CN115717887B - Star point rapid extraction method based on gray distribution histogram - Google Patents
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Abstract
A star point rapid extraction method based on a gray distribution histogram belongs to the field of star map recognition. The invention aims at solving the problems that the star point extraction efficiency is low and the dynamic performance is limited to be improved under the complex working condition of the existing star sensor. Comprising the following steps: dividing an original star map into a plurality of sub-maps with equal sizes according to an array form; counting gray average values and gray distribution histograms for each sub-graph; obtaining a background threshold value of the subgraph by gray average value calculation; calculating the inter-class variance between the background and the foreground of the subgraph, and marking whether each subgraph contains star points according to the inter-class variance; searching four connected domain directions of the subgraphs marked as containing star points, and extracting star point pixels; for each sub-graph marked as containing star points, if the number of the star point pixels extracted in the set area range is greater than a preset threshold value, determining a star point by the extracted star point pixels; the centroid of the star point is calculated for each star point from the extracted star point pixels. The method is used for extracting the star points of the star map.
Description
Technical Field
The invention relates to a star point rapid extraction method based on a gray distribution histogram, belonging to the field of star map recognition.
Background
The star sensor is a high-precision and high-reliability gesture sensitive instrument widely applied to space detection and astronomical navigation at present.
The star sensor is generally divided into two working modes of a full celestial sphere mode and a star tracking mode, and works in the full celestial sphere mode after the star sensor is started or fails to track the star, so that star point extraction, recognition and gesture capture are carried out on the full star map. Because no prior information exists at this time, a wavelet gate is established according to the calculated gesture, and if star points can be successfully extracted in the wavelet gate, a star tracking mode is shifted to perform local star point extraction and identification. When the star sensor works in a dynamic environment, if the whole image is used for extracting the mass center and identifying the mass center for a long time, the star point moves to a larger position on the image plane; if the moving distance is larger than the size of the wave gate, the star tracking mode cannot be entered. The star sensor is in the full celestial sphere mode for a long time, continuous gesture capture cannot be achieved, normal operation of the star sensor is affected, and therefore time consumption of a full graph extraction algorithm is used for limiting dynamic performance of the star sensor.
For the star point extraction method under the complex working condition, the present scholars mainly consider the success rate and the robustness of the algorithm under the conditions of parasitic light and dynamic tailing. Some scholars have conducted intensive research on star map extraction algorithm under the disturbance of stray light, and the stray light resistance of the star sensor is improved to a certain extent through modeling a stray light model, carrying out background estimation by using a mask, background filtering and other methods. Still other scholars research star map extraction algorithms under high dynamic working conditions, and the focus of the research is mainly how to realize star point restoration and improve centroid positioning accuracy through restoration methods such as Richardson-Lucy (RL) algorithm, super Laplacian algorithm and the like. However, the filtering algorithm and the iterative recovery algorithm have the problem of large calculation amount, the time consumption and the real-time performance of the algorithm and the success rate of the full celestial sphere rotation tracking are less concerned, the accuracy and the calculation rapidity of star point extraction cannot be considered, and the requirements on the current engineering implementation are high. In addition, star point extraction under the conditions of stray light and high dynamic is essentially the problem of extracting star images with low signal to noise ratio, and few methods at present can be simultaneously applied to two conditions.
Disclosure of Invention
Aiming at the problems of low star point extraction efficiency and limitation of dynamic performance improvement of the existing star sensor under the complex working condition, the invention provides a star point rapid extraction method based on a gray distribution histogram.
The invention relates to a star point rapid extraction method based on gray distribution histogram, which comprises the following steps,
Step one: dividing an original star map into a plurality of sub-maps with equal sizes according to an array form;
Step two: counting gray average values and gray distribution histograms for each sub-graph; obtaining a background threshold value of the subgraph by gray average value calculation;
Step three: calculating the inter-class variance between the background and the foreground of the subgraph according to the background threshold value and the gray level distribution histogram, and marking whether each subgraph contains star points according to the inter-class variance;
Step four: searching four connected domain directions of the subgraphs marked as containing star points, and extracting star point pixels; for each sub-graph marked as containing star points, if the number of the star point pixels extracted in the set area range is greater than a preset threshold value, determining a star point by the extracted star point pixels;
step five: the centroid of the star point is calculated for each star point from the extracted star point pixels.
