JP5297677B2 - Detection apparatus and method, program, recording medium, and simulation system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To accurately detect the rotation of any ball despite its type. <P>SOLUTION: A characteristic point extraction section 91 detects edges in a first image and a second image, and stores the plurality of detected edges as characteristic point groups of the first image and the second image respectively. A simple rotation section 92 specifies a coordinate position in a three-dimensional space of the characteristic point group of the first image, rotates the coordinate position of the characteristic point group by a predetermined Eulerian angle and specifies the Eulerian angle where a distance between proximate characteristic points is minimized. A characteristic point association control section 93 rotates the first image by the Eulerian angle of the minimum distance of the proximate characteristic points and associates the two characteristic points in the first image and the second image respectively based on correlative values computed between the first image and the second image. A rotation speed detection control section 94 specifies the rotation and the rotation direction of a golf ball per unit time based on the associated characteristic points and detects information related to the rotation speed and the like. <P>COPYRIGHT: (C)2010,JPO&amp;INPIT

Description

  The present invention relates to a detection apparatus and method, a program, a recording medium, and a simulation system, and in particular, a detection apparatus and method, program, and recording that can accurately detect rotation of any ball. The present invention relates to a medium and a simulation system.

  In recent years, demand for lessons such as golf and tennis is increasing in sports clubs and the like. In particular, golf practice required a large site, and for example, it was difficult to conduct golf lessons in the city.

  Therefore, for example, a golf ball shot with a golf club is captured with a camera, and the ballistic trajectory and flight distance of the golf ball are calculated using various data such as the velocity of the golf ball obtained from the captured image data. A simulation system that displays the trajectory of a golf ball on a screen has been developed.

  In addition, a simulation apparatus that calculates a trajectory, a flight distance, and the like considering aerodynamic characteristics has also been proposed (see, for example, Patent Document 1).

  Further, in recent years, a simulation system has been developed that detects the direction and number of rotation of a golf ball from the image of the golf ball shot with a golf club and calculates the trajectory of a hook or slice.

JP 2003-24493 A

  However, in order to detect the direction of rotation and the number of rotations of a golf ball by conventional techniques, in order to specify the rotation direction and the angle of rotation of the ball in an image, It was necessary to use the attached golf ball.

  Therefore, for example, there is a problem that the simulation system cannot be used by freely using a ball that the user likes.

  The present invention has been made in view of such a situation, and makes it possible to accurately detect the rotation of any ball.

  A first aspect of the present invention is a detection device that detects rotation of the golf ball based on an image obtained by imaging a moving golf ball, the first image obtained by imaging the golf ball at a first time, And a plurality of images constituting part of the image of the surface of the golf ball by detecting pixels that are edges in each of the second images obtained by capturing the golf ball at a second time after the first time. A feature point extracting means for extracting the feature points, a three-dimensional coordinate position of the plurality of feature points extracted in the first image, and a three-dimensional coordinate position of the plurality of feature points extracted in the second image The Euler angle specifying means for specifying the Euler angle representing the positional relationship between the first image and the second image based on the distance to each of the first image, and the first image corresponding to the specified Euler angle. A predetermined feature point of the extracted feature points in the rotated first image, and a predetermined feature point of the extracted feature points in the second image. Correlation calculating means for calculating a correlation value by an image correlation method, and correlation for associating a feature point of the first image with a feature point of the second image based on the calculated correlation value Means for detecting the amount and direction of rotation of the golf ball based on the associated feature points of the first image and the second image.

  A first strobe that emits light at the first time, a second strobe that emits light at the second time, and one camera that images the golf ball as a subject at the first time and the second time The image of the golf ball corresponding to the first image and the image of the golf ball corresponding to the first image can be captured by the one camera.

  The Euler angle specifying means rotates each of the three-dimensional coordinate positions of the extracted feature points in the first image by an Euler angle corresponding to the parameter based on a given parameter. Feature point rotation means for plotting on the second image, each of the three-dimensional coordinate positions of the plurality of feature points extracted in the rotated first image among the given parameters, A parameter specifying unit that specifies the parameter having the highest evaluation value calculated based on the distance from each of the three-dimensional coordinate positions of the extracted feature points in the second image; it can.

  According to a first aspect of the present invention, there is provided a detection method for detecting a rotation of the golf ball based on an image of a moving golf ball, wherein the golf ball is imaged at a first time. In each of one image and a second image obtained by imaging the golf ball at a second time after the first time, a part of the image on the surface of the golf ball is detected by detecting pixels that are edges. A plurality of constituent feature points are extracted, and three-dimensional coordinate positions of the extracted feature points in the first image and three-dimensional coordinate positions of the extracted feature points in the second image, respectively. The Euler angle representing the positional relationship between the first image and the second image is identified based on the distance to the first image, the first image is rotated by the identified Euler angle, and the rotated first 1 stroke A correlation value between a predetermined feature point of the plurality of extracted feature points and a predetermined feature point of the plurality of extracted feature points in the second image is calculated by an image correlation method. And a step of associating a feature point of the first image with a feature point of the second image based on the calculated correlation value, and the feature of the associated first image In this detection method, the amount and direction of rotation of the golf ball are detected based on a point and a feature point of the second image.

  One aspect of the present invention is a detection device that detects rotation of the golf ball based on an image obtained by imaging a moving golf ball, and the first image obtained by imaging the golf ball at a first time. , And a second image obtained by imaging the golf ball at a second time after the first time, by detecting a pixel serving as an edge, a part of the image of the surface of the golf ball is formed Feature point extracting means for extracting a plurality of feature points, three-dimensional coordinate positions of the plurality of feature points extracted in the first image, and three-dimensional coordinates of the plurality of feature points extracted in the second image An Euler angle specifying means for specifying an Euler angle representing a positional relationship between the first image and the second image based on a distance from each of the positions; and an amount corresponding to the specified Euler angle. One image is rotated, a predetermined feature point among the plurality of feature points extracted in the rotated first image, and a predetermined one of the plurality of feature points extracted in the second image. Correlation calculation means for calculating a correlation value with a feature point by an image correlation method, and correspondence between the feature point of the first image and the feature point of the second image based on the calculated correlation value A detecting device for detecting the amount of rotation and the direction of rotation of the golf ball based on the feature points of the first image and the feature points of the second image that are associated with each other. It is a program that makes it work.

