JPH012179A - How to identify food - Google Patents
How to identify foodInfo
- Publication number
- JPH012179A JPH012179A JP62-158464A JP15846487A JPH012179A JP H012179 A JPH012179 A JP H012179A JP 15846487 A JP15846487 A JP 15846487A JP H012179 A JPH012179 A JP H012179A
- Authority
- JP
- Japan
- Prior art keywords
- food
- pixels
- brightness
- identification
- camera
- 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
- 235000013305 food Nutrition 0.000 title claims description 18
- 235000013311 vegetables Nutrition 0.000 claims description 18
- 238000000034 method Methods 0.000 claims description 14
- 239000000203 mixture Substances 0.000 claims description 8
- 238000003384 imaging method Methods 0.000 claims description 5
- 240000007124 Brassica oleracea Species 0.000 description 9
- 235000003899 Brassica oleracea var acephala Nutrition 0.000 description 9
- 235000011301 Brassica oleracea var capitata Nutrition 0.000 description 9
- 235000001169 Brassica oleracea var oleracea Nutrition 0.000 description 9
- 238000010586 diagram Methods 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 244000291564 Allium cepa Species 0.000 description 1
- 235000002732 Allium cepa var. cepa Nutrition 0.000 description 1
- 244000000626 Daucus carota Species 0.000 description 1
- 235000002767 Daucus carota Nutrition 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000003306 harvesting Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 235000014102 seafood Nutrition 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Abstract
(57)【要約】本公報は電子出願前の出願データであるた
め要約のデータは記録されません。(57) [Summary] This bulletin contains application data before electronic filing, so abstract data is not recorded.
Description
【発明の詳細な説明】
〔産業上の利用分野〕
本発明は乾燥野菜等の種別を識別する場合に用いて好適
な食品の識別方法に関する。DETAILED DESCRIPTION OF THE INVENTION [Industrial Field of Application] The present invention relates to a food identification method suitable for use in identifying types of dried vegetables and the like.
一般に、刻んだキャベツ、ネギ、人参等を乾燥させた乾
燥野菜(インスタントラーメンの具等)においてはその
色具合も商品価値として重要な要素となる。例えば夏場
に収穫するキャベツは緑色であるが、冬場に収穫するキ
ャベツは比較的黄色、 が強い。このため、要望に応じ
た色具合のものを供給したり、異なる収穫時期のキャベ
ツを適当な割合で混合して要望に応じた色具合に調製す
ることも多い。In general, the color of dried vegetables such as chopped cabbage, green onions, and carrots (instant ramen ingredients, etc.) is also an important factor in terms of commercial value. For example, cabbage harvested in the summer is green, but cabbage harvested in the winter is relatively yellow. For this reason, it is often necessary to supply cabbage with a desired color or mix cabbage from different harvest periods in an appropriate ratio to obtain a desired color.
ところで、このような場合において、要望された種別は
どれか或はどの程度の割合で混合すれば要望の色具合に
調製できるかは感覚的なものであるだけに大変な困難を
伴う。By the way, in such a case, it is very difficult to determine which of the desired types or in what proportions should be mixed to obtain the desired color.
このため、従来は熟練者の肉眼による目視に頼っていた
が、感覚的故に正確な識別は困難であった。また、色相
、彩度等に対し高精度に測定できる高価な色差計による
識別も試みられたが、色模様がバラつく乾燥野菜の場合
には高精度故に再現性がなくなり、反って識別精度は悪
くなる等実用できない不具合があった。For this reason, conventional methods relied on visual inspection by an expert, but accurate identification was difficult due to the sensory nature of the technique. In addition, attempts have been made to identify using expensive color difference meters that can measure hue, saturation, etc. with high precision, but in the case of dried vegetables with variable color patterns, the high precision results in poor reproducibility and the identification accuracy is reduced. There were problems such as deterioration that made it impractical.
本発明は上記従来技術に存在する諸問題を解決した新規
な食品おける識別方法の搗供を目的とするもので、以下
に示す方法によって達成される。The present invention aims to provide a novel food identification method that solves the problems existing in the prior art, and is achieved by the method described below.
