WO2014141881A1 - 特徴点集合間の対応付け方法、対応付け装置ならびに対応付けプログラム - Google Patents

特徴点集合間の対応付け方法、対応付け装置ならびに対応付けプログラム Download PDF

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WO2014141881A1
WO2014141881A1 PCT/JP2014/054642 JP2014054642W WO2014141881A1 WO 2014141881 A1 WO2014141881 A1 WO 2014141881A1 JP 2014054642 W JP2014054642 W JP 2014054642W WO 2014141881 A1 WO2014141881 A1 WO 2014141881A1
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coordinate
feature points
order
value
feature
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French (fr)
Japanese (ja)
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井戸伸彦
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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
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/754—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries involving a deformation of the sample pattern or of the reference pattern; Elastic matching
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
    • G06V30/10—Character recognition
    • G06V30/28—Character recognition specially adapted to the type of the alphabet, e.g. Latin alphabet
    • G06V30/287—Character recognition specially adapted to the type of the alphabet, e.g. Latin alphabet of Kanji, Hiragana or Katakana characters

Definitions

  • the present invention compares two objects, which are sets of feature points having coordinates composed of N coordinate values in N (N is a natural number of 2 or more) dimensional space, and compares the feature points in one set with the other.
  • the present invention relates to an associating method, an associating device, and an associating program for determining correspondence with feature points in the set.
  • a feature point here has a coordinate which consists of N coordinate values corresponding to N-dimensional space, and points out what is an element of an object.
  • a feature point may have an attribute value other than coordinates.
  • a handwritten input character expressed by a stroke on a two-dimensional plane and a template character referred to for comparison with the object may be an object, and the start point and the end point of each stroke may be considered as feature points. I can do it.
  • the feature point association in this case is applied in the field of on-line character recognition, as can be seen in Patent Document 1.
  • an audio signal expressed on a two-dimensional plane of a spectrum with two axes of frequency and intensity can be used as an object, and a point on the path can be considered as a feature point.
  • the correspondence between the feature points of the two audio signals is applied in the field of speech recognition, as seen in Patent Document 2.
  • Non-Patent Document 1 Any of the above application fields are general technical fields described in textbooks (Non-Patent Document 1).
  • the association of two feature points in these applications means that the two feature points have the same mutual positional relationship on the two-dimensional plane with other feature points in each object. . That is, since the association is performed for other feature points, the correspondence can be established as a whole only after the mutual positional relationship in each object is the same.
  • Non-Patent Document 4 a case is considered in which the Euclidean distance obtained from the coordinates of the two feature points in the N-dimensional space is included in the cost in DP matching. If the Euclidean distance is large, the cost increases, and it becomes negative to associate two feature points. That is, it means that the difference between the two feature points is large in the mutual positional relationship with other feature points in each object. Conversely, if the Euclidean distance is small, it means that there is little difference in the mutual positional relationship between two feature points. As described above, in the correspondence such as DP matching, the distance between the feature points obtained by the coordinates in the N-dimensional space is used as a measure of the difference in the mutual positional relationship between the feature points in the object. Can be interpreted.
  • Non-Patent Document 3 (Section 5.1) describes that “normalization” is performed on character data.
  • Non-Patent Document 1 (Section 2.4.2) also describes that parallel movement and enlargement / reduction processing are used as processing for matching.
  • the object to be associated includes a variation that is a target of processing generally called normalization.
  • the Euclidean distance between the feature points is usually calculated after normalization.
  • the present invention converts a coordinate system that includes coordinate values including variations due to distortion and variations to be normalized into a new coordinate system that represents the essence of the mutual positional relationship, and converts the distance on the new coordinate system. It is an object to provide a method, an apparatus, and a program for associating feature points used.
  • the first configuration of the feature point associating method according to the present invention is an object having two or more feature points having coordinates composed of N coordinate values in an N (N is a natural number of 2 or more) dimensional space.
  • N is a natural number of 2 or more
  • the cost between the feature points to be matched is determined and in the feature point association method for determining the association so as to reduce the total value of the costs between the attached feature points, two or more non-parallel coordinate axes on the N-dimensional space are defined, and the plurality of coordinate axes For each coordinate axis, a coordinate value on the coordinate axis of the feature point that is an element of the first object is obtained, and the feature points are sorted in ascending or descending order according to the obtained coordinate value.