  In the first aspect of the present invention, in each of a first image obtained by imaging the golf ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time. The plurality of feature points constituting a part of the image of the surface of the golf ball are extracted by detecting pixels as edges, and the three-dimensional coordinate positions of the extracted feature points in the first image And an Euler angle representing a positional relationship between the first image and the second image based on a distance from each of the three-dimensional coordinate positions of the plurality of feature points extracted in the second image, The first image is rotated by an amount corresponding to the specified Euler angle, a predetermined feature point among the plurality of extracted feature points in the rotated first image, and the extracted in the second image. The A correlation value with a predetermined feature point among the number of feature points is calculated by an image correlation method, and based on the calculated correlation value, the feature point of the first image and the feature of the second image Association with points is performed, and the amount of rotation and the direction of rotation of the golf ball are detected based on the feature points of the first image and the feature points of the second image that are associated with each other.

  The second aspect of the present invention is based on a first image obtained by imaging a hit golf ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time. A simulation device for calculating a trajectory of the golf ball after being hit based on the detected velocity of the golf ball and the rotation of the golf ball and displaying an image of the golf ball flying according to the trajectory on the screen A feature point extraction that extracts a plurality of feature points constituting a part of the image of the surface of the golf ball by detecting pixels that are edges in each of the first image and the second image. Means, three-dimensional coordinate positions of the plurality of feature points extracted in the first image, and three-dimensional coordinate positions of the plurality of feature points extracted in the second image. Euler angle specifying means for specifying an Euler angle representing a positional relationship between the first image and the second image based on a distance from each of the first image, and the first image corresponding to the specified Euler angle. A predetermined feature point of the plurality of extracted feature points in the rotated first image and a predetermined feature point of the plurality of extracted feature points in the second image Correlation calculating means for calculating a correlation value by an image correlation method, and correlation for associating a feature point of the first image with a feature point of the second image based on the calculated correlation value A simulation system comprising: a detecting device configured to detect a rotation amount and a rotation direction of the golf ball based on the associated feature point of the first image and the feature point of the second image. is there.

  In the second aspect of the present invention, a plurality of feature points constituting a part of the image of the surface of the golf ball by detecting pixels serving as edges in each of the first image and the second image. A feature point extracting means for extracting a plurality of feature points extracted from the first image, and a three-dimensional coordinate position of the plurality of feature points extracted from the second image. The Euler angle specifying means for specifying the Euler angle representing the positional relationship between the first image and the second image based on the distance of the first image, and rotating the first image by the specified Euler angle, A correlation value between a predetermined feature point of the plurality of extracted feature points in the rotated first image and a predetermined feature point of the plurality of feature points extracted in the second image, Image correlation Correlation calculating means for calculating by the above, and associating means for associating the feature point of the first image with the feature point of the second image based on the calculated correlation value, Based on the feature point of the attached first image and the feature point of the second image, the rotation of the golf ball is detected by a detection device that detects the amount and direction of rotation of the golf ball.

  According to the present invention, rotation can be accurately detected for any ball.

  Embodiments of the present invention will be described below. Correspondences between constituent elements of the present invention and the embodiments described in the specification or the drawings are exemplified as follows. This description is intended to confirm that the embodiments supporting the present invention are described in the specification or the drawings. Therefore, even if there is an embodiment which is described in the specification or the drawings but is not described here as an embodiment corresponding to the constituent elements of the present invention, that is not the case. It does not mean that the form does not correspond to the constituent requirements. Conversely, even if an embodiment is described here as corresponding to a configuration requirement, that means that the embodiment does not correspond to a configuration requirement other than the configuration requirement. It's not something to do.

  The detection device according to the first aspect of the present invention is a detection device that detects the rotation of the golf ball based on an image of a moving golf ball (for example, the sensing of FIG. 1 having the rotation detection unit 72 of FIG. Block 22), each of a first image obtained by imaging the golf ball at a first time, and a second image obtained by imaging the golf ball at a second time after the first time; A feature point extracting unit (for example, feature point extracting unit 91 in FIG. 5) for extracting a plurality of feature points constituting a part of the image of the surface of the golf ball by detecting the pixel, and the first image And the first image and the first image based on the distances between the three-dimensional coordinate positions of the plurality of feature points extracted and the three-dimensional coordinate positions of the plurality of feature points extracted in the second image. Euler angle specifying means (for example, the simple rotation unit 92 in FIG. 5) for specifying the Euler angle representing the positional relationship of the image, and the first image is rotated by the specified Euler angle, and the rotated image is rotated. A correlation value between a predetermined feature point of the plurality of feature points extracted in the first image and a predetermined feature point of the plurality of feature points extracted in the second image is calculated using an image correlation method. Based on the calculated correlation value (for example, the feature point correspondence control unit 93 in FIG. 5 that executes the processing of steps S91 to S94 in FIG. 19), and the feature of the first image based on the calculated correlation value An association means for associating a point with the feature point of the second image (for example, the feature point correspondence control unit 93 in FIG. 5 that executes the process of step S95 in FIG. 19), Said first image Based on the feature points of the feature points second image to detect the rotation direction and rotation amount of the golf ball.

  A detection method of a detection device (for example, the sensing block 22 of FIG. 1 having the rotation detection unit 72 of FIG. 4) that detects the rotation of the golf ball based on an image of a moving golf ball. In each of a first image obtained by imaging the ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time, pixels that become edges are detected, A plurality of feature points constituting a part of the image of the surface of the golf ball are extracted (for example, the process of step S31 in FIG. 16), and the three-dimensional coordinate positions of the extracted feature points in the first image are And an Euler angle representing a positional relationship between the first image and the second image based on a distance from each of the three-dimensional coordinate positions of the extracted feature points in the second image. (For example, the process of step S32 in FIG. 16), the first image is rotated by the specified Euler angle, and among the extracted feature points in the rotated first image. A correlation value between the predetermined feature point of the second feature point and a predetermined feature point of the plurality of feature points extracted in the second image is calculated by an image correlation method (for example, in steps S91 to S94 in FIG. 19). Processing) and associating the feature points of the first image with the feature points of the second image based on the calculated correlation value (for example, the processing of step S95 in FIG. 19). The rotation amount and the rotation direction of the golf ball are detected based on the associated feature points of the first image and the second image.

  Embodiments of the present invention will be described below with reference to the drawings.

  FIG. 1 is a diagram showing an example of a simulation system 10 according to an embodiment of the present invention. The simulation system 10 is used for golf practice, for example, and is installed in a sports club or the like.

  In the simulation system 10, an image of a golf ball immediately after a user shots with a golf club is captured and analyzed by the sensing block 22, and the trajectory of the golf ball is calculated. For example, an image of a golf ball flying on a calculated trajectory is displayed on the screen 21 along with an image of a golf course or the like.

  In the simulation system 10, a golf club and a golf ball used for actual golf play are used, and the user shots the golf ball with the golf club so as to actually play golf on the golf course. That is, by using the simulation system 10, for example, even in a limited space, the user can experience as if he / she is playing on a golf course.

