JPS5957384A - Recognition of musical notation - Google Patents

Recognition of musical notation

Info

Publication number
JPS5957384A
JPS5957384A JP57168153A JP16815382A JPS5957384A JP S5957384 A JPS5957384 A JP S5957384A JP 57168153 A JP57168153 A JP 57168153A JP 16815382 A JP16815382 A JP 16815382A JP S5957384 A JPS5957384 A JP S5957384A
Authority
JP
Japan
Prior art keywords
dictionary
matching
notation
data
recognition
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.)
Granted
Application number
JP57168153A
Other languages
Japanese (ja)
Other versions
JPH0259509B2 (en
Inventor
Makoto Nagao
真 長尾
Masa Saito
斉藤　雅
Akira Toda
明 戸田
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.)
Dai Nippon Printing Co Ltd
Original Assignee
Dai Nippon Printing 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 Dai Nippon Printing Co Ltd filed Critical Dai Nippon Printing Co Ltd
Priority to JP57168153A priority Critical patent/JPS5957384A/en
Publication of JPS5957384A publication Critical patent/JPS5957384A/en
Publication of JPH0259509B2 publication Critical patent/JPH0259509B2/ja
Granted legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR 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
    • G06V30/30—Character recognition based on the type of data
    • G06V30/304—Music notations

Abstract

PURPOSE:To enable the sure recognition of a musical notation by preparing two dictionaries consisting of data of 1, and -1, and 1 and 0 for an object notation, and extracting the object notation for matching from the object area of recognition. CONSTITUTION:When matching a picture A with a notation B registered in a dictionary, parts in different shape are conspicuous in P and Q. A dictionary (f) whose data makes picture part 1 and other parts 0 and a dictionary f' whose data makes picture part 1 and surrounding part -1, are prepared. A black part of the object picture is made 1 and a white part is made 0 and the total of black 1 is made l. Using a dictionary of fXg=(b) for the pictur A, and calculating resemblance degree Sfg=b/lX100%, by using f'Xg=c for others, for instance such musical notations as (b), notation B etc., and calculating Sfg=c/ lX100%, sure recognition of notations is made possible.

Description

【発明の詳細な説明】 この発明は、楽譜の１，１ｌｌＩ等の記号を２次元マツ
チング法で認識するための方法に関する。DETAILED DESCRIPTION OF THE INVENTION The present invention relates to a method for recognizing symbols such as 1 and 1llI in a musical score using a two-dimensional matching method.

この発明では、第１図に示すような五線等を消去した画
像エリア１からマツチングを行なうための対象物（この
例ではｒ＞を切出イための切出し範囲２を定め、切出し
範囲２で定められた切出し画像をマツチングを行なうエ
リア３に移動する。In this invention, a cropping range 2 is defined for cropping an object for matching (in this example, r The determined cutout image is moved to area 3 where matching is to be performed.

なお、このマツチングエリア３Ｉｉ切出し画像よりも少
し大きくなっている。そして、別に予め用意しておいた
マツチング用の辞書を、マツチングエリア３内で動かし
て両者のマツチングを行なうよ　　。Note that this matching area 3Ii is slightly larger than the cutout image. Then, a matching dictionary prepared separately in advance is moved within the matching area 3 to match the two.

