Laser reflection tomography projection registration method based on target contour segmentation and fitting

Abstract

The invention relates to the technical field of laser radar imaging, in particular to a laser reflection tomography projection registration method, system and device based on target contour segmentation and fitting and a medium. The method comprises the following steps: firstly, converting obtained target laser reflection echo projection data into transmission projection data; according to the transmission projection data, a BP algorithm is called to reconstruct a two-dimensional contour, and an initial target image is obtained; secondly, calling a skeleton extraction algorithm to extract an initial target image to obtain skeleton lines; an iterative approximation algorithm is called to segment skeleton lines, and skeleton line segments are fitted; correcting transmission projection data according to a skeleton line segment obtained by fitting; and finally, calling a BP algorithm to reconstruct the corrected transmission projection data to obtain a target image. On the basis of a BP algorithm, contour segmentation and fitting are combined, projection data are corrected, interference caused by random jitter of laser pulse emission and detector receiving is reduced, a reconstructed target contour image is clearer, and optimization of a reconstruction result is achieved.

Description

Technical Field

[0001] The present invention relates to the field of laser radar imaging technology, and in particular to a laser reflection tomography projection registration method, system, equipment and medium based on target contour segmentation and fitting. Background Art

[0002] Currently, research in lidar imaging technology has entered a new phase of development. With advances in optoelectronics, simply using lidar to obtain target information such as distance, speed, and orientation is no longer sufficient to meet the needs of aviation, aerospace, and ground-based systems. Using lidar to achieve high-precision computational imaging has become a new research hotspot.

[0003] Due to limitations such as optical aperture, diffraction limit, and atmospheric turbulence, general optical imaging systems find it difficult to achieve long-distance, high-precision imaging. Therefore, the detection and imaging of space targets at long distances, small targets, and in low-light environments encounter difficulties, which urgently need to be solved.

[0004] Laser reflection tomography uses a single detector to detect a target from multiple angles, obtaining laser echo projection data at different angles and performing image computation and reconstruction to detect and image the target's contours. Under certain conditions, its spatial resolution is independent of range, dependent only on pulse width, detector bandwidth, and noise. It is also insensitive to atmospheric turbulence, offering significant technical advantages and significant military applications in weaponry, aviation, and aerospace.

[0005] In laser reflection tomography technology, there is a critical step, namely projection registration, which aligns the multi-angle echo projection data with the target rotation center, thereby reducing interference caused by random jitter between laser pulse emission and detector reception, and solving the problem of blurred reconstructed images.

[0006] Common projection alignment methods include the reference screen method, feature point tracking, and spectroscopic methods. The reference screen method is the most common projection alignment method used in ground-based verification experiments. It requires the reference screen to remain stationary relative to the laser beam. Multi-angle echo waveforms are then aligned based on its constant distance from the center of rotation. However, ideal reference screens are rarely available near actual targets. Therefore, this method is not suitable for actual target detection. The feature point tracking method is based on points where the reflectance coefficient of the target surface changes suddenly. However, in practice, due to the generally uniform material quality, the target surface often lacks such feature points. Furthermore, the extremely narrow laser pulse width required to distinguish feature points is not practical for system design. The spectroscopic method splits the laser beam into two paths using a beam splitter: one path covers the target, and the other path illuminates the detector as a reference beam. This method offers high accuracy, but due to the significant time delay in waveform reception during long-distance detection, it requires longer acquisition time for the detection circuitry, increasing hardware costs. Summary of the Invention

[0007] To address the above problems, the present invention proposes a laser reflection tomography projection registration method based on target contour segmentation and fitting. This method is based on the BP algorithm and combines contour segmentation and fitting to correct the projection data, thereby reducing the interference caused by random jitter between laser pulse emission and detector reception, making the reconstructed target contour image clearer and optimizing the reconstruction results.

[0008] The specific implementation contents of the present invention are as follows:

[0009] A laser reflection tomography projection registration method based on target contour segmentation and fitting specifically comprises the following steps:

[0010] Step S1: converting the acquired target laser reflection echo projection data into transmission projection data;

[0011] Step S2: Based on the transmission projection data, the BP algorithm is called to reconstruct the two-dimensional contour to obtain the initial target image;

[0012] Step S3: Calling the skeleton extraction algorithm to extract the initial target image and obtain the skeleton line;

[0013] Step S4: calling the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segment;

[0014] Step S5: Correcting the transmission projection data according to the fitted skeleton line segments;

[0015] Step S6: Call the BP algorithm to reconstruct the corrected transmission projection data to obtain the target image.

