Linear point detection method based on dual space transformation

Abstract

The invention discloses a straight line point detection method based on dual spatial transformation, which is used for extracting a point set of approximate linear motion from continuous multi-frame spatial target images, and belongs to the technical field of image processing. The method comprises the following steps: firstly, extracting a space target point set, and converting the space target point set into a line segment set in dual space; and calculating intersection points among the line segments, and constructing an intersection point event queue. The outliers are filtered by counting the neighborhood density of the intersection points and setting a threshold value. Then, constructing a grid model, counting the number of intersection points in each grid, and screening out a grid region with the highest intersection point density; related line segments are extracted based on the region and mapped back to the original space, a candidate straight line point set is obtained, repeated points are removed, and finally a straight line point set is obtained. The invention aims to provide a straight line point detection method based on dual space transformation, which can screen out points in approximate linear motion by using the spatial position and direction characteristics of a spatial target point, and provides reliable technical support for sequence star map recognition.

Description

Technical Field

[0001] This invention belongs to the field of image processing technology, specifically relating to a method for detecting straight line points based on dual space transformation. Background Technology

[0002] With the rapid development of space monitoring technology, the accuracy and timeliness of star map data captured by space-based and ground-based sensors have continuously improved, significantly enhancing space situational awareness capabilities. This is crucial for maintaining space security and ensuring the operation of spacecraft. Space targets (such as satellites and space debris) are major carriers and potential threats to human space activities; identifying their precise locations and mapping their trajectories using star map sequences is of significant strategic and scientific importance. Effective trajectory extraction is fundamental for orbit determination, collision warning, and space behavior analysis.

[0003] Space targets in high-frame-rate star map sequences exhibit trajectory features consisting of point targets and continuous displacements. Existing detection methods typically focus on utilizing the brightness characteristics of target points and the spatial continuity between frames, constructing motion models for identification through point detection combined with trajectory association or predictive filtering algorithms, achieving some success. However, these methods do not fully utilize the trajectory clues formed by the target points and their continuous observations (such as the potential straightness implied by the consistency of motion direction and velocity in short time intervals). In complex backgrounds or with a large number of decoy interferences, this can easily lead to trajectory breaks, association errors, or increased false alarm rates. Therefore, how to robustly and accurately extract space target points in complex star map backgrounds and effectively model and associate their potential straight-line motion characteristics has become a key breakthrough direction for straight-line point detection methods based on dual space transformation. This method is expected to capture the motion trajectory more directly by exploring the intrinsic connections of point targets in multi-dimensional space, significantly improving detection performance in dense star fields or strong interference environments. Summary of the Invention

[0004] The purpose of this invention is to provide a method for detecting straight-line points based on dual space transformation, which can use the spatial position and directional characteristics of spatial target points to filter out points that move in approximately a straight line, providing reliable technical support for sequence star map recognition.

[0005] The technical solution adopted in this invention is as follows:

[0006] A method for detecting line points based on dual space transformation includes the following steps:

[0007] Step 1: Acquire spatial target images from multiple consecutive frames and extract the spatial target point set P;

[0008] Step 2: Transform all points in the spatial target point set P into the line segment set L in the dual space;

[0009] Step 3: Find any two line segments based on the line segment set L in the dual space. and intersection , , This represents the number of points in the spatial target point set P;

[0010] Step 4: Combine all intersection points with the corresponding line segment numbers in the line segment set L to obtain the intersection event queue E;

[0011] Step 5: Count any intersection points The point distribution density within a distance r is added to the intersection event queue E to obtain a new intersection event queue. ;

[0012] Step 6, for the new intersection event queue By setting a density threshold Filter outlier intersection events to obtain a queue of outlier intersection events. ;

[0013] Step 7, based on the intersection event queue Construct a mesh G and determine the origin of the mesh. and grid scale For the intersection event queue Construct the corresponding grid number at any intersection point. ;

[0014] Step 8, based on grid G, in the intersection event queue Add grid numbers to the grid and build a new intersection event queue. ;

[0015] Step 9: Count the number of intersection events within each grid in grid G, and sort them to obtain the grid number with the most intersection events. ;

[0016] Step 10, based on grid number Filter out the candidate set of intersection events ;

[0017] Step 11, based on the candidate set of intersection events Obtain all line segments involved in the intersection event to obtain a candidate line segment set. ;

[0018] Step 12, set the intersection candidate line segments Mapping to the target space point set yields the filtered candidate line point set. ;

[0019] Step 13, for the candidate line point set Repeated point removal yields the detected set of line points. .

