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WO2017032190A1 - Procédé et appareil d'identification d'image - Google Patents

Procédé et appareil d'identification d'image Download PDF

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Publication number
WO2017032190A1
WO2017032190A1 PCT/CN2016/090712 CN2016090712W WO2017032190A1 WO 2017032190 A1 WO2017032190 A1 WO 2017032190A1 CN 2016090712 W CN2016090712 W CN 2016090712W WO 2017032190 A1 WO2017032190 A1 WO 2017032190A1
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WO
WIPO (PCT)
Prior art keywords
shape
image
feature
target geometry
geometric
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2016/090712
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English (en)
Chinese (zh)
Inventor
付金祥
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangzhou Shirui Electronics Co Ltd
Original Assignee
Guangzhou Shirui Electronics Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangzhou Shirui Electronics Co Ltd filed Critical Guangzhou Shirui Electronics Co Ltd
Publication of WO2017032190A1 publication Critical patent/WO2017032190A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/255—Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/245—Aligning, centring, orientation detection or correction of the image by locating a pattern; Special marks for positioning

Definitions

  • the present invention relates to the field of image recognition technologies, and in particular, to a method and apparatus for shape recognition in an image.
  • the present invention provides a method and device for image recognition, which can identify different kinds of geometric figures contained in objects in an image, and abstract and simplify the real objects to a certain extent.
  • An aspect of the present invention provides a method for image recognition, including:
  • the target geometry is marked for display.
  • the identifying the geometric shape included in the image includes:
  • a closed shape formed by the lines is identified, the closed shape being determined as a geometric shape included in the image.
  • comparing the geometric shape with the set reference shape, determining a target geometry that matches the reference feature comprises:
  • the method further includes:
  • the comparing the geometric shape with the set reference shape, determining the target geometry matching the reference feature comprises: setting a reference shape;
  • the reference shape includes: a circle, a rectangle, a triangle, or a star;
  • the reference features include: distance information from each point to the center point, number of vertices, angle of vertex information, and/or side length information.
  • Another aspect of the present invention provides an apparatus for image recognition, including:
  • An image acquisition module configured to acquire an image to be analyzed
  • a first identification module configured to identify a geometric shape included in the image
  • a second identification module configured to compare the geometric shape with the set reference shape, and determine a target geometry that matches the reference shape
  • a display module is configured to display the target geometry.
  • the first identification module includes:
  • a pixel point analyzing unit configured to acquire a gray value of each pixel in the image
  • a line identifying unit configured to determine adjacent pixels whose gray values differ by less than or equal to a set tolerance, and identify lines formed by the adjacent pixels
  • a shape recognition unit for identifying a closed shape formed by the line, the closed shape being determined as a geometric shape included in the image.
  • the second identification module includes:
  • a feature extraction unit configured to extract a reference feature of the reference shape, and extract feature information of the geometric shape
  • the feature comparison unit calculates the approximation degree of the feature information and the reference feature, and if the approximation is greater than or equal to the set threshold, determining that the corresponding geometry is the target geometry.
  • the method further includes: a correction module, configured to modify the target geometry according to the reference feature, so that the corrected target geometry has all reference features corresponding to the reference shape.
  • the method further includes: a setting module, configured to set a reference shape;
  • the reference shape includes: a circle, a rectangle, a triangle, or a star;
  • the reference features include: distance information from each point to the center point, number of vertices, angle of vertex information, and/or side length information.
  • the beneficial effects of implementing the above technical solution of the present invention include: identifying, based on the set reference shape, whether there is a target geometric shape matching the reference shape in the image to be analyzed, and marking the existing target geometric shape in the image; According to different reference shapes, different types of geometric shapes in the image can be identified, and the objects in the image can be more abstracted and simplified, and the students (users) can intuitively recognize the geometric figures in natural life.
  • FIG. 1 is a schematic flowchart of a method for image recognition according to an embodiment of the present invention