うにするのであ７Ｉ０ ここにおいて、第２図（Ａ）に示す画像（＃）を、辞書
に登録されている同図（１１）に示すような記号（埼）
とマツチングを行なう場合、その形状の異なる部分はＰ
及びＱにおいて顕著である。そして、両津に関しては第
３図（Ａ）に示すように画像部分を１１“とし、その他
の領域を１０”と−４−＾と共に、辞書には第３図中）
に示すような肖像部分を％　１　Ｎとして周囲を全て％
□ｌとするデータと、同図（Ｃ）　Ｋ示すように画像部
分を１１′として周囲を全て１−１＃とするデータとを
用意しておき、第２［１ＶＩＡｌのｌ’　ｉｆ！Ｓ分と
同図（Ｉ３）のＱ　？４Ｕ分とを陥理演１１により比較
するようにしている。すなわち、第３図（Ａ）に示すＰ
ｌ、　Ｐ２　と同図（１３）に示″ｔＱｓ　、　Ｑ３及
び同図（（、’）Ｋ示すＱ２　、　Ｑ４との画素データ
毎の論理積を求め、後述する類似度からその積値を減算
するようにして類似程度を判断するようにしていイ）。7I0 Here, the image (#) shown in Fig. 2 (A) is replaced with the symbol (Sait) shown in Fig. 2 (11) registered in the dictionary.
When matching is performed, the parts with different shapes are P
and Q. Regarding Ryotsu, as shown in Figure 3 (A), the image part is set to 11", and the other areas are 10" and -4-^ (in Figure 3) in the dictionary.
The portrait part as shown in is % 1 N, and the surrounding area is %
□Prepare the data for l' and the data for the image part to be 11' and the surrounding area to be 1-1# as shown in Figure (C) K. S minute and Q in the same figure (I3)? I am trying to compare it with the 4U portion using trick theory 11. That is, P shown in FIG. 3(A)
1, P2 and "tQs, Q3 shown in (13) in the same figure, and Q2, Q4 shown in ((,')K in the same figure for each pixel data are calculated, and the product value is subtracted from the degree of similarity described later. The degree of similarity is determined in this way.

つまり、第３図囚と（Ｂ）との場合には、ＰＩ　ＸＱＩ
　＋Ｐ２　ｘＱａ＝０であるから類イυ度に影響はない
。これに対して、第３図（Ａ）と（（こ）との場合にｉ
、’ｒ、　、　　ＰＩ　ＸＱ２　＋Ｐ２　ＸＱ４＝−６
であるから類似度から［６Ｊを減算し、その減算値を類
似度とする。したがって、前者の場合には画像が辞得よ
り太き（ても知什ｊ度には変化がな（、形状の違いが評
価されない。これに対し、後者の場合には画像が大きい
とその分だけ類似度から減算されるので、辞劉との違い
が強調されて評価されることにｔ「る。In other words, in the case of the prisoner in Figure 3 and (B), PI
Since +P2 xQa=0, there is no effect on the degree of similarity. On the other hand, in the case of Fig. 3(A) and (((this)), i
, 'r, , PI XQ2 +P2 XQ4=-6
Therefore, [6J is subtracted from the similarity, and the subtracted value is taken as the similarity. Therefore, in the former case, the image is thicker than the size (even if there is no change in the degree of familiarity), the difference in shape is not evaluated.On the other hand, in the latter case, if the image is larger, Since the difference from Ji Liu is subtracted from the similarity, the difference from Ji Liu will be emphasized and evaluated.

ここで、第３図（Ａ）の画像をｇとし、第３図（均に示
すような′１′及び１０″のデータの記憶で成る辞書な
ｆとし、同図（（−’）に示すような１１′′及び′−
１′　のデータで成る辞書をｆ′とした場合、ｆＸｇに
よる汁のマツチング方式では、丑と科で高い類似度を得
、竹と１では指状の差力槽わ」する。Here, the image in Fig. 3 (A) is g, the dictionary consisting of the memory of data ``1'' and 10'' as shown in Fig. 11'' and '-
If f' is a dictionary consisting of data of 1', then in the soup matching method using fXg, a high degree of similarity is obtained between the ox and the family, and a finger-like differential force is obtained between the bamboo and 1.

これにより吐と明の区別ができろ。これに対ｔ７、ｆ’
Ｘｇのマツチング方式では、件と壮において画像ｇのず
れに敏感とｆｃす、類イリ度は低く、科と町の類似度と
同じになり、址と１の区別が［７にくい。This allows you to distinguish between tō and ming. On the other hand, t7, f'
In the matching method of Xg, it is sensitive to the deviation of the image g in case and place, and the degree of similarity is low, the degree of similarity is the same as that of family and town, and it is difficult to distinguish between place and place.