[0020] Furthermore, in step 3, any two line segments in the dual space and intersection Represented as:

[0021]

[0022] in, and They represent the intersection points respectively. Coordinate information, and Let i and j be the i-th and j-th lines in the set of line segments L in the dual space, respectively, with slopes of respectively. and The intercepts are respectively and .

[0023] Furthermore, the intersection event queue E in step 4 is represented as:

[0024] .

[0025] 4. A method for detecting straight line points based on dual space transformation according to claim 3, characterized in that, in step 5, a new intersection event queue... It can be represented as:

[0026]

[0027] in, Represents the intersection event queue The k-th intersection event, The intersection density is calculated as follows:

[0028]

[0029] In the formula, Indicates the intersection point The number of points within a distance r around it.

[0030] Furthermore, the grid scale in step 7 It is a hyperparameter, the grid origin. The calculation method is as follows:

[0031]

[0032] in, Intersection event queue The minimum x-coordinate of all intersection points in the equation. Intersection event queue The minimum ordinate of all intersection points in the equation;

[0033] For the intersection event queue any intersection point Construct the corresponding grid number for:

[0034]

[0035] Where int represents rounding down. and These represent the line segment numbers that satisfy the density condition. and They represent and The coordinates of the intersection point of the line segments. This represents the intersection density.

[0036] Furthermore, in step 9, the set of intersection events for any grid number w is first counted. for:

[0037]

[0038] in, This represents the event numbers of all possible intersection points within grid number w. Represents the intersection event queue Middle element;

[0039] Then, count the set of intersection events in each grid. The number of elements is denoted as . The grid number with the most intersection events is... Represented as:

[0040]

[0041] in, This represents the total number of all grids in grid G.

[0042] Furthermore, in step 10, the candidate set of intersection events... Represented as:

[0043]

[0044] in, A non-consecutive positive integer represents the intersection event queue. The selected intersection event indices, where r is a consecutive positive integer, represent the candidate set of intersection events. Element count index Represents the candidate set of intersection events Total number of elements and Represent the candidate sets of intersection events respectively. The line segment number corresponding to any intersection event in the equation. and They represent and The coordinates of the intersection point of the line segments. express and The density of intersections of line segments, Represents the intersection event queue The number of all intersections in the equation.

[0045] The present invention has the following beneficial effects:

[0046] (1) This invention proposes a line point detection method based on dual space transformation, which does not rely on the trajectory feature extraction of points and can effectively extract the position of star points in continuous star maps.

[0047] (2) The method of the present invention is applied to perform dual space transformation based on the positional relationship of star points in multiple frames of starry sky images, providing effective technical support for the rapid extraction of star points from sequence star maps. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the principle of the line point detection method based on dual space transformation of the present invention. Detailed Implementation

[0049] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0050] A method for detecting line points based on dual space transformation includes the following steps:

[0051] Step 1: Acquire spatial target images from multiple consecutive frames and extract spatial target point sets. , means as follows:

[0052]

[0053] In the formula, Represents the set of spatial target points The number of midpoints For the set of spatial target points The Middle One point, They represent The coordinates of the point.

[0054] Step 2, set the spatial target points All points are transformed into a set of line segments in the dual space. , means as follows:

[0055]

[0056] in, Represents the set of line segments in the dual space The first in A straight line, corresponding to the point The transformation to the dual space has a slope of The intercept is Therefore, for any straight line in the dual space The expression is:

[0057]

[0058] in, and These represent the two-dimensional spatial dimensions of the line.