According to the star point rapid extraction method based on the gray distribution histogram, the calculation method of the background threshold value of the subgraph in the second step comprises an iterative threshold value method, an adaptive threshold value method and a local threshold value method.
According to the star point rapid extraction method based on the gray distribution histogram, the calculation method of the background threshold value of the subgraph in the step two is an adaptive threshold value method:
thre＝E+α·δ，
Wherein thre is a background threshold, E is a gray average value, alpha is a weighting coefficient, and delta is a standard deviation of gray values of the subgraph.
According to the star point rapid extraction method based on the gray distribution histogram, the weighting coefficient alpha takes the value according to the actual noise of the original star map, and the value range is 3-5.
According to the star point rapid extraction method based on the gray distribution histogram, the gray distribution histogram is a histogram with span 0-255 or span 0-127 distribution.
According to the star point rapid extraction method based on the gray distribution histogram, in the third step, the calculation method of the inter-class variance is as follows:
where σ 2 is the inter-class variance, m G is the global mean of the subgraph, m is the average gray value of the subgraph background, and p 1 characterizes the probability that a pixel is divided into the background:
i is the gray level of the sub-image background pixel, k is the maximum segmentation threshold value of the sub-image background pixel, p i is the probability that the pixel with the gray level i is segmented into the background, n is the total number of pixels, and n i is the pixel number with the gray level i;
According to the quick star point extraction method based on the gray distribution histogram, in the third step, the method for marking whether each sub-graph contains star points comprises the following steps:
Wherein flag is a label, wherein a label value of 1 represents inclusion of a star point, a label value of 0 represents non-inclusion of a star point, and N is a segmentation variance determined by a background noise level of an original star map.
According to the quick star point extraction method based on the gray distribution histogram, the star point determination method in the fourth step comprises the following steps:
Searching for a growth seed point by traversing the subgraph with a preset step length for each subgraph marked as containing star points, and determining a growth seed point for the search points with pixels larger than the subgraph background threshold;
For each growth seed point, carrying out pixel-by-pixel region growth on the four connected domain directions of the growth seed points to obtain pixel points to be selected; determining a sub-graph where each pixel point to be selected is located, and taking the pixel point to be selected as a star point pixel if the gray value of the pixel point to be selected is larger than the background threshold value of the sub-graph where the gray value of the pixel point to be selected is located; for each growth seed point, if the number of star point pixels determined in the set area range is greater than a pixel number threshold value, a star point is determined.
According to the star point rapid extraction method based on the gray distribution histogram, the star point centroid calculation method in the fifth step comprises a centroid method, a square weighted centroid method, a centroid method with a threshold value and a polynomial fitting method.
According to the star point rapid extraction method based on the gray distribution histogram, the method for calculating the star point centroid by adopting the centroid method with the threshold value comprises the following steps:
Wherein x 0 is the abscissa of the centroid of the star point, and y 0 is the ordinate of the centroid of the star point; f (x, y) is the gray value at the coordinates (x, y), T is the background threshold of the corresponding sub-graph, P is the number of pixels in the x direction of the sub-graph, and Q is the number of pixels in the y direction of the sub-graph.
The invention has the beneficial effects that: the method improves the star point extraction efficiency: the potential effective information subgraphs in the star map are marked by extracting the gray level distribution histogram features of the star map, so that the scanning of unnecessary areas is greatly reduced, and the resource consumption of a large amount of useless information during star map processing can be effectively reduced; in addition, star points are extracted through a multi-region threshold searching method, so that accuracy and robustness of complex working condition star point extraction are further improved.
The method can improve the dynamic performance of the star sensor all-weather tracking: the star point extraction method can realize the rapid star point extraction by reducing the resource consumption of a large amount of useless information during star map processing, so that the star point extraction method can be successfully converted from a full-sky mode to a star tracking mode under the working condition of a large angular speed, and the dynamic performance of full-sky rotation tracking is improved.
Drawings
FIG. 1 is a flow chart of a star point rapid extraction method based on a gray distribution histogram according to the invention;
FIG. 2 is an original star map in accordance with a first embodiment;
FIG. 3 is a sub-graph star point marking scenario in a first embodiment;
FIG. 4 is a diagram showing star point extraction results according to the first embodiment; pixels are shown as pixels;
fig. 5 is an original star map in a second embodiment.
Fig. 6 is a diagram showing star point extraction results in the second embodiment.
Fig. 7 is an original star map in a third embodiment.
Fig. 8 is a diagram of star point extraction results in a third embodiment.