  Note that the golf ball used in the simulation system 10 does not need to be a ball with a special mark, pattern, coloring, or the like, and is a golf ball that is widely sold generally.

  FIG. 2 is a block diagram illustrating a configuration example of the sensing block 22. As shown in the figure, the sensing block 22 is configured by connecting a sensor unit 41 having a CCD camera or the like to a data processing unit 42.

  The sensor unit 41 is, for example, two CCD cameras, and includes a first camera and a second camera. In the example of the figure, a lens 51 of the first camera and a lens 52 of the second camera are included. It is shown. The first camera is, for example, a golf ball when a golf ball is shot with a golf club based on a signal output from a sensor connected to a tee or the like incorporated in a bat of the simulation system 10. An image is taken. The second camera captures an image of the golf ball after a predetermined time (for example, several milliseconds) after the first camera captures the image.

  Data of an image captured by the first camera of the sensor unit 41 and data of an image captured by the second camera are supplied to the data processing unit 42.

  The data processing unit 42 is configured to include a processor, a memory, and the like, and executes processing corresponding to software such as a prestored program. The data processing unit 42 determines the speed of the golf ball and the number of rotations of the golf ball per unit time based on the data of the image captured by the first camera and the data of the image captured by the second camera. The rotation direction is calculated, and the trajectory is calculated based on the speed of the golf ball and the rotation number and rotation direction of the golf ball per unit time, and image data to be displayed on the screen 21 is generated. .

  FIG. 3 is a block diagram illustrating a functional configuration example of software such as a program of the data processing unit 42.

  The imaging control unit 61 in the figure controls the imaging of the golf ball by the sensor unit 41, and is a golf ball image when shot with a golf club by the first camera of the sensor unit 41. One image is picked up, and the second camera picks up a second image, which is an image of the golf ball after a predetermined time, after being picked up by the first camera.

  The imaging control unit 61 outputs the first image data and the second image data captured by the sensor unit 41 to the image analysis unit 62.

  Based on the data of the first image and the data of the second image, the image analysis unit 62 calculates the speed of the golf ball, the number of rotations and the direction of rotation of the golf ball per unit time, as will be described later. Has been made. Then, the image analysis unit 62 specifies the movement of the golf ball based on the speed of the golf ball, the number of rotations and the rotation direction of the golf ball per unit time, and determines the position of the golf ball over time, for example, The coordinate value is calculated.

  The simulation image generation unit 63 identifies the trajectory based on the position of the golf ball with the passage of time calculated by the image analysis unit 62, and generates display data of the golf ball image corresponding to the trajectory. The simulation image generation unit 63 generates an image data to be displayed on the screen 21 by combining the golf ball image with a golf course image or the like.

  That is, a trajectory in which a golf ball actually flies after being shot is predicted by the image analysis unit 62, and an image of the flying ball is generated according to the trajectory by the simulation image generation unit 63 and displayed on the screen. become.

  FIG. 4 is a block diagram illustrating a detailed configuration example of the image analysis unit 62. As shown in the figure, the image analysis unit 62 includes a speed detection unit 71, a rotation detection unit 72, and a motion calculation unit 73.

  For example, the speed detection unit 71 specifies a movement distance of the golf ball per unit time based on the first image and the second image, and calculates (detects) the speed of the golf ball shot by the golf club. A block. For example, the speed detection unit 71 may be the same as that used in a conventional golf simulation system.

  The rotation detection unit 72 specifies the rotation amount and rotation direction of the golf ball per unit time based on the first image and the second image, and the rotation number per unit time of the golf ball shot by the golf club, And a function block for detecting information related to rotation including a numerical value indicating the rotation direction. Details of the rotation detector 72 will be described later with reference to FIG.

  The motion calculation unit 73 is based on the golf ball speed detected by the speed detection unit 71 and the information on the rotation of the golf ball detected by the rotation detection unit 72, for example, the trajectory of the shot golf ball flying. Calculate the trajectory. For example, the motion calculation unit 73 is configured to calculate the trajectory of the golf ball by predicting the position of the golf ball over time by specifying the position of the golf ball at a predetermined time as a coordinate value. . For example, the motion calculation unit 73 may be the same as that used in a conventional golf simulation system.

  FIG. 5 is a block diagram illustrating a detailed configuration example of the rotation detection unit 72. As shown in the figure, the rotation detection unit 72 includes a feature point extraction unit 91, a simple rotation unit 92, a feature point correspondence control unit 93, and a rotation number detection control unit 94.

  The feature point extraction unit 91 detects edges in the first image and the second image. Here, the edge is, for example, an area in the image where the change in the luminance value of the pixel is larger than a preset threshold value. The feature point extraction unit 91 stores a plurality of edges detected in the first image as feature points of the first image, respectively, and uses a plurality of edges detected in the second image as feature points of the second image, respectively. It is made to memorize.

  FIG. 6 is an example of an image of a golf ball, and is a diagram for explaining an example of feature points detected from the image of the golf ball. The golf ball shown in FIG. 1 is a mark composed of two black triangles and one black square, and a mark (hereinafter simply referred to as a mark) in which the letter “F” is made into a graphic. ing. When the image of FIG. 6 is analyzed by the feature point extraction unit 91, areas including pixels constituting the outline of the mark are detected as edges, and these edges, for example, the areas indicated by small rectangles in the figure are detected. Each is stored as a feature point.

  FIG. 7 shows an example of a first image in which feature points are detected, and FIG. 8 shows an example of a second image in which feature points are detected. As shown in the figure, in each of the first image and the second image, each of the regions (in this example, a rectangular region) including the pixels constituting the outline of the mark is detected as a feature point.

  Further, in the second image in FIG. 8 taken after the first image in FIG. 7, the position of the mark moves in the upper right direction in the figure due to the rotation of the golf ball shot with the golf club. doing.

  Here, in order to simplify the explanation, examples of marks composed of two black triangles and one black square are shown in each figure. A contour such as a mark including a brand name, a maker name, etc., and a mark made up of a pattern is detected as an edge and becomes a feature point. Further, as a matter of course, in the present invention, there is no problem in detecting the feature points depending on the color of the golf ball, the color of the mark attached to the golf ball, or the like. In short, it is only necessary that a part detected as an edge exists in a part of the surface of the golf ball in the captured image.

  That is, in the present invention, it is possible to detect a part of the surface of the golf ball formed of an arbitrary shape, color, pattern, etc. as a feature point.

  The simple rotation unit 92 rotates each feature point detected in the first image so as to approach the feature point detected in the second image. At this time, each of the feature points detected in the first image is rotated in the three-dimensional space.

