A continuous blind element identification marking and statistical method, system and storage medium

Abstract

The application discloses a continuous blind element identification, labeling and statistics method and system and a storage medium, and relates to the field of continuous blind element identification. The method comprises the following steps: acquiring an original blind element data matrix by using a planar array infrared focal plane detector; determining continuous blind elements in the original blind element data matrix; deleting continuous blind elements with a size smaller than a minimum continuous blind element size p in the original blind element data matrix to obtain a new blind element data matrix; saving information of the continuous blind elements in the new blind element data matrix into a structure; creating a continuous blind element statistics matrix according to information of the maximum continuous blind element in the structure; statistically processing information of a single continuous blind element by using the continuous blind element statistics matrix; determining a continuous blind element distribution map according to the new blind element data matrix; and labeling centroid position coordinates and envelope size data of the continuous blind elements according to the continuous blind element distribution map. The application can automatically identify, label and statistically process the size and position of continuous blind elements with a size greater than a certain value.

Description

Technical Field

[0001] This invention relates to the field of continuous blind pixel identification, and in particular to a continuous blind pixel identification annotation and statistical method, system and storage medium. Background Technology

[0002] For array infrared focal plane detectors, the presence of consecutive blind elements can affect the detector's target recognition and response, thus impacting its performance. Furthermore, different application scenarios have different requirements regarding the size and distribution of consecutive blind elements in array infrared focal plane detectors. Some applications require the size of consecutive blind elements in the array infrared focal plane detector to be less than a fixed value, and also specify the location of the consecutive blind elements. Therefore, the size and location of consecutive blind elements larger than a certain value need to be identified and labeled in the detector blind element distribution map obtained from testing. Currently, there are two methods for processing consecutive blind element data. Mao Jingxiang et al. mentioned a method for statistically analyzing consecutive blind elements in their article "Statistical Analysis of Blind Elements in Infrared Focal Plane Detectors Using MATLAB" (Infrared, Vol. 30, No. 3, pp. 43-45). Hua Hua et al. disclosed a method for screening consecutive blind elements of specific shapes in their invention patent "A Method for Screening Blind Elements in Infrared Focal Plane Detectors" (CN103310108B). Neither of these two methods can identify consecutive blind cells larger than a certain value, nor can they label the locations and sizes of all blind cells that meet the requirements on the blind cell distribution map. Currently, the identification and labeling of consecutive blind cells larger than a certain value can only be done manually based on the blind cell distribution map obtained from testing, which is inefficient. As the scale of detectors continues to increase, the efficiency and accuracy of relying on manual statistics will further decrease.

[0003] Therefore, there is an urgent need to propose a method for automatically identifying and labeling the size and position of continuous blind cells with a size greater than a certain value. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and storage medium for continuous blind cell identification, labeling, and statistics, which can automatically identify, label, and count the size and position of continuous blind cells larger than a certain value.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A continuous blind pixel identification, annotation, and statistical method, comprising:

[0007] The original blind data matrix is ​​obtained using an area array infrared focal plane detector; and continuous blind elements in the original blind data matrix are determined.

[0008] Delete consecutive blind elements in the original blind data matrix whose size is smaller than the smallest consecutive blind element size p, to obtain a new blind data matrix;

[0009] The information of consecutive blind elements in the new blind data matrix is ​​saved to a structure; the information of the consecutive blind elements includes: the size of the consecutive blind element, the centroid position coordinates, and the envelope size data;

[0010] Based on the information of the largest continuous blind element in the structure, a continuous blind element statistical matrix is ​​created; and the information of a single continuous blind element is statistically analyzed using the continuous blind element statistical matrix.

[0011] Determine the distribution map of continuous blind elements based on the new blind element data matrix;

[0012] The centroid coordinates and envelope size data of the continuous blind cells are labeled based on the distribution map of the continuous blind cells.

[0013] Optionally, determining the continuous blind elements in the original blind data matrix specifically includes:

[0014] Continuous blind elements in the original blind data matrix are determined using either an 8-neighborhood judgment method or a 4-neighborhood judgment method.

[0015] Optionally, saving the information of consecutive blind elements in the new blind data matrix into a structure specifically includes:

[0016] The size of consecutive blind cells is stored in the BP_size matrix;

[0017] The BP_position matrix is ​​used to store the centroid coordinates of continuous blind elements;

[0018] The BP_bound matrix is ​​used to store the envelope size data of continuous blind elements.