[0016] In order to better implement the present invention, further, step S3 specifically includes the following steps:

[0017] Step S31: binarizing the initial target image according to the set threshold value to obtain a binarized image;

[0018] Step S32: Calculate pixel distances based on background pixels of the binary image to obtain a distance-transformed image;

[0019] Step S33: Calculate the local maximum point of the image after distance transformation;

[0020] Step S34: Mark the local maximum point as a skeleton line pixel, and aggregate the skeleton line pixels to obtain a skeleton line.

[0021] In order to better implement the present invention, further, the specific operation of step S31 is: according to the set threshold T, the pixel values ​​of the initial target image I(x, y) greater than the threshold T are assigned to 1, and the pixel values ​​of the initial target image I(x, y) less than the threshold T are assigned to 0, thereby obtaining a binary image B(x, y).

[0022] In order to better implement the present invention, the specific operation of step S33 is as follows: according to the distance value set N(x, y)=d1, d2, d3, ..., d n ; If the pixel distance D(x,y)≥d i , i=1,2,3,……,n, then the current pixel is the local maximum point.

[0023] In order to better implement the present invention, further, step S4 specifically includes the following steps:

[0024] Step S41: Calculate the angle change between adjacent points on the skeleton line;

[0025] Step S42: Determine the type of skeleton line according to the angle change, and fit the skeleton line in segments.

[0026] In order to better implement the present invention, further, step S41 specifically includes the following steps:

[0027] Step S411: Get three adjacent points (x i ,y i )、(x i+1 ,y i+1 )、(x i+2 ,y i+2 ), get the first vector v i , the second vector v i+1 , and calculate the first vector v i With the second vector v i+1 The angle between the two is θ, and the first angle θ is obtained. i+1 ;

[0028] Step S412: Get three adjacent points (x i-1 ,y i-1 )、(x i ,y i )、(x i+1 ,y i+1 ), get the third vector v i-1 、The fourth vector v i , and calculate the third vector v i-1 With the fourth vector v i The angle between the two angles is θ. i ;

[0029] Step S413: Calculate the first angle θ i+1 and the second angle θ i If the absolute value of the difference exceeds the set angle threshold, it is determined that there is a segmentation point and the skeleton line is segmented according to the segmentation point.

[0030] In order to better implement the present invention, further, the step S42 specifically includes the following steps:

[0031] Step S421: Determine the type of the skeleton line based on the segmentation points and the set straight line segmentation threshold. If the current skeleton line segment is a straight line, construct a straight line equation based on the skeleton line pixels to obtain the skeleton straight line segment.

[0032] Step S422: Determine the type of the skeleton line based on the segmentation points and the set curve segmentation threshold. If the current skeleton line segment is a curve, estimate initial parameters based on the skeleton line pixels.

[0033] Step S423: construct an error function based on the skeleton line pixels and calculate the total error;

[0034] Step S424: constructing a Jacobian matrix based on the error function and the estimated initial parameters;

[0035] Step S425: construct an iterative update equation based on the Jacobian matrix, the set step size, the identity matrix, and the total error;

[0036] Step S426: Calculate the Jacobian matrix and the total error according to the iterative update equation until the total error is less than the set error threshold, and obtain the optimal parameters;

[0037] Step S427: Construct an ellipse equation according to the optimal parameters to obtain a skeleton curve segment.

[0038] Based on the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting, in order to better realize the present invention, a laser reflection tomography projection registration system based on target contour segmentation and fitting is further proposed, which is used to execute the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting, including a receiving conversion unit, a reconstruction unit, a skeleton line extraction unit, a fitting unit, a correction unit, and a reconstruction unit;

[0039] The receiving and converting unit is used to convert the acquired target laser reflection echo projection data into transmission projection data;

[0040] The reconstruction unit is used to call the BP algorithm to reconstruct the two-dimensional contour according to the transmission projection data to obtain the initial target image;

[0041] The skeleton line extraction unit is used to call the skeleton extraction algorithm to extract the initial target image and obtain the skeleton line;

[0042] The fitting unit is used to call the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segment;

[0043] The correction unit is used to correct the transmission projection data according to the skeleton line segments obtained by fitting;

[0044] The reconstruction unit is used to call the BP algorithm to reconstruct the corrected transmission projection data to obtain a target image.

[0045] Based on the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting, in order to better implement the present invention, an electronic device is further proposed, including a memory and a processor; a computer program is stored on the memory; when the computer program is executed on the processor, the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting is implemented.