  For example, as illustrated in FIG. 9, it is assumed that a feature point group 111 including a plurality of feature points is detected in the first image, and as illustrated in FIG. 10, from the plurality of feature points in the second image. It is assumed that the feature point group 112 is detected. In FIG. 9, the feature point group 111 is indicated by a one-dot chain line, and in FIG. 10, the feature point group 112 is indicated by a dotted line.

  For example, the simple rotation unit 92 specifies the coordinate position of the feature point group 111 in the three-dimensional space, and rotates the coordinate position of the feature point group 111 by a predetermined Euler angle. Of course, the first image and the second image are two-dimensional images, respectively. However, since a golf ball is considered to have a shape close to a true sphere, the coordinate position of the two-dimensional image The coordinate position at can be specified.

  For example, as shown in FIG. 11, the simple rotation unit 92 specifies the center point (cx, cy) and radius r of the image of the golf ball, and is given as indicated by the dotted arrow in the figure. The coordinate position of the feature point group 111 in the three-dimensional space is moved to a position corresponding to the Euler angle. Here, the Euler angle is specified by three parameters α, β, and γ. For example, if the Euler angles are α = 30, β = 31, and γ = 32, rotate 30 ° around the z-axis and then rotate 31 ° around the x-axis (the z-axis rotated at this time is changed to the z′-axis) ), And further rotating by 32 ° around the z ′ axis. Here, the axis perpendicular to the screen is the z-axis, and the forward direction from the rear of the screen is the positive direction.

  The simple rotation unit 92 plots the coordinate position in the three-dimensional space of the feature point group 111 rotated by a predetermined Euler angle on the second image as shown in FIG. 12, for example. Then, the distance between each feature point constituting the feature point group 111 plotted on the second image and the feature point constituting the feature point group 112 at the closest position is calculated.

  Note that the distance calculated at this time is calculated based on the three-dimensional coordinate position of each feature point.

  For example, when the feature point constituting the feature point group 112 located closest to the feature point 111a constituting the feature point group 111 plotted on the second image is the feature point 112a, the feature point 111a and the feature point D1 is calculated as the distance from the point 112a. Further, when the feature point constituting the feature point group 112 located closest to the feature point 111b constituting the feature point group 111 plotted on the second image is the feature point 112c, the feature point 111b and feature The distance d2 is calculated from the point 112c. Furthermore, when the feature point constituting the feature point group 112 located closest to the feature point 111c constituting the feature point group 111 plotted on the second image is the feature point 112a, the feature point 111c and feature D3 is calculated as the distance from the point 112a.

  Thus, for example, when there are n feature points constituting the feature point group 111, the distances d1, d2, d3,... Dn are calculated.

  Then, the simple rotation unit 92 further calculates the total sum of the calculated distances d1, d2, d3,. The sum of the distances d1 to dn calculated here is referred to as the distance between the nearest feature points.

  The simple rotation unit 92 moves the coordinate position of the feature point group 111 in the three-dimensional space to the position corresponding to the given Euler angle and plots the distance between the nearest feature points each time it is plotted on the second image. It is made to calculate. For example, it is assumed that the above-described parameters α, β, and γ are changed at predetermined intervals within a predetermined range, and the feature point group 111 is moved (rotated) to K positions. In this case, the simple rotation unit 92 calculates the distance between the K nearest feature points.

  Further, the simple rotation unit 92 specifies the smallest one of the calculated K nearest neighbor feature point distances, and identifies the Euler angle corresponding to the smallest nearest feature point distance. To do.

  In this way, the Euler angle of the minimum distance between nearest feature points is specified, and the coordinate position of the feature point group 111 in the three-dimensional space is moved to the position corresponding to the Euler angle on the second image. Is plotted as shown in FIG. In FIG. 13, the feature point group 111 rotated by the Euler angle of the minimum distance between the nearest neighbor feature points is shown by a small rectangle in the figure. In the figure, the rotated feature point group 111 is plotted at a position substantially coincident with the mark of the second image.

  Thus, it is possible to roughly estimate the amount of rotation of the golf ball from the time of the first image to the time of the second image by specifying the Euler angle of the distance between the nearest adjacent feature points. is there. However, at this time, it has not yet been specified which of the feature points constituting the feature point group 111 corresponds to which of the feature points constituting the feature point group 112. In order to accurately detect the rotation amount of the golf ball, it is necessary to associate the feature points constituting the feature point group 111 with the feature points constituting the feature point group 112. This association processing is performed by the feature point correspondence control unit 93 as described later.

  The feature point correspondence control unit 93 rotates the first image by the Euler angle corresponding to the minimum distance between the nearest feature points specified by the simple rotation unit 92. The simple rotation unit 92 rotates only the feature point group 111 of the first image, whereas the feature point correspondence control unit 93 rotates the first image itself.

  Further, the feature point correspondence control unit 93 extracts, for example, feature points having a feature amount exceeding a preset threshold value from among the feature points constituting the feature point group 111. This feature amount is, for example, a luminance value of a feature point pixel, a difference between luminance values of a feature point pixel and surrounding pixels, or the like.

  The feature point correspondence control unit 93 further extracts feature points that are separated from each other by a preset number (for example, three) from feature points extracted as feature points having feature amounts exceeding the threshold. . For example, among the feature points constituting the feature point group 111, feature points 111a, 111f, 111m, 111r, and 111t each having a feature amount exceeding the threshold are extracted, and the feature point 111a having the largest feature amount is further extracted. The extracted feature points 111m that are the most distant from the feature points 111a are specified, and the feature points 111t that are the most distant from the feature points 111a and 111m are specified. Three (in this case, feature point 111a, feature point 111m, and feature point 111t) are further extracted.

  Then, the feature point correspondence control unit 93 configures the feature point group 111 extracted as feature points that are separated from each other (for example, three) from the feature points in order of increasing distance (for example, three). ) Extract feature points constituting the feature point group 112. For example, feature points 112a, 112b, and 112c that are close to the feature point 111a are extracted, feature points 112m, 112n, and 112o that are close to the feature point 111m are extracted, and feature points 112t, 112s that are close to the feature point 111t. , 112u are extracted. At this time, the distance between the feature point 111a and the feature point 112a, the distance between the feature point 111a and the feature point 112a,..., The distance between the feature point 111t and the feature point 112u is the smallest nearest neighbor feature. The calculation is performed in a state where the distance between the points is rotated by the Euler angle.

  Furthermore, the feature point correspondence control unit 93 sets, for example, a first image area A1 having a predetermined size centered on the feature point 111a, and has a predetermined size centered on the feature points 112a, 112b, and 112c. Two image areas B11, B12, and B13 are set. Then, the feature point correspondence control unit 93 calculates a correlation value between the region A1 and the region B11, a correlation value between the region A1 and the region B12, and a correlation value between the region A1 and the region B13 by the image correlation method. Based on the correlation value thus calculated, it can be estimated that the feature point included in the region of the second image where the highest correlation value is obtained is the feature point of the second image corresponding to the feature point 111a. .