また、−のマツチングにおいて、ｆＸｇのマツチング方
式では―と什で−は壮に重なってしまい、類似度が高（
なり、１と−の類似度と同じになり、せと−の区別がで
きない。こＪｌに対して、ｆ’Ｘｇのマツチング方式に
おいては、―と丑で形状の違いが強調されてｄ、、ｃ４
Ｗり度が小さくなるので、＃と時の区別ができるので、
ｔＩ）る。In addition, when matching -, in fXg's matching method, - and - overlap significantly, and the degree of similarity is high (
The degree of similarity is the same as that between 1 and -, and it is impossible to distinguish between set and -. For this Jl, in the matching method of f'Xg, the difference in shape is emphasized between - and ox, and d, , c4
Since the degree of W becomes smaller, it is possible to distinguish between # and hour.
tI).

この発明はこのような観点からなさねたものであり、辞
書にゝゝＩＮ及び１０′′のデータと、′１“及び％　
　１＃のデータとを予め２種類用意しておき、画像デー
タと辞書のデータとを比較すること１１Ｃより、セと目
との誤判断がないような記号の認識が可能となる。This invention was made from this point of view, and the dictionary contains the data ``IN'' and 10'', and the data ``1'' and %.
By preparing two types of data 1# in advance and comparing the image data with the dictionary data 11C, symbols can be recognized without misjudgment between the center and the eyes.

次に、この発明による類似贋のｌｑ方力法）呪制する。Next, the method of similar counterfeiting according to this invention is controlled.

第４図に示すように対象画像ｇの黒の部分を１１“とし
、白のｇｌｔ（Ｊヲ’　ｏ　’　トＬ、黒（％　１　Ｎ
　＞の総計を１とすると、ｈ［！号沖についてはｆＸｇ
＝ｂ　　　　　　　曲曲曲（１）の辞書を用い、類似度
Ｓｆｇを Ｓｆｇ＝７−Ｘ　１００　［％コ　　　　　川・曲間　
（２）で求める。そして、記号妙以外の記号、たとえば
す、Ｈ等の楽譜Ｈｒ４号に関しては ｆ’Ｘｇ＝ｃ　　　　　　曲曲曲（３）を用い、その類
イ１′）度Ｓｆｇを Ｓｆｇ　＝　−Ｘ　ＩＯ（＋　［％］　　曲曲曲（４）
皿 なる計算で求めるようにしている。なお、上記画像と辞
書とのマツチングは構成画累毎に行なうようにしている
ので、結局上記（１）及び（３）式は画素データをＧ（
ｉ、ｊ）及びＦ（’　ｅ　Ｊ　）、Ｆ”（＋。As shown in FIG.
> is 1, then h[! fXg for No.Oki
=b Using the dictionary of the song (1), the similarity Sfg is calculated as Sfg=7-X 100
Find it using (2). Then, for symbols other than the symbol, such as Su, H, etc., f'Xg=c Using the song (3), the degree Sfg of that kind is Sfg = -X IO(+ [ %] Song (4)
I try to find it using basic calculations. Furthermore, since the matching between the above image and the dictionary is performed for each constituent picture, the above equations (1) and (3) ultimately convert the pixel data into G(
i, j) and F(' e J ), F''(+.

ｊ）とすると、次の式のようになる。j), the following formula is obtained.

ｆＸｇ４１”　（ｉ　ｒ　ｊ）・（ｉ（ｉ　、　ｊ　）
　　＝＝＝　　（５）目 ｆ’Ｘｇ＝↓゛ΣＦ’（ｉ、ｊ）・Ｇ（ｉ、ｊ）・曲・
（６）１」 以上のようＫこの発明の認識方法にょり、ば、ヰとヰ以
外の記号に対してそねぞ」１異なるデータで成る辞書を
用意しておき、片と−の認識の困難性を除去しているの
で、２次元マツチング法によっても確実な記号の認識を
行なうことが可能となる。fXg41” (i r j)・(i(i, j)
=== (5)th f'Xg=↓゛ΣF'(i, j)・G(i,j)・song・
(6) 1 As described above, with the recognition method of this invention, it is possible to recognize symbols other than ba, ヰ and ヰ. Since this difficulty is removed, it is possible to reliably recognize symbols even with the two-dimensional matching method.