[0046] Based on the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting, in order to better implement the present invention, a computer-readable storage medium is further proposed, on which computer instructions are stored; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting is implemented.

[0047] The present invention has the following beneficial effects:

[0048] (1) The present invention is based on the BP algorithm, combines contour segmentation and fitting, and corrects the projection data, reducing the interference caused by random jitter of laser pulse emission and detector reception, making the reconstructed target contour image clearer and optimizing the reconstruction results.

[0049] (2) The present invention incorporates the factors of contour segmentation and fitting, and performs more detailed and accurate correction for target contours with relatively complex shapes. This is done entirely at the algorithm level, without the need for additional hardware and experimental additions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a schematic flowchart of the laser reflection tomography projection registration method based on target contour segmentation and fitting provided by the present invention. DETAILED DESCRIPTION

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be understood that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be regarded as limiting the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technical personnel in this field without making creative work are within the scope of protection of the present invention.

[0052] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "disposed," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections, electrical connections; direct connections, indirect connections through an intermediate medium, or internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0053] Example 1:

[0054] This embodiment proposes a laser reflection tomography projection registration method based on target contour segmentation and fitting, which specifically includes the following steps:

[0055] Step S1: Convert the acquired target laser reflection echo projection data into transmission projection data.

[0056] Step S2: Based on the transmission projection data, the BP algorithm is called to reconstruct the two-dimensional contour to obtain the initial target image.

[0057] Step S3: Call the skeleton extraction algorithm to extract the initial target image and obtain the skeleton line.

[0058] The step S3 specifically includes the following steps:

[0059] Step S31: binarizing the initial target image according to the set threshold value to obtain a binarized image;

[0060] The specific operation of step S31 is: according to the set threshold T, the pixel values ​​of the initial target image I(x,y) greater than the threshold T are assigned to 1, and the pixel values ​​of the initial target image I(x,y) less than the threshold T are assigned to 0, thereby obtaining a binary image B(x,y).

[0061] Step S32: Calculate pixel distances based on background pixels of the binary image to obtain a distance-transformed image;

[0062] Step S33: Calculate the local maximum point of the image after distance transformation;

[0063] The specific operation of step S33 is: according to the distance value set N(x, y)=d1, d2, d3, ..., d n ; If the pixel distance D(x,y)≥d i , i=1,2,3,……,n, then the current pixel is the local maximum point.

[0064] Step S34: Mark the local maximum point as a skeleton line pixel, and aggregate the skeleton line pixels to obtain a skeleton line.

[0065] Step S4: Call the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segment.

[0066] The step S4 specifically includes the following steps:

[0067] Step S41: Calculate the angle change between adjacent points on the skeleton line;

[0068] The step S41 specifically includes the following steps:

[0069] Step S411: Get three adjacent points (x i ,y i )、(x i+1 ,y i+1 )、(x i+2 ,y i+2 ), get the first vector v i , the second vector v i+1 , and calculate the first vector v i With the second vector v i+1 The angle between the two is θ, and the first angle θ is obtained. i+1 ;

[0070] Step S412: Get three adjacent points (x i-1 ,y i-1 )、(x i ,y i )、(x i+1 ,y i+1 ), get the third vector v i-1、The fourth vector v i , and calculate the third vector v i-1 With the fourth vector v i The angle between the two angles is θ. i ;

[0071] Step S413: Calculate the first angle θ i+1 and the second angle θ i If the absolute value of the difference exceeds the set angle threshold, it is determined that there is a segmentation point and the skeleton line is segmented according to the segmentation point.

[0072] Step S42: Determine the type of skeleton line according to the angle change, and fit the skeleton line in segments.

[0073] The step S42 specifically includes the following steps:

[0074] Step S421: Determine the type of the skeleton line based on the segmentation points and the set straight line segmentation threshold. If the current skeleton line segment is a straight line, construct a straight line equation based on the skeleton line pixels to obtain the skeleton straight line segment.

[0075] Step S422: Determine the type of the skeleton line based on the segmentation points and the set curve segmentation threshold. If the current skeleton line segment is a curve, estimate initial parameters based on the skeleton line pixels.

[0076] Step S423: construct an error function based on the skeleton line pixels and calculate the total error;

[0077] Step S424: constructing a Jacobian matrix based on the error function and the estimated initial parameters;

[0078] Step S425: construct an iterative update equation based on the Jacobian matrix, the set step size, the identity matrix, and the total error;

[0079] Step S426: Calculate the Jacobian matrix and the total error according to the iterative update equation until the total error is less than the set error threshold, and obtain the optimal parameters;

[0080] Step S427: Construct an ellipse equation according to the optimal parameters to obtain a skeleton curve segment.