  For example, when the correlation value between the region A1 and the region B12 is the highest among the three correlation values calculated as described above, the feature point of the second image corresponding to the feature point 111a of the first image is the feature It can be estimated that the point 112b.

  Similarly, the feature point correspondence control unit 93 sets a first image area A2 having a predetermined size centered on the feature point 111m, and has a second size having a predetermined size centered on the feature points 112m, 112n, and 112o. Regions B21, B22, and B23 of the image are set, and a correlation value between the region A2 and the region B21, a correlation value between the region A2 and the region B22, and a correlation value between the region A2 and the region B23 are calculated by the image correlation method. The feature point correspondence control unit 93 sets a first image area A3 having a predetermined size centered on the feature point 111t, and a second image area having a predetermined size centered on the feature points 112t, 112s, and 112u. B31, B32, and B33 are set, and the correlation value between the region A3 and the region B31, the correlation value between the region A3 and the region B32, and the correlation value between the region A3 and the region B33 are calculated by the image correlation method. Based on the correlation value calculated in this way, the feature point included in the region of the second image where the highest correlation value is obtained is the feature point of the second image corresponding to the feature point 111m or the feature point 111t. It can be estimated that there is.

  Thereby, for example, as shown in FIG. 14, three feature points constituting the feature point group 112 corresponding to each of the three feature points constituting the feature point group 111 are specified. In the example of the figure, the arrows in the figure indicate the correspondence between the three feature points constituting the feature point group 111 and the three feature points constituting the feature point group 112.

  The number-of-rotations detection control unit 94 shown in FIG. 5 performs the rotation amount and rotation of the golf ball per unit time based on the feature points of the first image and the feature points of the second image associated by the feature point correspondence control unit 93. The direction is specified, and information about the rotation including the number of rotations per unit time of the golf ball shot by the golf club and a numerical value indicating the rotation direction is detected. The rotation speed detection control unit 94 can use, for example, the same one used in a conventional golf simulation system.

  Next, the rotation detection process by the rotation detection unit 72 in FIG. 4 will be described with reference to the flowchart in FIG.

  In step S <b> 11, the feature point extraction unit 91, the simple rotation unit 92, and the feature point correspondence control unit 93 execute a feature portion specifying process which will be described later with reference to FIG. 16. Thereby, for example, each feature point is associated in the first image and the second image.

  In step S12, the rotation speed detection control unit 94 starts from the time when the first image is captured based on the feature point of the first image and the feature point of the second image associated with each other in the process of step S11. It is specified how much the golf ball has rotated (the amount of rotation) until the time when is captured. At this time, the rotation direction of the golf ball is also specified.

  In step S13, the rotation speed detection control unit 94 calculates the rotation speed of the golf ball per unit time based on the rotation amount specified in the process of step S12.

  In this way, the rotation detection process is executed.

  Next, with reference to the flowchart of FIG. 16, the detail of the characteristic part specific process of step S11 of FIG. 15 is demonstrated.

  In step S31, the feature point extraction unit 91 executes feature point detection processing. Here, the details of the feature point detection processing in step S31 in FIG. 16 will be described with reference to the flowchart in FIG.

  In step S51, the feature point extraction unit 91 detects edges in the first image and the second image. As described above, the edge is, for example, an area in the image where the change in the luminance value of the pixel is larger than a preset threshold value.

  In step S52, the feature point extraction unit 91 stores the plurality of edges detected in the first image as the feature points of the first image by the processing in step S51, and the plurality of edges detected in the second image. Are stored as feature points of the second image. Thereby, for example, each of the regions (points) indicated by small rectangles in the drawings as shown in FIGS. 7 and 8 is stored as a feature point.

  In this way, the feature point detection process is executed. By doing in this way, as mentioned above, it is possible to detect a part of the surface of the golf ball constituted by an arbitrary shape, color, pattern or the like as a feature point.

  Returning to FIG. 16, after the process of step S31, in step S32, the simple rotation unit 92 executes a feature point rotation process. Here, the details of the feature point rotation processing in step S32 in FIG. 16 will be described with reference to the flowchart in FIG.

  In step S71, the simple rotation unit 92 sets a predetermined Euler angle.

  In step S72, the simple rotation unit 92 has the feature point of the first image configured by the feature point detected from the first image by the process of step S31 at the position corresponding to the Euler angle set by the process of step S71. Rotate the group.

  At this time, as described above, the simple rotation unit 92 specifies the center point (cx, cy) and the radius r of the image of the golf ball, for example, as shown in FIG. As described above, the coordinate position of the feature point group 111 in the three-dimensional space is moved to the position corresponding to the given Euler angle.

  In step S73, the simple rotation unit 92 plots the coordinate position of the feature point group rotated in the process of step S72 in the three-dimensional space on the second image. At this time, for example, as shown in FIG. 12, the rotated feature point group 111 is plotted on the second image.

  In step S74, the simple rotation unit 92 calculates the distance between the nearest feature points. As described above, the distance between the nearest feature points is the distance between the feature points constituting the feature point group 111 plotted on the second image and the feature points constituting the feature point group 112 at the closest position. The sum of

  In step S75, the simple rotation unit 92 stores the value of the distance between the nearest feature points calculated in step S74 in association with the Euler angle set in the process of step S71.

  In step S76, the simple rotation unit 92 determines whether or not there is an unset Euler angle. If it is determined that there is an unset Euler angle, the process proceeds to step S77.

  In step S77, the simple rotation unit 92 changes the Euler angle. As described above, the simple rotation unit 92 changes the above-described parameters α, β, γ, for example, at predetermined intervals within a predetermined range, and sets the feature point group 111 at K positions. Is moved (rotated). In step S76, for example, it is determined that there is an unset Euler angle until all K Euler angles are set. In each step, any one of parameters α, β, and γ is preset in step S77. It will be changed at different intervals.

  After the process of step S77, the process returns to step S72, and the subsequent processes are repeatedly executed. However, in this case, in step S72, the feature point group of the first image is rotated to the position corresponding to the Euler angle set (changed) in the process of step S77, and in step S75, the calculation is performed in step S74. The value of the distance between the nearest feature points is stored in association with the Euler angle set in the process of step S77.

  If it is determined in step S76 that there is no unset Euler angle, the process proceeds to step S78.

  In step S78, among the K nearest neighbor feature point distances stored in step S75, for example, the smallest distance is specified, and the Euler corresponding to the smallest nearest neighbor feature point distance is specified. Identify the corner.