  • the second order coordinate calculation step of calculating the order coordinate value of the feature point that is an element of the object, two feature points of the feature point of the first object and the feature point of the second object A cost calculating step for determining the cost between feature points by including a calculated value that monotonously increases with respect to a difference between the order coordinate values of the two feature points on each coordinate axis of the plurality of coordinate axes; and And determining the association using the cost determined in the cost calculating step.
  • the first effect is that the order coordinate values are maintained at the same value even if the coordinate values slightly change due to fluctuations due to distortion.
  • the second effect is that the order coordinate values on the remaining coordinate axes are maintained even if the order coordinate values may fluctuate on some coordinate axes of the plurality of coordinate axes. It is that the fluctuation
  • the first ordered coordinate calculation step and the second ordered coordinate calculation step include the ordered sequence of the feature points.
  • a value obtained by converting the integer value by a monotone function is used as the order coordinate value.
  • the object in the first or second configuration, is a character expressed as one or more strokes on a two-dimensional plane.
  • the coordinates of the feature point are composed of coordinate values of the coordinates of one or more points on the two-dimensional plane representing the position of the stroke.
  • the first configuration of the feature point associating device is an object having two or more feature points having coordinates composed of N coordinate values in N (N is a natural number of 2 or more) dimensional space.
  • N is a natural number of 2 or more
  • the cost between the feature points to be matched is determined and in the feature point associating apparatus for determining the correspondence so as to reduce the total value of the costs between the attached feature points, two or more non-parallel coordinate axes on the N-dimensional space are defined, and the multiple coordinate axes For each coordinate axis, a coordinate value on the coordinate axis of the feature point that is an element of the first object is obtained, and the feature points are sorted in ascending or descending order according to the obtained coordinate value.
  • First order coordinate calculation means for obtaining an order sequence of feature points, and calculating an integer value indicating an appearance order of the feature points in the order sequence as an order coordinate value on the coordinate axis of the feature points;
  • the second order coordinate calculation means for calculating the order coordinate value of the feature point that is an element of the object, two feature points of the feature point of the first object and the feature point of the second object.
  • the cost calculation means for determining the cost between feature points by including a calculated value that monotonically increases with respect to a difference between the order coordinate values of the two feature points on each coordinate axis of the plurality of coordinate axes; and And means for determining correspondence using the cost determined by the cost calculation means.
  • the first ordered coordinate calculating means and the second ordered coordinate calculating means may be configured such that the ordered sequence of the feature points.
  • a value obtained by converting the integer value by a monotone function is used as the order coordinate value.
  • the object is a character expressed as one or more strokes on a two-dimensional plane
  • the coordinates of the feature point are composed of coordinate values of coordinates of one or more points on a two-dimensional plane representing the position of the stroke.
  • the configuration of the program according to the present invention is characterized by causing a computer to execute the feature point association method according to the first to third configurations.
  • FIG. 1 is a conceptual configuration diagram of a writing test apparatus according to an embodiment of the present invention. It is an operation example of a writing test apparatus.
  • N is used as the number of feature points of the first object unless otherwise specified.
  • FIG. 1 (b) is a conceptual block diagram of the feature point matching apparatus (100) according to the embodiment of the present invention, and exchanged data is also shown in the figure.
  • the feature point associating device (100) is composed of an object input means (110), a cost calculation means (120), and an association determination means (130).
  • the cost calculating means (120) is composed of a distance cost calculating means (121) and another cost calculating means (125), and the distance cost calculating means (121) is an order coordinate value calculating means (122) and a distance calculating means. (123) and coordinate axis definition holding means (124).
  • the object input means (110) inputs a first object and a second object whose elements are feature points to be matched. For example, a handwritten input character represented by a stroke and a character referred to as a correct answer correspond to this.
  • equation (2) is given.
  • the order coordinate value calculation means (122) inputs a plurality of coordinate axis definitions (195) from the coordinate axis definition holding means (124), and calculates the order coordinates (192) of the feature points.