[0081] Step S5: Correcting the transmission projection data based on the fitted skeleton line segments.

[0082] Step S6: Call the BP algorithm to reconstruct the corrected transmission projection data to obtain the target image.

[0083] Working principle: This embodiment first converts the acquired target laser reflection echo projection data into transmission projection data; and based on the transmission projection data, calls the BP algorithm to reconstruct the two-dimensional contour to obtain the initial target image; secondly, calls the skeleton extraction algorithm to extract the initial target image to obtain the skeleton line; and calls the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segments; then, based on the fitted skeleton line segments, corrects the transmission projection data; finally, calls the BP algorithm to reconstruct the corrected transmission projection data to obtain the target image; based on the BP algorithm, the contour segmentation and fitting are combined to correct the projection data, thereby reducing the interference caused by the random jitter of the laser pulse emission and the detector reception, making the reconstructed target contour image clearer and realizing the optimization of the reconstruction results.

[0084] Example 2:

[0085] This embodiment is based on the above embodiment 1. Figure 1 As shown, a specific embodiment is described in detail, which specifically includes the following steps.

[0086] Step S1: using a single detector to obtain the laser reflection echo projection data of the target, and converting the laser reflection echo projection data into transmission projection data.

[0087] Step S2: Use the BP (Back Projection) algorithm to reconstruct the target's two-dimensional contour to obtain a reconstructed initial target image.

[0088] Step S3: Extract skeleton lines from the initial target image using a skeleton extraction algorithm.

[0089] Bone extraction protocol:

[0090] Step S31: Binarization: First, binarize the input image. Assume the input image is I(x, y) and the threshold is T. Pixel values ​​greater than T are assigned a value of 1, and all other values ​​are assigned a value of 0. 0 represents background, and 1 represents object. This yields the binary image B(x, y).

[0091] Step S32: Calculate the distance transform; for the binary image B(x, y), calculate its distance transform D(x, y). Various distance metrics can be used here, such as Euclidean distance, Manhattan distance, etc. Taking Manhattan distance as an example, for each object pixel (x, y), its distance D(x, y) is calculated as follows:

[0092]

[0093] Among them, S is the background pixel set.

[0094] Step S33: Find the local maximum; in the distance transformed image D(x, y), find the local maximum point. The condition for a pixel (x, y) to be a local maximum point is that its distance value is greater than or equal to the distance values ​​of all pixels in its neighborhood (8-neighborhood). Let N(x, y) be the distance value set d1, d2, d3, ..., d8 of the 8-neighborhood pixels of the pixel (x, y). If D(x, y) ≥ d i ,i=1,2,3,……,8, then the pixel is the local maximum point;

[0095] Step S34: Extract skeleton lines: Mark all local maximum points as skeleton line pixels. The set of these pixels is the extracted skeleton line.

[0096] Step S4: Using the iterative approximation principle, the skeleton line of the initial target image is segmented and decomposed into straight lines, circular arcs and elliptical arcs.

[0097] Skeleton segmentation and fitting processing, after the above steps are completed, the skeleton line is obtained, which is actually a point set, assuming that C = {p1, p2, p3..., p n}; where p i =(x i ,y i ) represents the coordinates of the i-th point on the skeleton line;

[0098] Step S41: Calculate the angle change between adjacent points on the skeleton line, i.e., the curvature; segment the skeleton line based on the angle change, and take three adjacent points on the skeleton line to form a vector, as follows:

[0099]

[0100] Calculate the angle θ between the two i+1 , the formula is:

[0101]

[0102] Calculate θ in the same way i ;

[0103] When |θ i -θ i+1 When the threshold is exceeded, it is considered a segmentation point. The threshold here needs to be adjusted based on the segmentation effect.