  • 3 is a schematic diagram of another image recognition effect
  • FIG. 4 is a schematic flowchart of a method for image recognition according to another embodiment of the present invention.
  • FIG. 5 is a schematic diagram of another image recognition effect
  • Figure 6 is a schematic diagram of an image correction effect
  • FIG. 7 is a schematic structural diagram of an apparatus for image recognition according to an embodiment of the present invention.
  • FIG. 8 is a schematic structural diagram of an apparatus for image recognition according to another embodiment of the present invention.
  • the invention provides a method for image recognition, which is especially suitable for abstracting and simplifying processing of objects in an image in the teaching field, identifying different types of geometric shapes in the image, and assisting students (users) to intuitively construct geometric figures in natural life. Cognition.
  • Embodiments of the present invention also provide corresponding apparatus for image recognition. The details are described below separately.
  • FIG. 1 is a schematic flowchart of a method for image recognition according to an embodiment of the present invention. As shown in FIG. 1, the method includes the following steps S101 to S104, which are described in detail as follows:
  • Step S101 acquiring an image to be analyzed
  • the image to be analyzed may be a locally stored image, an imported external image, or an image taken instantaneously by a camera. Preferably, only one image is acquired at a time as an image to be analyzed.
  • Step S102 identifying a geometric shape included in the image
  • the manner of identifying the geometric shape included in the image may be: first processing the image into a gray image, and acquiring gray values of each pixel in the image; determining that the gray value is less than or equal to the set capacity. Poor adjacent pixels, then identifying lines formed by the adjacent pixels; further identifying a closed shape formed by the lines, the closed shape being determined as the geometric shape contained in the image.
  • the tolerance setting has a key influence on the recognition accuracy and the recognition strength.
  • the smaller the tolerance setting the higher the recognition accuracy, but the smaller the recognition strength (that is, the possible lines cannot be recognized. Come out); the larger the tolerance setting, the lower the recognition accuracy, but the higher the recognition strength. Therefore, it is necessary to adjust the tolerance according to the actual situation.
  • Step S103 comparing the geometric shape with the set reference shape, determining a target geometry matching the reference shape
  • the specific manner of step S103 may be: firstly, extracting reference features of the set reference shape, and separately extracting feature information of the geometric shape identified by the above steps; and then separately calculating feature information of each geometric shape and the reference
  • the approximation of the feature can be expressed as a percentage. The higher the degree of approximation, the more similar the corresponding geometric shape is to the reference shape, and the more features satisfying the reference shape, when the degree of approximation is 100%, the corresponding geometric shape has all the references corresponding to the reference shape.
  • the feature conversely, the lower the degree of approximation, the greater the difference between the corresponding geometric shape and the reference shape, and the fewer features corresponding to the reference shape.
  • the set matching determination threshold if the approximation is greater than or equal to the threshold, it is determined that the corresponding geometry is the target geometry.
  • the reference shape such as a circle, a rectangle, a triangle, or a star
  • the above reference features include, but are not limited to, distance information from each point to the center point, the number of vertices, the angle of the vertex, and / or side length information.
  • a circular reference feature includes lines that are smooth and continuous, and each point-to-center distance is the same;
  • a reference feature of a triangle includes a closed figure composed of three sides and three vertices;
  • a rectangular reference feature includes four sides, four vertices, And all the apex angles are right angles and so on.
  • Step S104 displaying the target geometry.
  • a specific color may be filled in a corresponding area of the target geometric shape in the image, or a new layer may be created on the image, and a corresponding geometric line drawing may be drawn on the newly created layer, that is, in the layer.
  • the position of the geometric line map is aligned with the position of the corresponding target geometry in the image, and then the new layer is derived to clearly show the target geometry contained in the image.
  • the image to be analyzed is a photo of a bicycle
  • the reference shape is set to a triangle