  • a general multiple coordinate axis definition (195) is shown in equation (3).
  • Formula (4) shows a numerical example of formula (3).
  • the f 0 is the x-axis, coordinate axes of which was rotated counterclockwise by [pi / 4, and has a f 1, f 2, f 3 . Which coordinate axis should be defined depends on the application field.
  • Formula (6) shows a numerical example of Formula (5). This value will be described with reference to FIG.
  • FIG. 2 is a diagram illustrating a plurality of coordinate axis trains of Equation (4) and feature points of Equation (2) on a secondary plane as numerical examples. For example, when viewed in the direction of the vector f 1 in FIG. 2, the feature points are arranged in the order of p 1 , p 2 , p 0 , p 3 . This is the meaning of O f1 (P) in formula (6).
  • Equation (7) When the order of pn counted from 0 in the feature point O fk (P) is represented as O fk (p n ), it can be expressed as in Expression (7). However, “[..]” in the expression (7) takes a value of “1” when “..” is true and “0” when it is false. Equation (5) does not clearly indicate how to determine the order when the coordinate values are the same, but in equation (7), the second term on the right side follows the subscript of the variable of the feature point. As such a tie-breaking method, there are other methods such as (a) following the order when the coordinate axes are slightly rotated, and (b) treating them in the same order. Unless there are many arrangements in which three or more points are arranged on the same straight line, there is no problem with the method of equation (7).
  • the order coordinates (192) in FIG. 1 are defined as in Expression (8) using Ofk (p n ) in Expression (7).
  • the order coordinate as shown in Expression (8) is calculated by the order coordinate value calculation means (122) for each of the first object and the second object.
  • Equation (9) shows a numerical example of equation (8).
  • the value ( 0, 2, 3, 3 ) of the value of O F (p 0 ) is “0” when the order of p 0 in the expression (6) is O f0 (P), and “0” when O f1 (P) is “ 2 ”, O f2 (P) is“ 3 ”, and O f3 (P) is“ 3 ”.
  • the distance calculation means (123) that receives the order coordinates (192) of the feature points of the first and second objects calculates the cost (193) between the feature points using this.
  • the calculation method various calculation methods based on the difference between the order coordinate values may be used.
  • the cost d O is obtained by taking “the sum of the differences in the order coordinate values” shown in Expression (10).
  • a method using a calculation method such as “geometric mean of difference in order coordinate value” or “square of sum of squares of difference in order coordinate value” is also conceivable. As long as the difference in order coordinates increases, the cost increases. That is, any calculation value that monotonously increases with respect to the difference in order coordinates may be used.
  • P corresponds to the first object
  • Q corresponds to the second object.
  • the cost d 0 (p based on the distance between the feature point p 1 in the first object and the point q 2 in the second object is considered. 1 , q 2 ) is as shown in Equation (12). Furthermore, the costs ⁇ d O (p n , q m ) ⁇ for all combinations of feature points in the first object and points in the second object are as shown in Table 1.
  • the number of feature points is the same between the first and second objects.
  • Equation (13) A complicated shape can be associated with a simplified shape.
  • the reason why the denominator of Expression (13) is set to N ⁇ 1 instead of N is to make the lowest order “1”. That is, the normalized order coordinate value is not an integer value but a rational number in the closed interval [0, 1]. Even when normalized, the definition of the ordinal distance follows the equation (10).
  • the normalized order coordinate value is obtained by a linear function with respect to the rank O fk (p n ).
  • the function to be used is not necessarily linear, and may be a monotone function.
  • the function of Expression (14) is monotonous and may be used.
  • Expression (14) is used, the difference in the ranking is sensitively reflected in the normalized order coordinate value near the center, and the degree of reflection is reduced as the position approaches the highest or lowest position. It is reflected in the same way.
  • equations (13) and (14) are both monotonically increasing functions. There is no problem with using a monotonically decreasing function, but it is meaningless because the order is reversed.
  • the association determining means (130) inputs the cost including the cost (193) ⁇ d O (p n , q m ) ⁇ between the feature points according to the equation (10) as in the numerical example of Table 1. Then, the association (194) is determined.