即ち、本発明に係る食品の識別方法は単一種又は複数種
が混合した乾燥野菜V等の食品を撮像し、一定区画域か
ら得られる画素の明度を数値化、例えば予め設定したレ
ベルSによって二値化し、−方位の画素数によって当該
食品の種別を識別するようにしたことを特徴とする。That is, the food identification method according to the present invention images a food such as a single type or a mixture of multiple types of dried vegetables V, and converts the brightness of pixels obtained from a certain area into numerical values, for example, by dividing the brightness by a preset level S. It is characterized in that the type of food is identified by the number of pixels in the - direction.
以下には本発明に係る好適な実施例を図面に基づき詳細
に説明する。Hereinafter, preferred embodiments of the present invention will be described in detail based on the drawings.
第1図は本発明方法を実施できる識別装置のブロック系
統図、第2図は同識別装置における撮像部の外観斜視図
、第3図は同識別装置における二値化した画像の一例を
示すパターン図、第4図は種別の異なる乾燥野菜の混合
比に対する画素数の関係図である。FIG. 1 is a block system diagram of an identification device capable of implementing the method of the present invention, FIG. 2 is an external perspective view of an imaging unit in the identification device, and FIG. 3 is a pattern showing an example of a binarized image in the identification device. FIG. 4 is a diagram showing the relationship between the number of pixels and the mixing ratio of different types of dried vegetables.
ところで、本識別方法は本来的に色の識別をすべき対象
物を明度の検出によって識別するものである。即ち、単
一踵又は複数種が混合した乾燥野菜の場合には−の色の
刻み野菜と他の色の刻み野菜が混在した色模様として視
覚的に認識できるとともに、明度の強弱に基づく形状模
様としても認識できる。このことは色模様と明度の強弱
模様を対比させた実験結果においても高い相関関係と再
現性を示すことが確認され、本発明はこの原理に基づき
識別を行うものである。By the way, in this identification method, an object whose color should originally be identified is identified by detecting brightness. In other words, in the case of a single dried vegetable or a mixture of multiple types of dried vegetables, it can be visually recognized as a color pattern in which chopped vegetables of a negative color and chopped vegetables of other colors are mixed, as well as a shape pattern based on the intensity of brightness. It can also be recognized as This has been confirmed to show a high correlation and reproducibility even in experimental results comparing color patterns and brightness patterns, and the present invention performs identification based on this principle.
以下、第1図及び第2図に示す識別装置に基づき本発明
方法について説明する。The method of the present invention will be explained below based on the identification device shown in FIGS. 1 and 2.
第1図において符号lで示す識別装置は撮像部2を備え
る。この*R部2は第2図のように構成し、例えばC0
D(電荷結合素子)カメラ3、このカメラ3を高さ調整
自在に支持するカメラスタンド4、左右に配した照明具
5.6、これら全体を覆い外光の影響を排除する暗箱8
等を備える。The identification device indicated by reference numeral l in FIG. 1 includes an imaging section 2. This *R section 2 is configured as shown in FIG. 2, and for example, C0
A D (charge-coupled device) camera 3, a camera stand 4 that supports the camera 3 in a height-adjustable manner, lighting devices 5 and 6 arranged on the left and right sides, and a dark box 8 that covers all of these and eliminates the influence of outside light.
Equipped with etc.
カメラ3の画素数は多いほど精度は高まるが、実施例で
は画素数256ビツトの白黒カメラを使用した。よって
、トレイ7にトレイ面が隠れるように乾燥野菜■を収容
し、前記カメラ3の下方に置けば、当該カメラ3により
乾燥野菜Vを所定距離から撮像することができる。The accuracy increases as the number of pixels of the camera 3 increases, but in this embodiment, a black and white camera with a pixel count of 256 bits was used. Therefore, if the dried vegetables V are stored in the tray 7 so that the tray surface is hidden and placed below the camera 3, the camera 3 can image the dried vegetables V from a predetermined distance.
一方、カメラ3の映像出力信号は明度に対応した大きさ
となる。そして、この映像出力信号は二値化回路12に
供給され、レベル設定回路11において設定されたレベ
ル(しきい値)Sによって二値化される。つまりレベル
S未満は「0」、レベルS以上は「1」に変換される。On the other hand, the video output signal of the camera 3 has a magnitude corresponding to the brightness. This video output signal is then supplied to the binarization circuit 12 and binarized using the level (threshold) S set in the level setting circuit 11. In other words, a value less than level S is converted to "0", and a value equal to or higher than level S is converted to "1".