[0019] Optionally, the step of labeling the centroid coordinates and envelope size data of the continuous blind cells based on the continuous blind cell distribution map further includes:

[0020] Remove consecutive blind cell size items with a quantity of 0.

[0021] A continuous blind pixel identification, labeling, and statistical system, comprising:

[0022] The original blind data element acquisition module is used to acquire the original blind data element matrix using an area array infrared focal plane detector and to determine the continuous blind elements in the original blind data element matrix.

[0023] The new blind data matrix determination module is used to delete consecutive blind elements in the original blind data matrix whose size is smaller than the smallest consecutive blind element size p, and obtain a new blind data matrix.

[0024] A storage module is used to save the information of continuous blind elements in the new blind data matrix into a structure; the information of the continuous blind elements includes: the size of the continuous blind element, the centroid position coordinates, and the envelope size data;

[0025] The continuous blind cell statistics matrix module is used to create a continuous blind cell statistics matrix based on the information of the largest continuous blind cell in the structure; and to use the continuous blind cell statistics matrix to perform statistics on the information of a single continuous blind cell.

[0026] The continuous blind cell distribution map determination module is used to determine the continuous blind cell distribution map based on the new blind cell data matrix;

[0027] The continuous blind element distribution map annotation module is used to annotate the centroid coordinates and envelope size data of continuous blind elements based on the continuous blind element distribution map.

[0028] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned continuous blind pixel identification, labeling, and statistical method.

[0029] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0030] The present invention provides a continuous blind cell identification, labeling, and statistical method, system, and storage medium. It deletes continuous blind cells in the original blind cell data matrix whose size is smaller than the smallest continuous blind cell size *p*, obtaining a new blind cell data matrix. Then, it determines the continuous blind cell distribution map based on the new blind cell data matrix. Based on the continuous blind cell distribution map, it labels and statistically analyzes the centroid coordinates and envelope size data of the continuous blind cells. The present invention identifies all continuous blind cells larger than a certain value on the continuous blind cell distribution map of an array infrared focal plane detector, labels them on the continuous blind cell distribution map, and simultaneously counts the number of continuous blind cells of each size. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of a continuous blind cell identification, labeling, and statistical method provided by the present invention;

[0033] Figure 2 This is a binary graph of the original blind data matrix;

[0034] Figure 3This is a schematic diagram showing the identification and annotation results (continuous blind cell size ≥ 5) and statistical results of continuous blind cells. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] The purpose of this invention is to provide a method, system, and storage medium for continuous blind cell identification, labeling, and statistics, which can identify all continuous blind cells larger than a certain value on the blind cell distribution map of an array infrared focal plane detector, label them on the blind cell distribution map, and count the number of continuous blind cells of each size.

[0037] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0038] like Figure 1 As shown, the continuous blind cell identification, labeling, and statistical method provided by this invention includes:

[0039] S101, using an area-array infrared focal plane detector, obtain the original blind data matrix BP = [bp ij ]; the element bp ij The value of can be 0 (non-blind) or 1 (blind), and the matrix size is M×N, where M is the number of columns and N is the number of rows; the smallest continuous blind cell size to be identified is p.

[0040] Continuous blind cells in the original blind data matrix are determined using either an 8-neighborhood determination method or a 4-neighborhood determination method. The 8-neighborhood determination method considers a blind cell as connected if it has blind cells in any of its four directions (up, down, left, right, upper left, upper right, lower left, lower right). The 4-neighborhood determination method considers a blind cell as connected only if it has blind cells in any of its four directions (up, down, left, right).

[0041] S102, delete consecutive blind cells in the original blind data matrix whose size is smaller than the smallest consecutive blind cell size p, to obtain a new blind data matrix BP2; BP2 is:

[0042] BP2=bwareaopen(BP,p,conn).

[0043] Here, `bwareaopen` is a function in the MATLAB software. (The value of `conn` represents the continuous blind cell detection method described above, and can be either 8 or 4.)

[0044] S103, save the information of continuous blind elements in the new blind data matrix BP2 to the structure BP_info; where BP_info = regionprops(BP2,'Area','Centroid','BoundingBox'). `regionprops` is a function in MATLAB. This structure contains three types of data: BP_info.Area contains the size data of each continuous blind element; BP_info.Centroid contains the centroid position data of each continuous blind element; BP_BoundingBox contains the envelope size data of each continuous blind element. The information of the continuous blind element includes: the size of the continuous blind element, the centroid position coordinates, and the envelope size data.