[0059] Step 3, based on the set of line segments in the dual space Find any two line segments and intersection It can be expressed as:

[0060]

[0061] in, and They represent the intersection points respectively. Coordinate information.

[0062] Step 4: Combine all intersection points and line segment sets. The intersection event queue E is obtained by taking the corresponding line segment number, which can be expressed as:

[0063] .

[0064] Step 5: Count any intersection points The distribution density of points within a distance r is used to obtain a new queue of intersection events. It can be represented as:

[0065]

[0066] in, Represents the intersection event queue The k-th intersection event, The intersection density is calculated as follows:

[0067]

[0068] In the formula, Indicates the intersection point The number of points within a distance r around it.

[0069] Step 6, for the new intersection event queue By setting a density threshold Filter outlier intersection events to obtain a queue of outlier intersection events. It can be represented as:

[0070]

[0071] in, and These represent the line segment numbers that satisfy the density condition. and They represent and The coordinates of the intersection point of the line segments. express and The density of intersections of line segments, Represents the intersection event queue The number of all intersections in the equation.

[0072] Step 7, based on the existing intersection event queue Construct a mesh G and determine the origin of the mesh. and grid scale Grid Scale It is a hyperparameter, the grid origin. The calculation method is as follows:

[0073]

[0074] in, Intersection event queue The minimum x-coordinate of all intersection points in the equation. Intersection event queue The minimum ordinate of all intersection points in the equation;

[0075] For the intersection event queue any intersection point Construct the corresponding grid number for:

[0076]

[0077] Where int represents rounding down. and These represent the line segment numbers that satisfy the density condition. and They represent and The coordinates of the intersection point of the line segments. express and The density of intersections of line segments.

[0078] Step 8, based on grid G ​​and intersection event queue Construct a new intersection event queue It can be represented as:

[0079]

[0080] in, Represents the intersection event queue The Middle The grid number to which each intersection point belongs is calculated using the following expression:

[0081]

[0082] Where int represents rounding down. and These represent the new intersection event queues. The segment numbers of any two line segments in the equation. and They represent and The coordinates of the intersection point of the line segments. express and The density of intersections of line segments.

[0083] Step 9: First, count the set of intersection events for any grid number w in grid G. for:

[0084]

[0085] in, This represents the event numbers of all possible intersection points within grid number w. Represents the intersection event queue Middle element;

[0086] Then, count the set of intersection events in each grid. The number of elements is denoted as . The grid number with the most intersection events is... It can be represented as:

[0087]

[0088] in, This represents the total number of all grids in grid G.

[0089] Step 10, based on grid number Filter out the candidate set of intersection events , can be represented as:

[0090]

[0091] in, A non-consecutive positive integer represents the intersection event queue. The selected intersection event indices, where r is a consecutive positive integer, represent the candidate set of intersection events. Element count index To represent the candidate set of intersection events Total number of elements and Represent the candidate sets of intersection events respectively. The line segment number corresponding to any intersection event in the equation. and They represent and The coordinates of the intersection point of the line segments. express and The density of intersections of line segments.

[0092] Step 11, based on the candidate set of intersection events Obtain all line segments involved in the intersection event to obtain a candidate line segment set. , can be represented as:

[0093]

[0094] in, Represent the candidate sets of intersection events respectively. Intersection point Two straight lines.

[0095] Step 12, set the intersection candidate line segments Mapping to the target space point set yields the filtered candidate line point set. , can be represented as:

[0096]

[0097] in, Represent the candidate sets of intersection events respectively. Intersection point The original spatial points corresponding to the two straight lines.

[0098] Step 13, for the candidate line point set By removing duplicate points, the set of line points detected by the method can be obtained. , can be represented as:

[0099]

[0100] in, Represents a unique integer.

