JPS6152783A - Pattern recognizing system - Google Patents
Pattern recognizing systemInfo
- Publication number
- JPS6152783A JPS6152783A JP59173375A JP17337584A JPS6152783A JP S6152783 A JPS6152783 A JP S6152783A JP 59173375 A JP59173375 A JP 59173375A JP 17337584 A JP17337584 A JP 17337584A JP S6152783 A JPS6152783 A JP S6152783A
- Authority
- JP
- Japan
- Prior art keywords
- pattern
- patterns
- peripheral
- input
- stored
- 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.)
- Pending
Links
- 230000002093 peripheral effect Effects 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims abstract description 8
- 230000008602 contraction Effects 0.000 claims description 5
- 238000003384 imaging method Methods 0.000 claims description 3
- 238000012567 pattern recognition method Methods 0.000 claims description 3
- ORFPWVRKFLOQHK-UHFFFAOYSA-N amicarbazone Chemical compound CC(C)C1=NN(C(=O)NC(C)(C)C)C(=O)N1N ORFPWVRKFLOQHK-UHFFFAOYSA-N 0.000 abstract 2
- 238000003909 pattern recognition Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
Landscapes
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
Description
【発明の詳細な説明】 〔発明の技術分野〕 本発明はパターン認識方法に関する。[Detailed description of the invention] [Technical field of invention] The present invention relates to a pattern recognition method.
・文字や各種図形等のパターンを光学的、電気的に処理
して認識する場合、パターンを撮像した二次元信号をメ
モリに貯えこれを予め別のメモリに貯えていた規準信号
と比較し判別することが行われている。従来では撮像し
た二次元信号を処理する場合、二次元配列データーを対
象とし、またそのデータ内の特徴を有する部分を切り出
し、規準信号内の対応部分とマツチングして判別するよ
うにしている。しかしながら、このような手法は特徴部
分の面上の位置(アドレス)を計算せねばならす、パタ
ーン認識の高速化を損害する要因になっていた。また、
入力パターンに対して基準パターンに基ずく何らかの補
正をかえないで、パターンマツチングを行うと、相対的
なずれや部分的な変形が無視できなくな)、認識を誤ま
らせるという問題が生じる。近年、このずれの問題に対
処するための一手法として動的計画法(Dynamic
progr−am i ng ) によるマツチン
グ(以下、DPマツチングと略す)が用いられている。・When recognizing patterns such as characters and various figures by optically or electrically processing, the two-dimensional signal obtained by imaging the pattern is stored in memory and compared with a reference signal previously stored in another memory for discrimination. things are being done. Conventionally, when processing an imaged two-dimensional signal, two-dimensional array data is used as a target, and a portion having characteristics within the data is cut out and discriminated by matching it with a corresponding portion within a reference signal. However, such a method requires calculating the position (address) of a feature on a surface, which is a factor that impairs the speeding up of pattern recognition. Also,
If pattern matching is performed without changing some kind of correction based on the reference pattern to the input pattern, the problem arises that relative deviations and partial deformations cannot be ignored, leading to erroneous recognition. . In recent years, dynamic programming has been used as a method to deal with this misalignment problem.
matching (hereinafter abbreviated as DP matching) is used.
このDrマツチングは主に音声認識の分野に適用されて
いるが、文字認識にも試みられている。この文字認識に
おいては全体的なマツチングを行9ようにしているため
全体の形を追尾するための処理計算が複雑となる問題が
ある。寸だ、上記従来の二次元データ内における特徴部
分〒切シ出す手法にDPマツチングを当てはめてもアド
レス計算の負担量の問題が残る。This Dr matching is mainly applied to the field of speech recognition, but it has also been attempted for character recognition. In this character recognition, since the overall matching is performed in row 9, there is a problem in that processing calculations for tracking the overall shape are complicated. Indeed, even if DP matching is applied to the above conventional method of cutting out characteristic parts within two-dimensional data, the problem of the burden of address calculation remains.
本発明は信頼性の高いパターン認識はもとよ仄その認識
処理を著しく単純化した認識方法を提供することを目的
とする。An object of the present invention is to provide not only highly reliable pattern recognition but also a recognition method that significantly simplifies the recognition process.