[0104] Step S42: Linear fitting: For a segment of a skeleton line that is considered to be a straight line, assume that the segment point is P = {p j , p j+1 , p j+2 …, p k}, let the equation of the line be y=mx+c, then the error function is:

[0105]

[0106] Minimize E by solving the following system of equations:

[0107]

[0108] Where n = k - j + 1;

[0109] Elliptical arc fitting (circular arc is a kind of elliptical arc, so it is included), for a skeleton line segment that is considered to be an elliptical arc, assume that its point set is P = {p1, p2, p3..., p n};

[0110] The goal is to find a set of parameters (x0, y0, a, b) so that the point set P best satisfies the following ellipse equation:

[0111]

[0112] 1. Initial parameter estimation;

[0113]

[0114] a=max(d i )

[0115] b=min(d i )

[0116] 2. Construct error function:

[0117] For each point (x i ,y i ), the error function is:

[0118]

[0119] The total error is:

[0120]

[0121] 3. Calculate the Jacobian matrix. The Jacobian matrix J is as follows:

[0122]

[0123] The element J i，j Indicates E i For parameter p j The partial derivative of , p = (x0, y0, a, b);

[0124] The specific process of finding partial derivatives is as follows:

[0125] First, transform the ellipse equation into:

[0126]

[0127] make but:

[0128]

[0129] in,

[0130] Similarly, we can calculate and

[0131] 4. Iteratively calculate parameters and define the iterative update formula:

[0132] Δp=-(J T J+λI) -1 J T e

[0133] Where J is the Jacobian matrix, is the set step size, I is the identity matrix, and e is the error vector e = (E1, E2, ..., E n ) T ;

[0134] In each iteration, p new =p old +Δp, and then recalculate E and J until E is less than the preset threshold. In this way, the optimal parameters that meet the conditions can be obtained. If a and b in the parameters are very close or even completely equal, then this segment is an arc.

[0135] Step S5: Correct the corresponding transmission projection data according to the fitting result.

[0136] Step S6: Using the BP algorithm, the corrected projection data obtained in step S5 is used to reconstruct the target image, which is the final image.

[0137] Working principle: This embodiment introduces contour segmentation and fitting into the process of laser reflection tomography image reconstruction to achieve correction of projection data, thereby achieving the purpose of optimizing the reconstruction results of echo projection data.

[0138] The rest of this embodiment is the same as that of the above-mentioned embodiment 1, and therefore will not be described in detail.

[0139] Example 3:

[0140] This embodiment, based on any one of the above-mentioned embodiments 1-2, proposes a laser reflection tomography projection registration system based on target contour segmentation and fitting, which is used to execute the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting, and includes a receiving and converting unit, a reconstruction unit, a skeleton line extraction unit, a fitting unit, a correction unit, and a reconstruction unit;

[0141] The receiving and converting unit is used to convert the acquired target laser reflection echo projection data into transmission projection data;

[0142] The reconstruction unit is used to call the BP algorithm to reconstruct the two-dimensional contour according to the transmission projection data to obtain the initial target image;

[0143] The skeleton line extraction unit is used to call the skeleton extraction algorithm to extract the initial target image and obtain the skeleton line;

[0144] The fitting unit is used to call the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segment;

[0145] The correction unit is used to correct the transmission projection data according to the skeleton line segments obtained by fitting;

[0146] The reconstruction unit is used to call the BP algorithm to reconstruct the corrected transmission projection data to obtain a target image.

[0147] This embodiment also proposes an electronic device, including a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting is implemented.

[0148] This embodiment further proposes a computer-readable storage medium having computer instructions stored thereon. When the computer instructions are executed on the above-mentioned electronic device, the above-mentioned laser reflection tomography projection registration method based on target contour segmentation and fitting is implemented.

[0149] The rest of this embodiment is the same as any of the above-mentioned embodiments 1 and 2, and thus will not be described in detail.

[0150] The processor involved in the embodiments of the present application may be a chip. For example, it may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0151] The memory involved in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0152] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0153] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0154] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is merely a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0156] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located on a single device or distributed across multiple devices. Some or all of the modules may be selected to achieve the purpose of this embodiment based on actual needs.

[0157] In addition, the functional modules in the various embodiments of the present application may be integrated into one device, or each module may exist physically separately, or two or more modules may be integrated into one device.

[0158] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using a software program, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loading and executing computer program instructions on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode to another website, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more media that can be integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a DVD), or a semiconductor medium (eg, a solid state disk (SSD)).

[0159] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A laser reflection tomography projection registration method based on target contour segmentation and fitting, characterized in that: The specific steps include: Step S1: converting the acquired target laser reflection echo projection data into transmission projection data; Step S2: Based on the transmission projection data, the BP algorithm is called to reconstruct the two-dimensional contour to obtain the initial target image; Step S3: Calling the skeleton extraction algorithm to extract the initial target image and obtain the skeleton line; Step S4: calling the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segment; Step S5: Correcting the transmission projection data according to the fitted skeleton line segments; Step S6: Call the BP algorithm to reconstruct the corrected transmission projection data to obtain the target image.