  In this way, the feature point rotation process is executed. In this way, it is possible to roughly estimate the amount of rotation of the golf ball from the time when the first image is captured until the time when the second image is captured by rotating only the feature points. . By doing so, it is not necessary to rotate the image each time, and the processing can be speeded up. In addition, when performing the calculation by the image correlation method in the feature point association processing to be executed later, the Euler angles specified by the feature point rotation processing are used to realize accurate and high-speed feature point association. It becomes possible.

  Returning to FIG. 16, after the process of step S <b> 32, in step S <b> 33, the feature point association control unit 93 executes a feature point association process. Here, with reference to the flowchart of FIG. 19, the detail of the feature point matching process of step S33 of FIG. 16 is demonstrated.

  In step S91, the feature point correspondence control unit 93 extracts three feature points from the feature points constituting the feature point group 111 of the first image. At this time, as described above, for example, from the feature points extracted as feature points having feature amounts exceeding the threshold value of the feature point group 111, three feature points that are separated from each other are further extracted.

  In step S92, the feature point correspondence control unit 93 rotates the first image by the Euler angle specified in the process of step S78.

  In step S93, the feature point correspondence control unit 93 extracts a plurality of feature points of the second image that are close to each of the three feature points extracted in the process of step S91. At this time, as described above, for example, the feature points constituting the feature point group 112 are extracted in order of the distance from each of the three feature points constituting the feature point group 111 (for example, three). For example, feature points 112a, 112b, and 112c that are close to the feature point 111a are extracted, feature points 112m, 112n, and 112o that are close to the feature point 111m are extracted, and feature points 112t, 112s that are close to the feature point 111t. 112u are extracted.

  In step S94, the feature point correspondence control unit 93 calculates a correlation value by an image correlation method. At this time, as described above, the feature point correspondence control unit 93 sets, for example, the first image area A1 having a predetermined size centered on the feature point 111a and centers on the feature points 112a, 112b, and 112c. Regions B11, B12, and B13 of the second image having a predetermined size are set. Then, the feature point correspondence control unit 93 calculates a correlation value between the region A1 and the region B11, a correlation value between the region A1 and the region B12, and a correlation value between the region A1 and the region B13 by the image correlation method.

  Similarly, the feature point correspondence control unit 93 sets a first image area A2 having a predetermined size centered on the feature point 111m, and has a second size having a predetermined size centered on the feature points 112m, 112n, and 112o. Regions B21, B22, and B23 of the image are set, and a correlation value between the region A2 and the region B21, a correlation value between the region A2 and the region B22, and a correlation value between the region A2 and the region B23 are calculated by the image correlation method. The feature point correspondence control unit 93 sets a first image area A3 having a predetermined size centered on the feature point 111t, and a second image area having a predetermined size centered on the feature points 112t, 112s, and 112u. B31, B32, and B33 are set, and the correlation value between the region A3 and the region B31, the correlation value between the region A3 and the region B32, and the correlation value between the region A3 and the region B33 are calculated by the image correlation method.

  In step S95, the feature point correspondence control unit 93 specifies the feature point of the second image corresponding to the three feature points of the first image having the highest correlation value obtained in the process of step S94. Thereby, for example, as shown in FIG. 14, three feature points constituting the feature point group 112 corresponding to each of the three feature points constituting the feature point group 111 are specified.

  In this way, the feature point association process is executed. The feature point association process is not performed based on, for example, an image of only the feature point of the first image rotated by the feature point rotation process, but uses the Euler angle specified by the feature point rotation process. The first image is rotated and the calculation by the image correlation method is performed. By doing in this way, it becomes possible to implement | achieve the matching of a more accurate and high-speed feature point. As a result, it is possible to accurately detect the rotation direction and the rotation speed of the golf ball.

  In order to detect the direction and number of rotations of a golf ball using conventional techniques, a predetermined mark or pattern is added to identify the rotation direction and angle of rotation of the ball in the image. It was necessary to use a golf ball. Therefore, for example, there is a problem that the simulation system cannot be used by freely using a ball that the user likes.

  On the other hand, in the present invention, as described above, the edge of the image is detected and used as a feature point. In the present invention, since the detection of the feature points and the association of the feature points of the first image and the second image are performed after the golf ball is shot, any ball can be used. .

  Further, as described above, since the feature point may be detected as an edge, the golf ball used in the simulation system 10 of the present invention is not necessarily provided with a clear mark or the like. Absent. Therefore, in the present invention, for example, a so-called lost ball can be used.

  Furthermore, although the example which applies this invention to the simulation system 10 was demonstrated above, this invention is not necessarily applied to a simulation system. For example, the present invention can also be applied to a device that needs to detect the rotation direction and the number of rotations of a golf ball shot with a golf club, such as when a golf club is manufactured or finely adjusted.

  Alternatively, it is of course possible to use the present invention for detecting the rotational direction, rotational speed, etc. of a sphere other than the golf ball.

  Incidentally, in the example described above with reference to FIG. 2, it has been described that the sensor unit 41 of the sensing block 22 has two cameras, the first camera and the second camera. However, the sensor unit 41 has one camera. You may be set as the structure which has.

  FIG. 20 is a block diagram illustrating a configuration of the sensor unit 41 having one (one) camera. In the figure, it is assumed that a golf ball is set on a tee 160 and shot by a golf club. The shot golf ball flies from the right side to the left side in the drawing.

  The sensor 155-1 and the sensor 155-2 are each configured to detect an object existing in front (upper side in the drawing). When the golf club head passes in front of sensor 155-1 and sensor 155-2 to shot a golf ball set on tee 160, each of sensor 155-1 and sensor 155-2 A detection signal is output when the club head is detected.

  The control unit 156 outputs a shutter signal of the camera 151 and a light emission signal for causing the strobe 161 and the strobe 162 to emit light based on detection signals from the sensors 155-1 and 155-2.

  The strobe 161 and strobe 162 are configured as, for example, rod-shaped strobes.

  The control unit 156 detects whether the sensor 151-1 or the sensor 155-2 close to the tee 160 (in this case, the sensor 155-1) detects the detection signal, and only after a preset time, the camera 151 The first pulse (electronic shutter) in the shutter signal is output, and a light emission signal for causing the strobe 161 to emit light is output.

  Further, the control unit 156 outputs a second pulse (electronic shutter) in the shutter signal of the camera 151 and outputs a strobe 162 after a predetermined time after outputting the first pulse in the shutter signal of the camera 151. A light emission signal for emitting light is output. Here, it is assumed that the predetermined time is obtained based on, for example, the time from when the sensor 155-2 outputs a detection signal until the sensor 155-1 outputs the detection signal.