【図面の簡単な説明】[Brief explanation of the drawing]

第１図は画像エリアからマツチングエリアな切出す様子
を説明するための図、第２図（Ａ）及び（Ｉ３）はこの
発明における片と−の認識の様子を説明するための図、
第３図（Ａ）〜（Ｃ１は画像データと辞書データとを説
明するための図、第４図はこの発明による％　１　＃　
、　％　０　＃の振り分を示す図である。 １・・・画像エリア、２・・・切出し範囲、３・・・マ
ツチングエリア、Ｐ、Ｑ・・・マツチング対象。FIG. 1 is a diagram for explaining how a matching area is cut out from an image area, and FIGS. 2 (A) and (I3) are diagrams for explaining how pieces and - are recognized in this invention.
FIGS. 3(A) to (C1 are diagrams for explaining image data and dictionary data, and FIG. 4 is a diagram for explaining image data and dictionary data.
, % 0 # is a diagram showing the allocation. 1... Image area, 2... Cropping range, 3... Matching area, P, Q... Matching target.

Claims (1)

【特許請求の範囲】[Claims] 楽譜記号を２次元マツチング法で認識する方法において
、対象記号に対して１１Ｎ及びゝ−１′のデータで成る
辞書データｆと、′１＃及び′ＯＮのデータで成る辞書
データｆ′とを用意しておき、認識対象領域からマツチ
ングの対象記号を％　１１及び′□Ｉのデータｇで抽出
すると共に、ｆＸｇ＝ｂ又はｆ’Ｘｇ＝ｃを求め、前記
認識対象領域のなる式で類似度を求めるようＫしたこと
を特徴とする楽譜記号の認識方法。In a method for recognizing music score symbols using a two-dimensional matching method, dictionary data f consisting of data of 11N and -1' and dictionary data f' consisting of data of '1# and 'ON' are prepared for the target symbol. Then, extract the target symbol for matching from the recognition target area using the data g of %11 and '□I, find fXg=b or f'Xg=c, and calculate the similarity using the formula for the recognition target area. A method for recognizing music score symbols, characterized by the fact that K is used to ask for information.
JP57168153A 1982-09-27 1982-09-27 Recognition of musical notation Granted JPS5957384A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP57168153A JPS5957384A (en) 1982-09-27 1982-09-27 Recognition of musical notation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
JP57168153A JPS5957384A (en) 1982-09-27 1982-09-27 Recognition of musical notation

Publications (2)

Publication Number Publication Date
JPS5957384A true JPS5957384A (en) 1984-04-02
JPH0259509B2 JPH0259509B2 (en) 1990-12-12

Family

ID=15862795

Family Applications (1)

Application Number Title Priority Date Filing Date
JP57168153A Granted JPS5957384A (en) 1982-09-27 1982-09-27 Recognition of musical notation

Country Status (1)

Country Link
JP (1) JPS5957384A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0278522A2 (en) * 1987-02-12 1988-08-17 Kabushiki Kaisha Toshiba Microprocessor
US5864631A (en) * 1992-08-03 1999-01-26 Yamaha Corporation Method and apparatus for musical score recognition with quick processing of image data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP0278522A2 (en) * 1987-02-12 1988-08-17 Kabushiki Kaisha Toshiba Microprocessor
US5864631A (en) * 1992-08-03 1999-01-26 Yamaha Corporation Method and apparatus for musical score recognition with quick processing of image data

Also Published As

Publication number Publication date
JPH0259509B2 (en) 1990-12-12

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