Fig. 9 is an original star map in a fourth embodiment.
Fig. 10 is a diagram of star point extraction results in the fourth embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting.
In a first embodiment, referring to fig. 1, the present invention provides a fast star point extraction method based on a gray distribution histogram, including,
Step one: dividing an original star map into a plurality of sub-maps with equal sizes according to an array form;
Step two: counting gray average values and gray distribution histograms for each sub-graph; obtaining a background threshold value of the subgraph by gray average value calculation;
Step three: calculating the inter-class variance between the background and the foreground of the subgraph according to the background threshold value and the gray level distribution histogram, and marking whether each subgraph contains star points according to the inter-class variance;
Step four: searching four connected domain directions of the subgraphs marked as containing star points, and extracting star point pixels; for each sub-graph marked as containing star points, if the number of the star point pixels extracted in the set area range is greater than a preset threshold value, determining a star point by the extracted star point pixels;
Step five: and calculating the centroid positioning of the star point according to the extracted star point pixels for each star point.
In this embodiment, the whole original star map is first divided into a plurality of sub-maps with equal size, and then the gray average value of each sub-map is counted as the reference for dividing star points and background, and the gray distribution histogram of the sub-map is counted in parallel. And carrying out state marking on the sub-graph by adopting a maximum inter-class variance method according to the calculated background threshold value, and improving the star point extraction speed by only carrying out centroid extraction on the sub-graph marked as containing star points.
Further, the calculation method of the background threshold value of the subgraph in the second step comprises an iterative threshold value method, an adaptive threshold value method and a local threshold value method.
The calculation method of the background threshold value of the subgraph in the second step is an adaptive threshold value method:
thre＝E+α·δ，
Wherein thre is a background threshold, E is a gray average value, alpha is a weighting coefficient, and delta is a standard deviation of gray values of the subgraph.
The weighting coefficient alpha takes the value according to the actual noise of the original star map, and the value range is 3-5.
As an example, the gradation distribution histogram is a histogram of span 0-255 or span 0-127 distribution. Other corresponding improved distribution histograms may also be selected as appropriate. The maximum span of the histogram may be determined by 2 K -1, and the value of K is determined by how many bits of data each pixel represents in the computer.
The purpose of the statistical gray distribution histogram is to count the number of pixels corresponding to different gray ranges.
In the third step, the method for calculating the inter-class variance comprises the following steps:
where σ 2 is the inter-class variance, m G is the global mean of the subgraph, m is the average gray value of the subgraph background, and p 1 characterizes the probability that a pixel is divided into the background:
i is the gray level of the sub-image background pixel, k is the maximum segmentation threshold value of the sub-image background pixel, p i is the probability that the pixel with the gray level i is segmented into the background, n is the total number of pixels, and n i is the pixel number with the gray level i;
still further, in the third step, the method for marking whether each sub-graph includes star points is as follows:
Wherein flag is a label, wherein a label value of 1 represents inclusion of a star point, a label value of 0 represents non-inclusion of a star point, and N is a segmentation variance determined by a background noise level of an original star map.
Still further, the method for determining the star point in the fourth step includes:
Searching for a growth seed point by traversing the subgraph with a preset step length for each subgraph marked as containing star points, and determining a growth seed point for the search points with pixels larger than the subgraph background threshold;
for each growth seed point, carrying out pixel-by-pixel region growth on the four connected domain directions of the growth seed points to obtain pixel points to be selected; determining a sub-graph where each pixel point to be selected is located, and taking the pixel point to be selected as a star point pixel if the gray value of the pixel point to be selected is larger than the background threshold value of the sub-graph where the gray value of the pixel point to be selected is located; for each growth seed point, if the number of star point pixels determined in the set area range is greater than a pixel number threshold value, a star point is determined. Otherwise, the star point extraction is considered to be failed, and the seed points are scanned again to find the growth seed points until the sub-graph traversal is finished.
As an example, the method of calculating the centroid of the star point in the fifth step includes a centroid method, a square weighted centroid method, a centroid method with a threshold value, and a polynomial fitting method.
In this embodiment, in order to further reduce the algorithm time consumption, a seed point is searched in the star point mark subgraph by using a region growing method with step length, pixel growth is performed according to the corresponding subgraph background threshold value, and finally, a centroid method is used to calculate the centroid position.