  FIG. 21 is an example of a timing chart of signals output from the control unit 156. In the example shown in the figure, the waveforms of the shutter signal 181, the light emission signal 182 of the strobe 161, the light emission signal 183 of the strobe 162, and the readout signal 184 are shown. In the figure, the horizontal axis represents time, the vertical axis represents voltage value, the shutter signal 181 and the readout signal 184 are signals forming a downward convex pulse in the figure, and the strobe 161 emits light. The signal 182 and the light emission signal 183 of the strobe 162 are signals that form a convex pulse in the upward direction in the figure.

  Charges are accumulated in a photoelectric conversion element such as a CCD, which is the imaging element of the camera 151, for the time T1, which is the pulse width of the shutter signal 181. That is, the light that has been condensed by the lens of the camera 151 for the time T1 with the pulse width of the shutter signal 181 enters the image sensor, and the light is converted into electric charges by each image sensor and accumulated. Will be. Then, by supplying a pulse of the readout signal 184, a signal corresponding to the electric charge accumulated in each image sensor is output as a signal constituting image data for one frame. Thereby, for example, a pixel value is set based on a signal output from each imaging device, and one image composed of these pixels is generated.

  In addition, the strobe 161 and the strobe 162 emit light at the timing when pulses are output in the light emission signal 182 of the strobe 161 and the light emission signal 183 of the strobe 162, respectively.

  As described above, the pulse of the light emission signal that causes the strobe 161 to emit light is generated at the same timing as the first pulse (left pulse in the figure) in the shutter signal 181. In addition, a second pulse (right pulse in the figure) is generated in the shutter signal 181 after a predetermined time from the generation of the first pulse in the shutter signal 181, and the strobe 162 is emitted at the same timing. A pulse of the light emission signal is generated.

  Then, after the second pulse in the shutter signal 181 is generated, a pulse in the readout signal 184 is generated after a preset time.

  Thus, by supplying the camera 151 with the shutter signal 181, the light emission signal 182 of the strobe 161, the light emission signal 183 of the strobe 162, and the readout signal 184, the first signal in the shutter signal 181 is included in one frame image. An image of the golf ball at the time corresponding to this pulse and an image of the golf ball at the time corresponding to the second pulse in the shutter signal 181 are copied.

  By doing in this way, it is possible to obtain a golf ball image corresponding to the first image and a golf ball image corresponding to the second image as one frame image (one image). Become. That is, if the sensor unit 41 is configured as shown in FIG. 20, one camera 151 can capture one image corresponding to the first image and the second image.

  Instead of capturing the first image and the second image with two cameras, the configuration corresponding to the first image and the second image is captured with only one camera. It is possible to reduce the size of the device and to reduce the manufacturing cost of the device.

  FIG. 22 is a diagram illustrating an example of a golf ball image 200 captured by the camera 151. The image 200 in the figure includes a golf ball image 201 at a time corresponding to the first pulse in the shutter signal 181 and a golf ball image 202 at a time corresponding to the second pulse in the shutter signal 181. ing.

  For example, the golf ball image 201 and the golf ball image 202 are obtained by executing the above-described processes 15 to 19 with the golf ball image 201 as the first image and the golf ball image 202 as the second image. The feature points are associated with each other, and the rotation direction and the number of rotations of the golf ball can be accurately detected.

  In the present invention, as shown in the timing chart of FIG. 21, the pulse width (time T1) of the shutter signal 181 is made sufficiently short so that the strobe light is emitted at the same timing as the pulse of the shutter signal 181. Thus, the golf ball image 201 and the golf ball image 202 can be clearly captured.

  Theoretically, for example, if the pulse width of the shutter signal is set longer and the strobe is caused to emit light twice while the pulse is formed in the shutter signal, the first image described above is obtained as one image. It is possible to obtain a golf ball image corresponding to, and a golf ball image corresponding to the second image. However, in this case, an image similar to an image in which the exposure time of the camera is set to be long is captured. As a result, an afterimage or the like is generated between the golf ball image 201 and the golf ball image 202. The golf ball image 201 and the golf ball image 202 cannot be clearly captured.

  In contrast, in the present invention, the pulse width of the shutter signal 181 is sufficiently shortened so that the strobe light is emitted at the same timing, so that an afterimage or the like is generated between the golf ball image 201 and the golf ball image 202. , And the golf ball image 201 and the golf ball image 202 can be clearly captured.

  The series of processes described above can be executed by hardware, or can be executed by software. When the above-described series of processing is executed by software, a program constituting the software executes various functions by installing a computer incorporated in dedicated hardware or various programs. For example, a general-purpose personal computer 700 as shown in FIG. 23 is installed from a network or a recording medium.

  In FIG. 23, a CPU (Central Processing Unit) 701 executes various processes according to a program stored in a ROM (Read Only Memory) 702 or a program loaded from a storage unit 708 into a RAM (Random Access Memory) 703. To do. The RAM 703 also appropriately stores data necessary for the CPU 701 to execute various processes.

  The CPU 701, ROM 702, and RAM 703 are connected to each other via a bus 704. An input / output interface 705 is also connected to the bus 704.

  The input / output interface 705 includes an input unit 706 including a keyboard and a mouse, a display including a CRT (Cathode Ray Tube) and an LCD (Liquid Crystal display), an output unit 707 including a speaker, and a hard disk. A communication unit 709 including a storage unit 708, a network interface card such as a modem and a LAN card, and the like is connected. The communication unit 709 performs communication processing via a network including the Internet.

  A drive 710 is also connected to the input / output interface 705 as necessary, and a removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory is appropriately mounted, and a computer program read from them is loaded. It is installed in the storage unit 708 as necessary.

  When the above-described series of processing is executed by software, a program constituting the software is installed from a network such as the Internet or a recording medium such as a removable medium 711.

  This recording medium is a magnetic disk (including a floppy disk (registered trademark)) on which the program is recorded, which is distributed to distribute the program to the user, separately from the apparatus main body, shown in FIG. Removable media consisting of optical disks (including CD-ROM (compact disk-read only memory), DVD (digital versatile disk)), magneto-optical disks (including MD (mini-disk) (registered trademark)), or semiconductor memory It includes not only those configured by 711 but also those configured by a ROM 702 storing a program, a hard disk included in the storage unit 708, and the like that are distributed to the user in a state of being incorporated in the apparatus main body in advance.

  Note that the steps of executing the series of processes described above in this specification are performed in parallel or individually even if they are not necessarily processed in time series, as well as processes performed in time series in the order described. The processing to be performed is also included.