このように二値化された画像を第3図に示す。同図は乾
燥キャベツの二値化画像であり、黒部分は「0」、白部
分は「1」である。また、二値化回路12の出力はウィ
ンドウ設定回路13に供給され、一定区画域の画素のみ
が抽出される。第3図において点線の内側が一定区画域
を示すウィンドウWとなる。An image binarized in this way is shown in FIG. The figure is a binary image of dried cabbage, with black parts being "0" and white parts being "1". Further, the output of the binarization circuit 12 is supplied to a window setting circuit 13, and only pixels in a certain section are extracted. In FIG. 3, the area inside the dotted line is a window W indicating a fixed area.
そして、画素数演算回路14において、当該ウィンドウ
W内の一方の画素数、例えば「0」の画素数を計数し、
その合3tを出力回路I5から出力する。出力回路15
の出力は例えばコンピュータシステム等へインプットす
れば表示、記憶、記録、制御等に利用できる。Then, the pixel number calculation circuit 14 counts the number of pixels in one side of the window W, for example, the number of "0" pixels,
The sum 3t is outputted from the output circuit I5. Output circuit 15
The output can be used for display, storage, recording, control, etc. by inputting it into, for example, a computer system.
このような識別装置Iによって、色の異なる各種乾燥野
菜を撮像したところ、次表の結果を得た。When images of various dried vegetables of different colors were taken using the identification device I, the results shown in the following table were obtained.
なお、この場合、任意の乾燥キャベツAの画素数が20
0になるように前記レベルSを設定した。In this case, the number of pixels of any dried cabbage A is 20.
The level S was set to be 0.
このように、種別の異なる各種乾燥野菜のサンプルは明
確に区別できる画素数の差として現れ、肉眼では正確な
識別が困難である乾燥野菜の種別を画素数の相対的比較
で明確に識別することができた。なお、この場合、複数
のサンプルのうち任意のサンプルを基準として設定した
が、予め他の絶対的基準を用意して設定してもよい。In this way, samples of various types of dried vegetables appear as clearly distinguishable differences in the number of pixels, and it is possible to clearly identify the types of dried vegetables that are difficult to identify accurately with the naked eye by comparing the number of pixels. was completed. Note that in this case, an arbitrary sample among the plurality of samples is set as a reference, but other absolute standards may be prepared and set in advance.
他方、第4図から明らかなように黄色のキャベツ(Nl
)と緑色のキャベツ(NA)についてそれぞれの画素数
を得るとともに、両者を1:lで混合した場合の画素数
を得、これらをグラフにプロットしたところ高い相関関
係(98%)が認められた。これにより、事前に要望の
画素数がわかれば、混合比を知ることができるし、また
画素数を検出することによって他の成分がどの程度混入
しているかを知ることもできる。On the other hand, as is clear from Figure 4, yellow cabbage (Nl
) and green cabbage (NA), as well as the number of pixels when both were mixed at a ratio of 1:1, and when these were plotted on a graph, a high correlation (98%) was observed. . Thereby, if the desired number of pixels is known in advance, the mixture ratio can be known, and by detecting the number of pixels, it is also possible to know how much other components are mixed.
以上、実施例について詳細に説明したが、本発明はこの
ような実施例に限定されるものではない。Although the embodiments have been described in detail above, the present invention is not limited to these embodiments.
例えば、乾燥野菜を例にとったが魚介類等をはじめ、一
般には単一種または複数種が混合した任意の食品に適用
することができる。また、画素を得る手段としてCOD
カメラを例示したが、撮像管式カメラであってもよい。For example, although dried vegetables are taken as an example, it can be applied to any food, including seafood, etc., in general, either a single type or a mixture of multiple types. Also, COD is used as a means to obtain pixels.
Although a camera is shown as an example, an image pickup tube type camera may be used.