The method of the invention improves the star point extraction efficiency while ensuring the star point extraction accuracy, thereby improving the dynamic performance of the product all-day tracking. The robustness and the high efficiency of the invention under various working conditions are verified, and the invention has higher engineering application value.
The method for calculating the centroid of the star point by adopting the centroid method with the threshold value comprises the following steps:
Wherein x 0 is the abscissa of the centroid of the star point, and y 0 is the ordinate of the centroid of the star point; f (x, y) is the gray value at the coordinates (x, y), T is the background threshold of the corresponding sub-graph, P is the number of pixels in the x direction of the sub-graph, and Q is the number of pixels in the y direction of the sub-graph.
The process according to the invention is illustrated in more detail by the following examples:
First embodiment:
In connection with the illustration of fig. 1, the processing is performed with respect to the original star map illustrated in fig. 2:
s1, dividing an original star map into a plurality of sub-images with equal sizes; specifically, the total is divided into 20×16 sub-graphs, and 320 sub-graphs are taken as a total;
S2, calculating a background threshold value of each sub-graph in S1 by counting gray level mean values, and counting gray level distribution histograms of the sub-graphs at the same time;
s3, calculating an inter-class variance between the subgraph background and the foreground according to the background threshold value and the gray distribution histogram which are calculated in the S2;
S4, marking whether the subgraph contains star points or not according to the inter-class variance calculated in the S3;
S5, extracting star point pixels from the subgraph marked as containing star points in the S4, and determining the star points;
S6, conducting mass center subdivision positioning on the star points determined in the S5.
Further, calculating a background threshold value in the step S2 by adopting an adaptive threshold value method;
In S4, the labeling result for fig. 2 is shown in fig. 3, where a white block indicates that the sub-image does not contain a star point, and a black block indicates that the sub-image contains a star point, and 86 blocks total.
S5, selecting a preset step length=4 to traverse the subgraph to search for a growth seed point; the pixel number threshold may be selected to be 4, i.e., if the number of star pixels determined in the set area is greater than 4, a star is determined; and in the set area range, if the pixels in the four directions are not larger than the background threshold value, stopping growing. The set area range may be a square area formed by setting one maximum pixel number, for example, the maximum pixel number may be set to 50. If the number of star pixels determined without completing the set area traversal is already greater than 4, the growth is ended in advance.
Fig. 4 shows the star point extraction result of fig. 2, and 17 star points are extracted altogether.
Specific embodiment II:
the method of the invention is used for extracting star points, the original star map is the star map affected by the stray light interference, as shown in fig. 5, and the star point extraction result is shown in fig. 6.
Third embodiment:
The method of the invention is used for extracting star points, the original star map is a star map shot at the angular velocity of 1 degree/s, as shown in figure 7, and the star point extraction result is shown in figure 8.
Fourth embodiment:
The method of the invention is used for extracting star points, the original star map is a star map shot at the angular velocity of 2 degrees/s, as shown in fig. 9, and the star point extraction result is shown in fig. 10.
The time consumption, extraction accuracy and outfield dynamic test results of the star point extraction method in the four embodiments are summarized in table 1.
TABLE 1 time consuming and accuracy summary of star point extraction
In the four embodiments, the total number of the split subgraphs is 320 blocks. Compared with the method for extracting star points without adopting the method, the average time consumption without adopting the method is about 2s, and the dynamic performance of the actual measurement all-day tracking of the external field is 0.8 degrees/s. As can be seen from Table 1, the method of the invention marks only 1/4 to 1/3 of the total number of sub-blocks based on the fast star point extraction method of the histogram characteristic of the local star map, thereby greatly reducing the traversal of the star point-free subgraph; and the scanning of unnecessary pixel points is further reduced due to the adoption of a growth seed point searching algorithm with step length. Therefore, the method can improve the extraction efficiency by at least 60 percent, and greatly improve the overall extraction efficiency. From the viewpoint of star point extraction accuracy, the star point extraction method does not reduce the star point extraction rate, and has good robustness. The dynamic performance of the method is improved to 2 degrees/s, which is improved by 2.5 times compared with the prior method.
In summary, the method of the invention firstly divides the image into a plurality of sub-images, marks whether the sub-images contain star points by combining the background threshold value of the sub-images and the gray histogram distribution characteristics thereof, and only extracts the centroid of the sub-images containing the star points to reduce the time consumption of extracting the centroid of the whole image. The method further reduces time consumption and ensures extraction accuracy by adopting a local threshold region growth algorithm with step length. The star point extraction method is suitable for normal working conditions, stray light working conditions and dynamic working conditions, and can improve algorithm efficiency and dynamic performance of all-day tracking while ensuring star point extraction rate.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that the different dependent claims and the features described herein may be combined in ways other than as described in the original claims. It is also to be understood that features described in connection with separate embodiments may be used in other described embodiments.