  • the feature of the present invention is that the order coordinate value is used for calculating the cost, and the procedure used in the association determining means (130) does not have to be specific. In the present embodiment, a simple association procedure is adopted in which association is performed in order from the lowest cost.
  • the smallest d O (p n ′ , q m ′ ) is selected from the costs ⁇ d O (p n , q m ) ⁇ in which both pn and q m are not associated, and this p
  • the procedure of repeating associating n ′ and q m ′ until all feature points are associated is adopted. This procedure of associating in order from the lowest cost does not necessarily obtain the optimum association that minimizes the total cost. However, since the cost based on the order coordinate values works effectively, accurate association can be obtained even using such a simple association procedure.
  • association determining means (130) performs association according to the cost of the numerical example of Table 1 using the procedure of associating in ascending order from the above-mentioned lowest cost, the association (indicated by a circle in Table 1) 194) is obtained.
  • the procedure of associating in ascending order of cost is used on the premise that one-to-one correspondence is performed.
  • the present invention is not necessarily limited to only one-to-one correspondence. Absent. For example, when a complex shape is associated with a simplified shape by performing normalization as in Expression (13), the correspondence is one-to-many.
  • FIG. 3 is a conceptual configuration diagram of the writing test apparatus (300) according to the embodiment of the present invention, and exchanged data is also shown in the drawing.
  • the correlating device (100) shown in FIG. 1 is used by the writing test device (300) from the outside, but for the sake of consistency with FIG. Of course, the form corresponding to the function of the associating device (100) may be incorporated in the writing test device (300).
  • the writing test apparatus (300) includes a position input means (320) and a display means (330) in the touch panel (310), a handwriting input control means (340), a writing problem management means (350), and a scoring means (360).
  • the scoring means (360) is composed of a midpoint calculating means (361), an angle calculating means (362), and a correctness determination means (363).
  • the writing problem management means (350) outputs a writing question (391) to the display means (330), and the user (399) visually recognizes it (392), and position input means (320). Input (393) by drawing is performed.
  • the position input means (320) outputs the coordinate information (394) of the drawing input to the handwriting input control means (340).
  • the handwriting input control means (340) displays the character image (395) by the display means (330), and outputs the stroke information (396) of the handwritten input characters to the scoring means (360) when the drawing input ends and scoring starts. To do.
  • the midpoint calculating means (361) is applied to the stroke information (396) of the handwritten input characters and the stroke information (397) of the correct answer characters input from the writing problem management means (350). And angle calculation means (362).
  • the midpoint calculation means (361) obtains the midpoint of each stroke in the stroke information.
  • the coordinate value of the feature point that is, the value of Expression (1) is obtained.
  • the angle calculation means (362) obtains the angle with respect to the x-axis at the start point and end point of each stroke in the stroke information. This is used to calculate the cost in the other cost calculation means (125) in FIG. 1 when input to the associating device (100).
  • other costs are also added as necessary to be input to the association means (130).
  • the angle with respect to the x-axis at the start point and end point is an example. In this embodiment, since only the midpoint of the stroke obtained by the midpoint calculating means (361) cannot be distinguished from the first and second strokes of the “10” character, for example, this is another cost. Costs due to different angles are required. Formula (15) gives the definition of cost d S by angle.
  • the stroke information of the correct character As a first object, stroke information of handwritten input characters is input as a second object.
  • the association is performed by the processing described with reference to FIG. 1, and the value d of Expression (16) obtained by adding Expression (10) and Expression (15) is used as the cost at this time.
  • the associating (194) is output to the correctness determination means (363) in the scoring means (360).
  • the correctness / incorrectness determination means compares the associated strokes or the relationship between the strokes to determine whether the correct answer characters and the handwritten input characters match at a certain level or more, and determines correct / incorrect answers.
  • the result (398) is output to the display means (330).
  • a character composed of a stroke is used as an object, and the order coordinate value is calculated using the coordinates on the plane of the midpoint of the stroke that is the feature point.
  • the order coordinate value may be calculated in the four-dimensional space (xs, ys, xe, ye) using the xy coordinates of the start point (xs, ys) and the end point (xe, ye) of the stroke.
