sample image of the image to be positioned is determined through the predicted image of the target point, and therefore guarantee is provided for extracting the feature information of the point corresponding to the subsequent image lock to be positioned.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a feature determination apparatus according to a third embodiment of the present invention, which is applicable to a single molecule image positioning situation, and the apparatus can be implemented by software/hardware and can be integrated in an electronic device, such as a server, that carries a feature determination function.
As shown in fig. 3, the apparatus may include a curve construction module 310, a target sample image determination module 320, and a feature information determination module 330, wherein,
a curve construction module 310, configured to construct a bezier curve according to the feature information of the sample points;
the target sample image determining module 320 is configured to determine a target sample image of an image to be positioned according to a point on the bezier curve, a relationship between the sample point and the sample image, and the image to be positioned;
the characteristic information determining module 330 is configured to use the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned.
According to the invention, a Bezier curve is constructed according to the characteristic information of the sample points, then the target sample image of the image to be positioned is determined according to the points on the Bezier curve, the relation between the sample points and the sample image and the image to be positioned, and further the characteristic information of the sample points corresponding to the target sample image is used as the characteristic information of the points corresponding to the image to be positioned. Compared with the existing Gaussian fitting and cubic spline interpolation method, the technical scheme has the advantages that more imaging information is obtained, memory resources are reduced, and a new thought is provided for multi-dimensional single-molecule positioning imaging.
Further, the target sample image determining module 330 includes a target point determining unit, a prediction image determining unit, a distance determining unit, and a target sample image determining unit, wherein,
a target point determination unit for taking a point from points on the bezier curve as a target point;
a predicted image determining unit configured to determine a predicted image of the target point according to a relationship between the sample point and the sample image;
the distance determining unit is used for calculating the distance between the predicted image of the target point and the image to be positioned;
and the target sample image determining unit is used for determining a target sample image of the image to be positioned according to the distance.
Further, the target sample image determining unit includes a positional relationship determining subunit and a target sample image determining subunit, wherein,
a position relation determining subunit, configured to, if the distance is greater than the preset value, calculate a derivative of the residual function based on feature information of the target point to determine a position relation between the next predicted point and the target point; the residual error function is constructed according to points on a Bezier curve, the relation between a sample point and a sample image and the image to be positioned;
the target point determining subunit is used for determining a next prediction point from points on the Bezier curve as a target point according to the characteristic relation between the next prediction point and the target point, and returning to execute the process of calculating the distance between the prediction image of the target point and the image to be positioned until the distance is smaller than a preset value;
and the target sample image determining subunit is used for taking the predicted image of the target point as the target sample image of the image to be positioned.
Further, the derivative calculation subunit is specifically configured to:
a jacobian or blackplug matrix of the residual function is calculated.
Further, the target sample image determining module 330 further comprises a candidate point determining unit and a candidate image determining unit, wherein,
a candidate point determining unit configured to determine a preset number of candidate points from points on the bezier curve;
the candidate image determining unit is used for determining candidate images of the candidate points according to the relation between the sample points and the sample images;
and the target sample image determining unit is also used for determining a target sample image of the image to be positioned from the candidate images according to the distance between the candidate images and the image to be positioned.
Further, the characteristic information is position information.
The characteristic determining device can execute the characteristic determining method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the executing method.
Example four
Fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention, and fig. 4 shows a block diagram of an exemplary device suitable for implementing the embodiments of the present invention. The device shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiments of the present invention.
As shown in FIG. 4, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of embodiments described herein.
The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the electronic device 12, and/or any device (e.g., network card, modem, etc.) that enables the electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement the feature determination method provided by the embodiment of the present invention.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program (or referred to as computer-executable instructions) is stored, where the computer program is used for executing, when executed by a processor, the feature determination method provided in the embodiment of the present invention, where the method includes:
constructing a Bezier curve according to the characteristic information of the sample points;
determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned;
and taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 document, 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. 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 thereof. A computer readable signal medium may 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 wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for embodiments of the present invention may be written in any combination of one or more programming languages, including 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing description is only exemplary of the invention and that the principles of the technology may be employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the embodiments of the present invention have been described in more detail through the above embodiments, the embodiments of the present invention are not limited to the above embodiments, and many other equivalent embodiments can be included without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (7)
1. A method of feature determination, comprising:
constructing a Bezier curve according to the characteristic information of the sample points;
determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned;
taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned;
the determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned comprises the following steps:
taking a point from points on the Bezier curve as a target point;
determining a predicted image of the target point according to the relation between the sample point and the sample image;
calculating the distance between the predicted image of the target point and the image to be positioned;
determining a target sample image of the image to be positioned according to the distance;
determining a target sample image of an image to be positioned according to the distance, comprising:
if the distance is larger than a preset value, calculating a derivative of a residual function based on the characteristic information of the target point so as to determine the position relation between the next predicted point and the target point; the residual error function is constructed according to points on the Bezier curve, the relation between the sample points and the sample image and the image to be positioned;
determining a next prediction point from points on a Bezier curve as a target point according to the characteristic relation between the next prediction point and the target point, and returning to execute the process of calculating the distance between a prediction image of the target point and the image to be positioned until the distance is less than a preset value;
and taking the predicted image of the target point as a target sample image of the image to be positioned.
2. The method of claim 1, wherein computing a derivative of a residual function comprises:
a jacobian or blackplug matrix of the residual function is calculated.
3. The method of claim 1, wherein determining a target sample image of an image to be positioned based on points on a bezier curve, a relationship between sample points and the sample image, and the image to be positioned comprises:
determining a preset number of candidate points from points on a Bezier curve;
determining a candidate image of the candidate point according to the relation between the sample point and the sample image;
and determining a target sample image of the image to be positioned from the candidate images according to the distance between the candidate images and the image to be positioned.
4. A method according to any of claims 1-3, characterized in that the characteristic information is position information.
5. A feature determination device, comprising:
the curve construction module is used for constructing a Bezier curve according to the characteristic information of the sample points;
the target sample image determining module is used for determining a target sample image of the image to be positioned according to the point on the Bezier curve, the relation between the sample point and the sample image and the image to be positioned;
the characteristic information determining module is used for taking the characteristic information of the sample point corresponding to the target sample image as the characteristic information of the point corresponding to the image to be positioned;
the target sample image determination module, comprising:
a target point determination unit for taking a point from points on the bezier curve as a target point;
a predicted image determining unit configured to determine a predicted image of the target point according to a relationship between the sample point and the sample image;
the distance determining unit is used for calculating the distance between the predicted image of the target point and the image to be positioned;
the target sample image determining unit is used for determining a target sample image of the image to be positioned according to the distance;
the target sample image determining unit includes a positional relationship determining subunit and a target sample image determining subunit, wherein,
the position relation determining subunit is configured to, if the distance is greater than the preset value, calculate a derivative of the residual function based on feature information of the target point to determine a position relation between the next predicted point and the target point; the residual error function is constructed according to points on the Bezier curve, the relation between the sample points and the sample image and the image to be positioned;
the target point determining subunit is used for determining a next prediction point from points on the Bezier curve as a target point according to the characteristic relation between the next prediction point and the target point, and returning to execute the process of calculating the distance between the prediction image of the target point and the image to be positioned until the distance is smaller than a preset value;
and the target sample image determining subunit is used for taking the predicted image of the target point as the target sample image of the image to be positioned.
6. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the feature determination method of any one of claims 1-4.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method for determining characteristics of any one of claims 1 to 4.
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