  • the feature information of the triangle is extracted, for example, three vertices, and the three vertices are not on the same straight line, and the sum of the vertices is 180 degrees, the sum of the lengths of any two sides is greater than the third side, and the like. It then begins to identify the lines in the bicycle image, resulting in a closed shape of lines, determining whether the features satisfy the characteristics of the triangle, and if so, marking the triangle at the corresponding position in the bicycle image.
  • the tripod of the bicycle body and the triangle formed by the spokes in the wheel are identified (all triangles are not shown in the figure, just an example), or the identified triangles may be derived, and the identified multiples will be identified.
  • the triangles are displayed separately.
  • the image to be analyzed is a photograph of a bicycle, and the reference shape is reset to a circular shape, and the circular features can be extracted: a continuous smooth closed curve, and the distance from each point to the center point is equal. Start recognition, it can be recognized that the two wheels of the bicycle are round, and then the indicator display, or two rounds Export is displayed separately.
  • FIG. 4 is a schematic flowchart of a method for image recognition according to another embodiment of the present invention. As shown in FIG. 4, the method includes:
  • Step S201 acquiring an image to be analyzed
  • Step S202 setting a reference shape
  • Step S203 identifying a geometric shape included in the image
  • Step S204 determining whether the approximate degree of the geometric shape and the reference shape meets the set condition. If yes, go to the next step, otherwise, go back to the previous step.
  • the setting condition is a threshold of the set approximation, and the threshold is less than 100%, for example, 80%;
  • Step S205 displaying the identified target geometry in the image.
  • a transparent layer is newly created on the image, and a corresponding geometric line drawing is drawn on the transparent layer, that is, the position of the geometric line drawing in the transparent layer and the corresponding target geometric shape in the image.
  • the positions are aligned to clearly show the target geometry contained in the image.
  • Step S206 does the identified target geometry have all the reference features corresponding to the reference shape? If yes, go to step S208, if no, go to the next step.
  • Step S207 correcting the target geometry according to the reference feature, so that the corrected target geometry has all the reference features corresponding to the reference shape;
  • a certain fault tolerance range may be set, that is, the threshold is set to be less than 100%, and the approximate degree is greater than or equal to
  • the geometry of the threshold can be confirmed as the target geometry.
  • the ellipse can also be recognized as a circle, the trapezoid is also recognized as the target geometry of the rectangular reference shape, or the diamond is also recognized as the target geometry of the square reference shape; in addition, if there are some unexpected protrusions, turns in the line If it is not smooth, it can also be recognized as a smooth line.
  • step S208 the corrected geometry is derived and displayed.
  • the image to be analyzed is an image of a fence, and the reference shape is set to a rectangle.
  • the two quadrilaterals shown in the figure can be identified through the above steps 201-204. However, the two quadrilaterals are not standard rectangles, and after the quadrilateral is subjected to a certain correction/deformation by the step S207, the four apex angles of the quadrilateral are at right angles, and the standard rectangle can be changed. At the same time, the correction process can be dynamically displayed to further help students understand the deformation between various geometric shapes.
  • correction/deformation rule is not arbitrary, but based on the reference feature information of the reference shape, and follows the regular change of the depth of field transformation in the three-dimensional figure.
  • the circular shape is set as the reference shape, and the graphic on the left side is a geometric shape close to a circle, because although one of the points is recessed inward, most of the points satisfy the characteristics of the circle, and a certain fault tolerance.
  • This graphic can be recognized as a circle within the range.
  • the standard circular shape information may be modified according to the standard circular information, including removing the inwardly recessed points in the left geometry, connecting the notched portions so as to satisfy all the features of the standard circle, and correcting the right
  • the side is regular in a circle.
  • the set reference shape based on the set reference shape (geometry)
  • different reference shapes different geometric shapes in the image can be identified, and the objects in the image can be more abstracted and simplified, which is convenient for the students (users) to intuitively recognize the geometric figures in natural life.
  • the characteristics of the graphics it can be automatically corrected to a more standard graphics, which is conducive to students to distinguish and contrast different geometric shapes.
  • FIG. 7 is a schematic structural diagram of an apparatus for image recognition according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of an apparatus for image recognition according to an embodiment of the present invention.