It is a figure which shows the example of the simulation system which concerns on one embodiment of this invention. It is a block diagram which shows the structural example of the sensing block of FIG. It is a block diagram which shows the functional structural example of software, such as a program of the data processing part of FIG. It is a block diagram which shows the structural example of the image analysis part of FIG. It is a block diagram which shows the structural example of the rotation detection part of FIG. It is a figure which shows the example of the image of the golf ball from which the feature point was detected. It is a figure which shows the example of a 1st image. It is a figure which shows the example of a 2nd image. It is a figure which shows another example of a 1st image. It is a figure which shows another example of a 2nd image. It is a figure explaining rotation of a feature point. It is a figure explaining rotation of a feature point. It is a figure which shows the example of the feature point group of the 1st image plotted in the 2nd image. It is a figure explaining matching of a feature point. It is a flowchart explaining the example of a rotation detection process. It is a flowchart explaining the example of a corresponding | compatible part specific process. It is a flowchart explaining the example of a feature point detection process. It is a flowchart explaining the example of a feature point rotation process. It is a flowchart explaining the example of a feature point matching process. It is a figure which shows another structural example of the sensor part of FIG. It is a figure which shows the example of the timing chart of the signal which the control part of FIG. 20 outputs. It is a figure which shows the example of the image imaged by the sensor part of FIG. And FIG. 16 is a block diagram illustrating a configuration example of a personal computer.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 Simulation system, 21 Screen, 22 Sensing block, 41 Sensor part, 42 Data processing part, 61 Imaging control part, 62 Image analysis part, 63 Simulation image generation part, 71 Speed detection part, 72 Rotation detection part, 73 Motion calculation part , 91 feature point extraction unit, 92 simple rotation unit, 93 feature point correspondence control unit, 94 rotation speed detection control unit, 151 camera, 156 control unit, 161 strobe, 162 strobe

Claims (7)

A detection device for detecting rotation of the golf ball based on an image of a moving golf ball,
By detecting pixels that are edges in each of a first image obtained by imaging the golf ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time. A feature point extracting means for extracting a plurality of feature points constituting a part of the image of the surface of the golf ball;
Based on the distances between the three-dimensional coordinate positions of the plurality of feature points extracted in the first image and the three-dimensional coordinate positions of the plurality of feature points extracted in the second image. Euler angle specifying means for specifying an Euler angle representing a positional relationship between an image and the second image;
The first image is rotated by the specified Euler angle, a predetermined feature point among the plurality of extracted feature points in the rotated first image, and the extraction in the second image. Correlation calculating means for calculating a correlation value with a predetermined feature point of the plurality of feature points by an image correlation method;
Association means for associating the feature points of the first image and the feature points of the second image based on the calculated correlation value;
A detection device that detects a rotation amount and a rotation direction of the golf ball based on the feature point of the first image and the feature point of the second image that are associated with each other. A first strobe that emits light at the first time;
A second strobe that emits light at the second time;
A camera that images the golf ball as a subject at the first time and the second time;
The detection device according to claim 1, wherein an image of the golf ball corresponding to the first image and an image of the golf ball corresponding to the first image are captured by the one camera. The Euler angle specifying means is:
Based on the given parameter, the three-dimensional coordinate positions of the extracted feature points in the first image are rotated by the Euler angle corresponding to the parameter to be displayed on the second image. A feature point rotation means for plotting;
Among the given parameters, each of the three-dimensional coordinate positions of the plurality of feature points extracted in the rotated first image and the three-dimensional of the plurality of feature points extracted in the second image The detection device according to claim 2, further comprising: a parameter specifying unit that specifies the parameter having the highest evaluation value calculated based on a distance from each of the coordinate positions. A detection method of a detection device for detecting rotation of the golf ball based on an image obtained by imaging a moving golf ball,
By detecting pixels that are edges in each of a first image obtained by imaging the golf ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time. Extracting a plurality of feature points constituting part of the image of the surface of the golf ball;
Based on the distances between the three-dimensional coordinate positions of the plurality of feature points extracted in the first image and the three-dimensional coordinate positions of the plurality of feature points extracted in the second image. Identify an Euler angle representing the positional relationship between the image and the second image;
The first image is rotated by the specified Euler angle, a predetermined feature point among the plurality of extracted feature points in the rotated first image, and the extraction in the second image. A correlation value with a predetermined one of the plurality of feature points is calculated by an image correlation method,
Correlating the feature points of the first image with the feature points of the second image based on the calculated correlation value;
A detection method for detecting a rotation amount and a rotation direction of the golf ball based on the feature point of the first image and the feature point of the second image that are associated with each other. Computer
A detection device for detecting rotation of the golf ball based on an image of a moving golf ball,
By detecting pixels that are edges in each of a first image obtained by imaging the golf ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time. A feature point extracting means for extracting a plurality of feature points constituting a part of the image of the surface of the golf ball;
Based on the distances between the three-dimensional coordinate positions of the plurality of feature points extracted in the first image and the three-dimensional coordinate positions of the plurality of feature points extracted in the second image. Euler angle specifying means for specifying an Euler angle representing a positional relationship between an image and the second image;
The first image is rotated by the specified Euler angle, a predetermined feature point among the plurality of extracted feature points in the rotated first image, and the extraction in the second image. Correlation calculating means for calculating a correlation value with a predetermined feature point of the plurality of feature points by an image correlation method;
Association means for associating the feature points of the first image and the feature points of the second image based on the calculated correlation value;
A program that functions as a detection device that detects a rotation amount and a rotation direction of the golf ball based on the associated feature points of the first image and the second image.   A recording medium on which the program according to claim 5 is recorded. The velocity of the golf ball detected based on a first image obtained by imaging the hit golf ball at a first time and a second image obtained by imaging the golf ball at a second time after the first time. And a simulation device for displaying an image of the golf ball flying according to the trajectory on the screen by calculating a trajectory of the golf ball after being hit based on the rotation of the golf ball,
A feature point extracting means for extracting a plurality of feature points constituting a part of the image of the surface of the golf ball by detecting pixels serving as edges in each of the first image and the second image;
Based on the distances between the three-dimensional coordinate positions of the plurality of feature points extracted in the first image and the three-dimensional coordinate positions of the plurality of feature points extracted in the second image. Euler angle specifying means for specifying an Euler angle representing a positional relationship between an image and the second image;
The first image is rotated by the specified Euler angle, a predetermined feature point among the plurality of extracted feature points in the rotated first image, and the extraction in the second image. Correlation calculating means for calculating a correlation value with a predetermined feature point of the plurality of feature points by an image correlation method;
Association means for associating the feature points of the first image and the feature points of the second image based on the calculated correlation value;
A simulation system comprising: a detection device that detects a rotation amount and a rotation direction of the golf ball based on the associated feature points of the first image and the second image.

Images