[0045] Specifically, the size of continuous blind cells is stored in the matrix BP_size = [BP_info.Area]; the square brackets in the formula are used to integrate the data into a matrix.

[0046] The BP_position = cat(1, BP_info.Centroid) matrix is ​​used to store the centroid coordinates of continuous blind elements.

[0047] The envelope size data of continuous blind cells is stored using the BP_bound = cat(1, BP_info.BoundingBox) matrix.

[0048] cat is a function in the MATLAB software.

[0049] S104. Based on the information of the largest continuous blind cell in the BP_size matrix in the structure, create a continuous blind cell statistics matrix BP_count; and use the continuous blind cell statistics matrix BP_count to count the information of a single continuous blind cell; the BP_count matrix has a size of max(BP_size) rows and 2 columns, where max(BP_size) is the size of the largest continuous blind cell in BP_size, the first column of the matrix is ​​used to store the size of each type of continuous blind cell, starting from 1 up to max(BP_size), and the second column is used to store the number of continuous blind cells of each size.

[0050] S105, Determine the continuous blind cell distribution map based on the new blind cell metadata matrix, specifically:

[0051] Imshow (~BP2).

[0052] Hold on.

[0053] Here, `Imshow` and `hold` are functions in the MATLAB software. The purpose of the `holdon` statement is to keep the plotting interface open, preparing for the subsequent continuous blind labeling.

[0054] S106, Label the centroid coordinates and envelope size data of the continuous blind elements based on the distribution map of the continuous blind elements.

[0055] On the continuous blind cell distribution map, the position of a single continuous blind cell is marked with a circle. Assuming this continuous blind cell is the k-th one, we have:

[0056] plot(BP_position(k,1),BP_position(k,2),'ko','MarkerSize',sqrt(BP_bound(k,3)^2+BP_bound(k,4)^2)+q).

[0057] In this context, plot and sqrt are both functions in MATLAB. In 'ko', k indicates that the circle color is black, but it can also be set to other colors, such as b for blue, r for red, etc. o indicates that a circle is being drawn. 'MarkerSize' sets the size of the circle, which automatically changes according to the size of the continuous blind cells. q is the offset of the circle's edge from the edge of the continuous blind cell envelope, which can be set freely.

[0058] On the continuous blind cell distribution map, the size of a single continuous blind cell is marked. Assuming this continuous blind cell is the k-th one, we have:

[0059] text(BP_position(k,1)+BP_bound(k,3) / 2+m,BP_position(k,2)+BP_bound(k,4) / 2+n,num2str(BP_size(k))).

[0060] Here, text and num2str are functions in MATLAB software, and m and n are the offsets of the marked text relative to the continuous blind cells, which can be set freely.

[0061] The size of the k-th consecutive blind element is recorded in the matrix BP_count, assuming its size is s. Then, the value of the element in the s-th row and 2-th column of the matrix BP_count is incremented by 1.

[0062] Complete the labeling of the size and position of all consecutive blind cells that meet the conditions, as well as the consecutive blind cell statistical matrix. Iterate through all consecutive blind cells that meet the conditions. The number of the last consecutive blind cell is given by length(BP_size), where length is a function in MATLAB software.

[0063] To improve the continuous blind cell statistics matrix, i.e., to remove the consecutive blind cell terms with a count of 0, we have:

[0064] BP_count(BP_count(:,2)==0,:)=[].

[0065] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:

[0066] This embodiment identifies, labels, and counts continuous blind elements in a 512×512 infrared focal plane detector. Figure 2 This is a binary image of the original blind data matrix of the detector. The minimum size of the continuous blind cells to be identified is set to 5, and the method for determining continuous blind cells is conn = 4 (i.e., 4-neighborhood). The calculated result is obtained using a pre-written calculation program. Figure 3 The result.

[0067] Corresponding to the above method, the present invention also provides a continuous blind pixel identification, annotation, and statistical system, comprising:

[0068] The original blind data matrix acquisition module is used to acquire the original blind data matrix using an area array infrared focal plane detector and to determine the continuous blind elements in the original blind data matrix.