  • Only parts related to the embodiment of the present invention are shown in the figure, and those skilled in the art can understand the structure of the apparatus shown in the figure.
  • FIG. 7 is a schematic structural diagram of an apparatus for image recognition according to an embodiment of the present invention. As shown in Figure 7, the device comprises:
  • An image obtaining module 710 configured to acquire an image to be analyzed
  • a first identification module 720 configured to identify a geometric shape included in the image
  • a second identification module 730 configured to compare the geometric shape with the set reference shape, and determine a target geometry that matches the reference shape
  • the display module 740 is configured to display the target geometry.
  • the apparatus for image recognition of the embodiment further includes a setting module 750, configured to set a reference shape;
  • the above reference shapes include, but are not limited to, a circle, a rectangle, a triangle, or a star; the above reference features include, but are not limited to, distance information from each point to a center point, number of vertices, angle of vertex information, and/or side length information.
  • the first identification module 720 illustrated in FIG. 7 may specifically include:
  • a pixel point analyzing unit 721, configured to acquire a gray value of each pixel in the image
  • a line identifying unit 722 configured to determine adjacent pixel points whose gray value differences are less than or equal to a set tolerance, and identify a line formed by the adjacent pixel points;
  • the shape recognition unit 723 is configured to identify a closed shape formed by the line, and determine the closed shape as a geometric shape included in the image.
  • the second identification module 730 illustrated in FIG. 7 may specifically include:
  • a feature extraction unit 731 configured to extract a reference feature of the reference shape, and extract feature information of the geometric shape
  • the feature comparison unit 732 calculates the approximation degree of the feature information and the reference feature, and if the approximation is greater than or equal to the set threshold, determines that the corresponding geometry is the target geometry.
  • FIG. 8 is a schematic structural diagram of an apparatus for image recognition according to another embodiment of the present invention.
  • the apparatus for image recognition of the embodiment further includes a correction module 760, configured to correct the target geometry according to the reference feature, so that the corrected target geometry has the reference shape corresponding to All reference features.
  • each functional module is merely an example, and may be used in an actual application, for example, according to configuration requirements of the corresponding hardware or software.
  • the above-mentioned function allocation is completed by different functional modules, that is, the internal structure of the device for image recognition is divided into different functional modules to complete all or part of the functions described above.
  • the user can set a reference shape (geometry), and the apparatus based on the image recognition can automatically recognize whether there is a target geometry matching the reference shape in the input image, and
  • the existing target geometry is marked in the image; according to different reference shapes, different geometric shapes in the image can be identified, and the objects in the image are more abstracted and simplified, which is beneficial to the students (users) to nature.
  • the geometry in life has an intuitive perception.
  • any embodiment of the present invention can be accomplished by the program instructing the associated hardware (personal computer, server, or network device, etc.).
  • the program can be stored in a computer readable storage medium.
  • the program when executed, may perform all or part of the steps of the method specified in any of the above embodiments.
  • the foregoing storage medium may include any medium that can store program code, such as a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé et un appareil d'identification d'image. Le procédé comprend les étapes consistant : à acquérir une image à analyser (S101, S201) ; à identifier une forme géométrique comprise dans l'image (S102, S203) ; à comparer la forme géométrique avec une forme de référence définie, et à déterminer une forme géométrique cible correspondant à la forme de référence (S103) ; à marquer et à afficher la forme géométrique cible (S104). La solution permet d'identifier différentes formes géométriques dans une image selon différentes formes de référence, d'extraire et de simplifier de manière plus souple un objet dans l'image, et d'aider des étudiants à reconnaître de manière intuitive des figures géométriques dans la vie naturelle.
PCT/CN2016/090712 2015-08-24 2016-07-20 Procédé et appareil d'identification d'image Ceased WO2017032190A1 (fr)

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CN114037639A (zh) * 2021-11-26 2022-02-11 天翼数字生活科技有限公司 一种几何图像识别方法、装置、设备及可读存储介质

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CN114037639A (zh) * 2021-11-26 2022-02-11 天翼数字生活科技有限公司 一种几何图像识别方法、装置、设备及可读存储介质

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