さらに、二値化を例にしたが、信号レベルに応じて複数
階調にコード化してもよい。その他細部の構成、形状、
手法等において本発明の要旨を逸脱しない範囲で任意に
変更実施できる。Furthermore, although binarization is taken as an example, it may be encoded into multiple gradations depending on the signal level. Other detailed composition, shape,
Any changes can be made in the method etc. without departing from the gist of the present invention.
このように、本発明に係る食品の識別方法は単一種又は
複数種が混合した食品を撮像し、一定区画域から得られ
る画素の明度を数値化、例えば予め設定したレベルによ
って二値化し、−力値の画素数によって当該食品を識別
するようにしたため、次のような効果を得る。As described above, the food identification method according to the present invention images a food of a single type or a mixture of multiple types, digitizes the brightness of pixels obtained from a certain area, for example, binarizes it according to a preset level, and - Since the food is identified by the number of pixels of the power value, the following effects are obtained.
■ 色具合が数値化されるため客観的、かつ正確に食品
の識別を行うことができる。■ Foods can be identified objectively and accurately because color is quantified.
■ 撮像部及び回路系には特に精度は要求されないため
、構成簡易で低コストに実施できる。(2) Since no particular precision is required for the imaging unit and circuit system, the configuration is simple and can be implemented at low cost.
■ 識別の他、調製、分析等に応用することができ、発
展性、汎用性に優れる。■ In addition to identification, it can be applied to preparation, analysis, etc., and has excellent expandability and versatility.
第1図:本発明方法を実施できる識別装置のブロック系
統図、
第2図:同識別装置における撮像部の外観斜視図、
第3図:同識別装置における二値化した画像の一例を示
すパターン図、
第4図二種別の異なる乾燥野菜の混合比に対する画素数
の関係図。
尚図面中、
■=識別装置 3:CCDカメラ11ニレベル
設定回路 12:二値化回路13:ウィンドウ設定回路
V:乾燥野菜 SニレベルFigure 1: Block system diagram of an identification device capable of implementing the method of the present invention, Figure 2: External perspective view of an imaging unit in the identification device, Figure 3: Pattern showing an example of a binarized image in the identification device. Figure 4: Relationship between the number of pixels and the mixing ratio of two different types of dried vegetables. In the drawing, ■ = Identification device 3: CCD camera 11 2-level setting circuit 12: Binarization circuit 13: Window setting circuit V: Dried vegetables S 2-level
Claims (1)
定区画域から得られる画素の明度を数値化するとともに
、この数値に基づいて前記食品の種別を識別することを
特徴とする食品の識別方法。 〔2〕画素の明度を予め設定したレベルによって二値化
し、一方値の画素数によって識別することを特徴とする
特許請求の範囲第1項記載の食品の識別方法。 〔3〕食品は乾燥野菜であることを特徴とする特許請求
の範囲第1項記載の食品の識別方法。[Scope of Claims] [1] Imaging a single type of food or a mixture of multiple types of food, quantifying the brightness of pixels obtained from a certain area, and identifying the type of food based on this numerical value. A food identification method characterized by: [2] The food identification method according to claim 1, wherein the brightness of pixels is binarized according to a preset level, and identification is performed based on the number of pixels of one value. [3] The food identification method according to claim 1, wherein the food is a dried vegetable.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP62-158464A JPH012179A (en) | 1987-06-25 | How to identify food |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP62-158464A JPH012179A (en) | 1987-06-25 | How to identify food |
Publications (2)
Publication Number | Publication Date |
---|---|
JPS642179A JPS642179A (en) | 1989-01-06 |
JPH012179A true JPH012179A (en) | 1989-01-06 |
Family
ID=
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8874702B2 (en) | 2009-12-10 | 2014-10-28 | Ricoh Company, Ltd. | Network apparatus, communication control method, and recording medium |
AT514517A3 (en) * | 2014-11-05 | 2016-04-15 | Avl List Gmbh | Method and device for operating a pump |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8874702B2 (en) | 2009-12-10 | 2014-10-28 | Ricoh Company, Ltd. | Network apparatus, communication control method, and recording medium |
AT514517A3 (en) * | 2014-11-05 | 2016-04-15 | Avl List Gmbh | Method and device for operating a pump |
AT514517B1 (en) * | 2014-11-05 | 2016-06-15 | Avl List Gmbh | Method and device for operating a pump |
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