[0069] The new blind data matrix determination module is used to delete consecutive blind elements in the original blind data matrix whose size is smaller than the smallest consecutive blind element size p, to obtain a new blind data matrix.

[0070] The storage module is used to save the information of continuous blind elements in the new blind data matrix into a structure; the information of the continuous blind elements includes: the size of the continuous blind element, the centroid position coordinates, and the envelope size data.

[0071] The Continuous Blind Element Statistical Matrix module is used to create a continuous blind element statistical matrix based on the information of the largest continuous blind element in the structure; and to use the continuous blind element statistical matrix to perform statistics on the information of a single continuous blind element.

[0072] The continuous blind cell distribution map determination module is used to determine the continuous blind cell distribution map based on the new blind cell data matrix.

[0073] The continuous blind element distribution map annotation module is used to annotate the centroid coordinates and envelope size data of continuous blind elements based on the continuous blind element distribution map.

[0074] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the continuous blind pixel identification, labeling and statistical method described above.

[0075] Based on the above description, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned computer storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0076] This invention boasts advantages such as simple program implementation, fast computation speed, and wide applicability. The method can be implemented using MATLAB software in no more than 20 lines of code. Only a single loop is needed to complete the identification, labeling, and statistical analysis of all eligible continuous blind cells. This method is applicable to area array infrared focal plane detectors of any size.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0078] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A continuous blind pixel identification, labeling, and statistical method, characterized in that, include: The raw blind data matrix is ​​obtained using an area array infrared focal plane detector; And identify consecutive blind elements in the original blind data matrix; Delete consecutive blind elements in the original blind data matrix whose size is smaller than the smallest consecutive blind element size p, to obtain a new blind data matrix; Save the information of consecutive blind elements in the new blind data matrix into a structure; The information of the continuous blind element includes: the size of the continuous blind element, the centroid position coordinates, and the envelope size data; Based on the information of the largest continuous blind element in the structure, a continuous blind element statistical matrix is ​​created; and the information of a single continuous blind element is statistically analyzed using the continuous blind element statistical matrix. Determine the distribution map of continuous blind elements based on the new blind element data matrix; The centroid coordinates and envelope size data of the continuous blind elements are labeled based on the distribution map of the continuous blind elements. The step of saving the information of consecutive blind elements in the new blind data matrix into a structure specifically includes: The size of consecutive blind cells is stored in the BP_size matrix; The BP_position matrix is ​​used to store the centroid coordinates of continuous blind elements; The BP_bound matrix is ​​used to store the envelope size data of continuous blind elements.

2. The continuous blind pixel identification, labeling, and statistical method according to claim 1, characterized in that, The determination of continuous blind elements in the original blind data matrix specifically includes: Continuous blind elements in the original blind data matrix are determined using either an 8-neighborhood judgment method or a 4-neighborhood judgment method.

3. The continuous blind pixel identification, labeling, and statistical method according to claim 1, characterized in that, The step of labeling the centroid coordinates and envelope size data of continuous blind cells based on the continuous blind cell distribution map further includes: Remove consecutive blind cell size items with a quantity of 0.

4. A continuous blind pixel identification, annotation, and statistical system, used to implement the continuous blind pixel identification, annotation, and statistical method as described in any one of claims 1 to 3, characterized in that, include: The raw blind data matrix acquisition module is used to acquire the raw blind data matrix using an area array infrared focal plane detector; And identify consecutive blind elements in the original blind data matrix; The new blind data matrix determination module is used to delete consecutive blind elements in the original blind data matrix whose size is smaller than the smallest consecutive blind element size p, and obtain a new blind data matrix. The storage module is used to save the information of consecutive blind elements in the new blind data matrix into a structure; The information of the continuous blind element includes: the size of the continuous blind element, the centroid position coordinates, and the envelope size data; The continuous blind cell statistics matrix module is used to create a continuous blind cell statistics matrix based on the information of the largest continuous blind cell in the structure; and to use the continuous blind cell statistics matrix to perform statistics on the information of a single continuous blind cell. The continuous blind cell distribution map determination module is used to determine the continuous blind cell distribution map based on the new blind cell data matrix; The continuous blind element distribution map annotation module is used to annotate the centroid coordinates and envelope size data of continuous blind elements based on the continuous blind element distribution map.

5. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements a continuous blind pixel identification, labeling, and statistical method as described in any one of claims 1 to 3.

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