Claims (8)
1. A star point rapid extraction method based on gray distribution histogram is characterized by comprising the following steps,
Step one: dividing an original star map into a plurality of sub-maps with equal sizes according to an array form;
Step two: counting gray average values and gray distribution histograms for each sub-graph; obtaining a background threshold value of the subgraph by gray average value calculation;
Step three: calculating the inter-class variance between the background and the foreground of the subgraph according to the background threshold value and the gray level distribution histogram, and marking whether each subgraph contains star points according to the inter-class variance;
Step four: searching four connected domain directions of the subgraphs marked as containing star points, and extracting star point pixels; for each sub-graph marked as containing star points, if the number of the star point pixels extracted in the set area range is greater than a preset threshold value, determining a star point by the extracted star point pixels;
step five: calculating the mass center of each star point according to the extracted star point pixels;
In the third step, the method for calculating the inter-class variance comprises the following steps:
where σ 2 is the inter-class variance, m G is the global mean of the subgraph, m is the average gray value of the subgraph background, and p 1 characterizes the probability that a pixel is divided into the background:
i is the gray level of the sub-image background pixel, k is the maximum segmentation threshold value of the sub-image background pixel, p i is the probability that the pixel with the gray level i is segmented into the background, n is the total number of pixels, and n i is the pixel number with the gray level i;
In the third step, the method for marking whether each sub-graph contains star points comprises the following steps:
Wherein flag is a label, wherein a label value of 1 represents inclusion of a star point, a label value of 0 represents non-inclusion of a star point, and N is a segmentation variance determined by a background noise level of an original star map.
2. The fast extraction method of star points based on gray distribution histogram according to claim 1, characterized in that,
The calculation method of the background threshold value of the subgraph in the second step comprises an iterative threshold value method, an adaptive threshold value method and a local threshold value method.
3. The fast extraction method of star points based on gray distribution histogram according to claim 1, characterized in that,
The calculation method of the background threshold value of the subgraph in the second step is an adaptive threshold value method:
thre＝E+α·δ，
Wherein thre is a background threshold, E is a gray average value, alpha is a weighting coefficient, and delta is a standard deviation of gray values of the subgraph.
4. The method for rapidly extracting star points based on gray distribution histogram according to claim 3,
The weighting coefficient alpha takes the value according to the actual noise of the original star map, and the value range is 3-5.
5. The method for rapidly extracting star points based on gray distribution histogram according to claim 4,
The gray distribution histogram is a histogram of span 0-255 or span 0-127 distribution.
6. The method for rapidly extracting star points based on gray distribution histogram according to claim 5, wherein,
The method for determining the star point in the fourth step comprises the following steps: searching for a growth seed point by traversing the subgraph with a preset step length for each subgraph marked as containing star points, and determining a growth seed point for the search points with pixels larger than the subgraph background threshold;
For each growth seed point, carrying out pixel-by-pixel region growth on the four connected domain directions of the growth seed points to obtain pixel points to be selected; determining a sub-graph where each pixel point to be selected is located, and taking the pixel point to be selected as a star point pixel if the gray value of the pixel point to be selected is larger than the background threshold value of the sub-graph where the gray value of the pixel point to be selected is located; for each growth seed point, if the number of star point pixels determined in the set area range is greater than a pixel number threshold value, a star point is determined.
7. The method for rapidly extracting star points based on gray distribution histogram according to claim 6, wherein,
And fifthly, calculating the centroid of the star point by a square weighted centroid method, a centroid method with a threshold value or a polynomial fitting method.
8. The fast extraction method of star points based on gray distribution histogram according to claim 7, characterized in that,
The method for calculating the centroid of the star point by adopting the centroid method with the threshold value comprises the following steps:
Wherein x 0 is the abscissa of the centroid of the star point, and y 0 is the ordinate of the centroid of the star point; f (x, y) is the gray value at the coordinates (x, y), T is the background threshold of the corresponding sub-graph, P is the number of pixels in the x direction of the sub-graph, and Q is the number of pixels in the y direction of the sub-graph.
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