[0101] In summary, this invention proposes a line point detection method based on dual space transformation. It performs dual space transformation based on the positional relationship of star points in multiple frames of starscape images, and does not rely on point-responsible trajectory feature extraction. It can filter out the coordinates of moving star points from a large set of points, providing effective technical support for the rapid extraction of star points from sequence star maps.

Claims

1. A line point detection method based on dual space transformation, characterized in that, The method comprises the following steps: Step 1, acquiring a plurality of continuous frames of spatial target images and extracting a spatial target point set P; Step 2, converting all points in the spatial target point set P into a line segment set L in a dual space; Step 3, find the intersection of any two lines of the set L in the dual space and denotes the number of points in the spatial set of target points P;​​​ Step 4, combining all intersection points and corresponding line segment numbers in the line segment set L to obtain an intersection event queue E; Step 5, count arbitrary intersection points The point distribution density within the distance r range, the point distribution density is added to the intersection event queue E, and a new intersection event queue is obtained ; Step 6, filtering outlier intersection events by preset density threshold , obtaining a filtered intersection event queue of outlier intersection events ;​ Step 7, build grid G for each intersection event in the queue Build grid G, determine origin of grid and grid scale For each intersection event in the queue build corresponding grid number ; Step 8, based on the grid G, the grid number is added to the intersection event queue, and a new intersection event queue is constructed ;​ Step 9, count the number of intersection events in each grid in the grid G and sort to get the grid number with the most intersections ; Step 10, depending on the grid number Screening out intersection event candidate set ; Step 11, according to the intersection event candidate set Get all line segments involved in the intersection event, get the candidate line segment set ; Step 12, mapping the intersection candidate line segment set to the target space point set to obtain a filtered candidate straight line point set mapping to the target space point set to obtain a filtered candidate straight line point set ; Step 13, remove repeated points from the candidate straight line point set Step 14, remove points that are not on the straight line .

2. The line point detection method based on dual space transformation according to claim 1, wherein, The intersection of any two line segments in the dual space in step 3 and is represented as: ​ wherein, and respectively represent coordinate information of intersection points , and respectively represent the i-th and j-th lines in the line segment set L in the dual space, whose slopes are and respectively, and whose intercepts are and .

3. The line point detection method based on dual space transformation according to claim 2, characterized in that, The intersection event queue E in step 4 is expressed as: 。 4. The line point detection method based on dual space transformation according to claim 3, characterized in that, The new intersection event queue in step 5 may be represented as: wherein, represents the intersection event queue the kth intersection event in the queue, represents the intersection density, which is calculated as follows: wherein represents the intersection point the number of points within a distance r around it.

5. The line point detection method based on dual space transformation according to claim 4, characterized in that, Grid scale in step 7 is a hyperparameter, the grid origin is calculated as follows: wherein is the intersection event queue is the minimum x-coordinate of all intersections in is the intersection event queue is the minimum y-coordinate of all intersections in For intersection event queue Any of the intersections , the corresponding grid number is constructed as: where int denotes the floor function, and denote the line segment numbers satisfying the density condition, respectively, and denote the and coordinates of the intersection point of the line segments, respectively, denote the intersection density.

6. The line point detection method based on dual space transformation according to claim 5, wherein, In step 9, the intersection event set for any grid number w is first counted is: wherein, represents all possible intersection event numbers within grid number w, represents the intersection event queue in element; Then, count the set of intersection events in each grid. The number of elements is denoted as . The grid number with the most intersection events is... Represented as: wherein represents the total number of all grids in the grid G.

7. The line point detection method based on dual space transformation according to claim 6, wherein, Step 10 intersection event candidate set is represented as: wherein, is a non-continuous positive integer, representing the intersection event queue is a continuous positive integer, representing the intersection event candidate set is the element number sequence, represents the intersection event candidate set is the total number of elements, and respectively represent the intersection event candidate set corresponding to any intersection event pair in the intersection event candidate set and respectively represent and the intersection coordinates of the line segments, represent and the intersection density of the line segments, represents the number of all intersections in the intersection event queue .

Images