撮像信号のうち一次元方向の特徴抽出(以下、周辺特徴
云と略す)を行い、かつこの周辺物微量に対して基準パ
ターンと入力パターンとの間で軸伸縮合せを行うように
構成したものである。It is configured to extract features in one-dimensional direction from the image signal (hereinafter referred to as peripheral features), and to perform axial expansion/contraction between the reference pattern and the input pattern with respect to a small amount of peripheral objects. be.
先ず、あるパターンの周辺分布を出してそのパターンの
特徴とする周辺物微量の処理について第1図について説
明する。すなわち、山は、ITV等の撮像装置によって
得られた二値化像をメモリに記憶したパターンである。First, with reference to FIG. 1, a description will be given of the processing of a trace amount of peripheral objects by obtaining the peripheral distribution of a certain pattern and using it as a feature of that pattern. That is, the mountain is a pattern in which a binarized image obtained by an imaging device such as an ITV is stored in a memory.
このパターンに対したとえばX方向について各行毎に黒
ドツトの数を累計することで符号(2)で示すX方向の
周辺パターンが得られる。この周辺パターン(2)を周
辺物微量とする。同じくY方向につしても行えばY方向
の周辺パターン(3)が得られる。For example, by summing up the number of black dots for each row in the X direction with respect to this pattern, a peripheral pattern in the X direction indicated by reference numeral (2) is obtained. This peripheral pattern (2) is assumed to be a trace amount of peripheral matter. If the same process is performed in the Y direction, a peripheral pattern (3) in the Y direction can be obtained.
以上のような周辺物微量の処理に基ずき、本発明の一実
施例を説明する。An embodiment of the present invention will be described based on the processing of a small amount of peripheral objects as described above.
第2図は複数個の基準パターンにつき、それらの周辺物
微量として記憶メモリに記憶された基準周辺パターンα
Q、 (111,OAである。上記基準パターンはある
特定のパターンの寸法的変化や図形的な変化に基ず“て
基準化されたものや・ある“は・ )全く種類の異
なるものから選ばれるが、本実施例では後者のパターン
になる。FIG. 2 shows a reference peripheral pattern α stored in a storage memory as a trace amount of peripheral objects for a plurality of reference patterns.
Q. (111, OA.The above reference pattern is one that has been standardized based on dimensional changes or graphical changes of a certain specific pattern, or one that has been selected from completely different types.) However, in this embodiment, the latter pattern is used.
次に、認識すべきパターンを入力してその周辺特徴量を
計算し入力周辺パターン(至)を記憶する。この入力周
辺パターンα→を基準周辺ノくターンaO、αV。Next, a pattern to be recognized is input, its surrounding feature amount is calculated, and the input surrounding pattern (to) is stored. This input peripheral pattern α→ is used as a reference peripheral pattern aO, αV.
αJの個々に対応させてDP千手法よシ軸伸縮合せを行
い両パターンの距離を可及的に近ずける。上記の軸伸縮
によって第3図に示すよ、うに対比ノ(ターン(xaa
)、 (13b)、 (13C)が得られる。The distance between both patterns is made as close as possible by performing cy-axis expansion and contraction using the DP thousand method corresponding to each αJ. As shown in Figure 3, the above-mentioned axis expansion and contraction results in a contrasting turn (xaa
), (13b), (13C) are obtained.
以上のような処理の後、対比パターン(13a)。After the above processing, a comparison pattern (13a) is created.
(13b)、 (13c)と基準周辺パターンαO9α
の、(6)とのそれぞれの差、すなわちαG −(xa
a) 、 (il)−(13b)。(13b), (13c) and reference peripheral pattern αO9α
, and (6), that is, αG − (xa
a), (il)-(13b).
(ロ)−(13c)の絶対値を計算する。これにより第
4図に示すような差パター70重、(ハ)、α0が得ら
れ、これらのうち、差パターンαゆが他に比べ小さく、
したがって入力パターンは基準周辺パターンαQの元の
基準パターンに相当することが認識される。(b) Calculate the absolute value of - (13c). As a result, a difference putter with a weight of 70, (c) and α0 as shown in Fig. 4 is obtained, and among these, the difference pattern α distortion is smaller than the others;
Therefore, it is recognized that the input pattern corresponds to the original reference pattern of the reference peripheral pattern αQ.