  • the coordinates of the feature points may be composed of coordinate values on the two-dimensional plane representing the stroke position. That is, it is not necessary to use only the coordinate value of a single point on the two-dimensional plane as in the case of using the coordinate value of the midpoint of the stroke. Even when the coordinate value of a single point on the two-dimensional plane is used, it does not have to be a middle point.
  • FIG. 4 shows an operation example of the writing test apparatus described in FIG.
  • a correct answer character image (401) and a handwritten input character image (402) are characters handwritten on a writing test apparatus equipped with an actual touch panel. Numbers are given in the order of strokes, and the numbers are associated with each other as an index.
  • the path (SVG) (411) of the stroke of the correct answer is the path information corresponding to 401, written in the notation of SVG (Scalable Vector Graph) adopted in HTML5.
  • 412 is path information 402.
  • the curve is expressed by a quadratic Bezier curve.
  • the y-axis is directed toward the bottom of the character and is opposite to a general xy coordinate plane.
  • the coordinate values of the path information 411 and 412 and the coordinate values and order coordinate values appearing in the following description follow the y-axis direction of the SVG.
  • the coordinates of the midpoint of the stroke and the ordinal coordinates (420) are defined as the coordinates of the midpoint of each stroke (image) extracted from the path information of 411 and 412, and these are defined by equation (8) and the like. It is the value converted to ordinal coordinates. Note that the order coordinates in FIG. 4 indicate values that are not normalized. An approximate value is used for the coordinate value of the midpoint for speeding up the processing.
  • the order distance (430) between strokes in FIG. 4 is a table showing the result of obtaining the order distance according to the equation (10) with respect to the value of 420.
  • the stroke of the correct answer character corresponding to 401 is written in the vertical direction of the table, and the stroke of the handwritten input character corresponding to 402 is written in the horizontal direction.
  • a combination of images that are associated with the value of 430 using the procedure for associating in order from the lowest cost is indicated by a circle in the table.
  • the costs shown in Expression (15) are added together to perform association, but Expression (15) is omitted because it is not directly related to the present invention.
  • the writing test apparatus using handwritten characters is dealt with.
  • the associating apparatus according to the present invention can be used in a form similar to online character recognition.
  • various objects such as images, audio signals, and sentences can be applied as objects.

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PCT/JP2014/054642 2013-03-11 2014-02-26 特徴点集合間の対応付け方法、対応付け装置ならびに対応付けプログラム Ceased WO2014141881A1 (ja)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05250518A (ja) * 1992-03-10 1993-09-28 Hitachi Ltd 文字認識方法
JP2003018557A (ja) * 2001-07-04 2003-01-17 Monolith Co Ltd 画像処理方法および装置
JP2003514305A (ja) * 1999-11-12 2003-04-15 ゴー・センサーズ・エルエルシー マシン・ビジョンのための堅牢なランドマークと前記ランドマークを検出するための方法
JP2008171140A (ja) * 2007-01-10 2008-07-24 Omron Corp 画像処理装置および方法、並びに、プログラム
JP2010027025A (ja) * 2008-06-20 2010-02-04 Sony Corp 物体認識装置、物体認識方法及び物体認識方法のプログラム

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Publication number Priority date Publication date Assignee Title
JP3871061B2 (ja) * 2003-03-25 2007-01-24 セイコーエプソン株式会社 画像処理システム、プロジェクタ、プログラム、情報記憶媒体および画像処理方法
KR20080040930A (ko) * 2006-11-06 2008-05-09 삼성전자주식회사 컴퓨터 시스템 및 그 제어방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05250518A (ja) * 1992-03-10 1993-09-28 Hitachi Ltd 文字認識方法
JP2003514305A (ja) * 1999-11-12 2003-04-15 ゴー・センサーズ・エルエルシー マシン・ビジョンのための堅牢なランドマークと前記ランドマークを検出するための方法
JP2003018557A (ja) * 2001-07-04 2003-01-17 Monolith Co Ltd 画像処理方法および装置
JP2008171140A (ja) * 2007-01-10 2008-07-24 Omron Corp 画像処理装置および方法、並びに、プログラム
JP2010027025A (ja) * 2008-06-20 2010-02-04 Sony Corp 物体認識装置、物体認識方法及び物体認識方法のプログラム

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