2. The laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 1, characterized in that: The step S3 specifically includes the following steps: Step S31: binarizing the initial target image according to the set threshold value to obtain a binarized image; Step S32: Calculate pixel distances based on background pixels of the binary image to obtain a distance-transformed image; Step S33: Calculate the local maximum point of the image after distance transformation; Step S34: Mark the local maximum point as a skeleton line pixel, and aggregate the skeleton line pixels to obtain a skeleton line.

3. The laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 2, characterized in that: The specific operation of step S31 is: according to the set threshold T, the pixel values ​​of the initial target image I(x,y) greater than the threshold T are assigned to 1, and the pixel values ​​of the initial target image I(x,y) less than the threshold T are assigned to 0, thereby obtaining a binary image B(x,y).

4. The laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 2, characterized in that: The specific operation of step S33 is: according to the distance value set N(x, y)=d1, d2, d3, ..., d n ; If the pixel distance D(x,y)≥d i , i=1,2,3,……,n, then the current pixel is the local maximum point.

5. The laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 1, characterized in that: The step S4 specifically includes the following steps: Step S41: Calculate the angle change between adjacent points on the skeleton line; Step S42: Determine the type of skeleton line according to the angle change, and fit the skeleton line in segments.

6. The laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 5, characterized in that: The step S41 specifically includes the following steps: Step S411: Get three adjacent points (x i ,y i )、(x i+1 ,y i+1 )、(x i+2 ,y i+2 ), get the first vector v i , the second vector v i+1 , and calculate the first vector v i With the second vector v i+1 The angle between the two is θ, and the first angle θ is obtained. i+1 ; Step S412: Get three adjacent points (x i-1 ,y i-1 )、(x i ,y i )、(x i+1 ,y i+1 ), get the third vector v i-1 、The fourth vector v i , and calculate the third vector v i-1 With the fourth vector v i The angle between the two angles is θ. i ; Step S413: Calculate the first angle θ i+1 and the second angle θ i If the absolute value of the difference exceeds the set angle threshold, it is determined that there is a segmentation point and the skeleton line is segmented according to the segmentation point.

7. The laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 6, characterized in that: The step S42 specifically includes the following steps: Step S421: Determine the type of the skeleton line based on the segmentation points and the set straight line segmentation threshold. If the current skeleton line segment is a straight line, construct a straight line equation based on the skeleton line pixels to obtain the skeleton straight line segment. Step S422: Determine the type of the skeleton line based on the segmentation points and the set curve segmentation threshold. If the current skeleton line segment is a curve, estimate initial parameters based on the skeleton line pixels. Step S423: construct an error function based on the skeleton line pixels and calculate the total error; Step S424: constructing a Jacobian matrix based on the error function and the estimated initial parameters; Step S425: construct an iterative update equation based on the Jacobian matrix, the set step size, the identity matrix, and the total error; Step S426: Calculate the Jacobian matrix and the total error according to the iterative update equation until the total error is less than the set error threshold, and obtain the optimal parameters; Step S427: Construct an ellipse equation according to the optimal parameters to obtain a skeleton curve segment.

8. A laser reflection tomography projection registration system based on target contour segmentation and fitting, used to execute the laser reflection tomography projection registration method based on target contour segmentation and fitting according to claim 1, characterized in that: It includes a receiving conversion unit, a reconstruction unit, a skeleton line extraction unit, a fitting unit, a correction unit, and a reconstruction unit; The receiving and converting unit is used to convert the acquired target laser reflection echo projection data into transmission projection data; The reconstruction unit is used to call the BP algorithm to reconstruct the two-dimensional contour according to the transmission projection data to obtain the initial target image; the skeleton line extraction unit is used to call the skeleton extraction algorithm to extract the initial target image to obtain the skeleton line; The fitting unit is used to call the iterative approximation algorithm to segment the skeleton line and fit the skeleton line segment; The correction unit is used to correct the transmission projection data according to the skeleton line segments obtained by fitting; The reconstruction unit is used to call the BP algorithm to reconstruct the corrected transmission projection data to obtain a target image.

9. An electronic device, characterized in that: The invention comprises a memory and a processor; a computer program is stored in the memory; when the computer program is executed on the processor, the laser reflection tomography projection registration method based on target contour segmentation and fitting according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device according to claim 9, the laser reflection tomography projection registration method based on target contour segmentation and fitting according to any one of claims 1 to 7 is implemented.

Images