なお、周辺物微量の計算処理のための走査方向は上記で
説明したXもしくはYといった方向だけでなく斜め方向
やパターン中心から特定方向に走査して得るようにして
もよい。Note that the scanning direction for calculating the amount of peripheral objects is not limited to the X or Y directions described above, but may also be obtained by scanning in an oblique direction or in a specific direction from the center of the pattern.
以上のように一次元方向のみの走査によりノくターンの
特徴量を抽出し、しかも抽出したパターンの歪を修正す
るDPに基ずく軸伸縮会せを行ったり、tで、パターン
マツチングするようにしたのでパターン認識の誤認を少
なくし、しかも簡略に行えるようなった。As mentioned above, we can extract the feature amount of the turn by scanning only in one-dimensional direction, and also perform axis expansion/contraction based on DP to correct the distortion of the extracted pattern, and perform pattern matching with t. This reduces the number of misidentifications in pattern recognition and makes it easier to perform pattern recognition.
第1図乃至第ゴ図は本発明の一実施例を説明するための
各種パターンを示す図である。
(10,αつ、■・・・・・・基準周辺パターンαa・
・・・・・・・・・・・・・・・・・入力周辺パターン
代理人 弁理士 則 近 意 佑
(ほか1名)1 to 5 are diagrams showing various patterns for explaining one embodiment of the present invention. (10, α, ■...Reference peripheral pattern αa・
・・・・・・・・・・・・・・・・・・Input peripheral pattern agent Patent attorney Noriyuki Chika (and 1 other person)
Claims (1)
パターンのうちの特定の規準パターンとの類似度を計測
するパターン認識方法において、上記一以上の規準パタ
ーンに対し少なくとも一次元方向への走査を所定の間隔
で行って特徴となる画素数の計数によって所定の規準周
辺パターンに変換して記憶する工程と、上記入力パター
ンを上記規準周辺パターンを得ると同一の手法で入力周
辺パターンに変換する工程と、この入力周辺パターンを
上記規準周辺パターンの個々に対応して伸縮軸合せを行
って対比パターンに変換する工程と、これら対比パター
ンと規準周辺パターンとの差を計数する工程とを備え、
上記差の大小によって類似度を認識することを特徴とす
るパターン認識方法。In a pattern recognition method for measuring the degree of similarity between an input pattern obtained by an imaging device and a specific reference pattern among one or more reference patterns, the one or more reference patterns are scanned in at least one dimension. a step of converting the input pattern into a predetermined reference peripheral pattern by counting the number of characteristic pixels at intervals of , and storing it; and a step of converting the input pattern into an input peripheral pattern using the same method once the reference peripheral pattern is obtained. , comprising the steps of converting this input peripheral pattern into a comparison pattern by performing expansion/contraction axis alignment corresponding to each of the reference peripheral patterns, and counting the difference between these comparison patterns and the reference peripheral pattern,
A pattern recognition method characterized by recognizing similarity based on the magnitude of the difference.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP59173375A JPS6152783A (en) | 1984-08-22 | 1984-08-22 | Pattern recognizing system |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP59173375A JPS6152783A (en) | 1984-08-22 | 1984-08-22 | Pattern recognizing system |
Publications (1)
Publication Number | Publication Date |
---|---|
JPS6152783A true JPS6152783A (en) | 1986-03-15 |
Family
ID=15959222
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
JP59173375A Pending JPS6152783A (en) | 1984-08-22 | 1984-08-22 | Pattern recognizing system |
Country Status (1)
Country | Link |
---|---|
JP (1) | JPS6152783A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH04277873A (en) * | 1991-03-05 | 1992-10-02 | Hamamatsu Photonics Kk | Features recognizing device |
US7684625B2 (en) | 2002-12-20 | 2010-03-23 | Fuji Xerox Co., Ltd. | Image processing apparatus, image processing method, image processing program, printed matter inspection apparatus, printed matter inspection method and printed matter inspection program |
-
1984
- 1984-08-22 JP JP59173375A patent/JPS6152783A/en active Pending
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH04277873A (en) * | 1991-03-05 | 1992-10-02 | Hamamatsu Photonics Kk | Features recognizing device |
US7684625B2 (en) | 2002-12-20 | 2010-03-23 | Fuji Xerox Co., Ltd. | Image processing apparatus, image processing method, image processing program, printed matter inspection apparatus, printed matter inspection method and printed matter inspection program |
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