WO1996018975A1 - Data recognition system - Google Patents
Data recognition system Download PDFInfo
- Publication number
- WO1996018975A1 WO1996018975A1 PCT/AU1995/000813 AU9500813W WO9618975A1 WO 1996018975 A1 WO1996018975 A1 WO 1996018975A1 AU 9500813 W AU9500813 W AU 9500813W WO 9618975 A1 WO9618975 A1 WO 9618975A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- state
- data
- neural network
- substance
- deriving
- Prior art date
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/10—Starch-containing substances, e.g. dough
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20076—Probabilistic image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30128—Food products
Definitions
- the present invention relates to recognition of data characteristics within an image and more particularly to the recognition of characteristics within a baking process.
- the goods are normally inspected to ensure quality control. Inspection often takes the form of looking at a particular batch of goods coming off a production line in order to ensure their suitability.
- a method of determining the state of a substance having a color property dependent on said state comprising the steps of: forming a pixel image of said substance; projecting said pixel image into a three dimensional color space; and comparing said projection with a projection of a second portion of said substance, having a predetermined state, so as to determine the state of said substance.
- an apparatus which is able to operate in accordance with the above method.
- a method of determining the state of a first data sample having collectively a series of characteristics comprising the steps of: projecting said first data sample into a multi-dimensional space; and comparing said projection with a projection of a second data sample having a set of predetermined characteristics, to determine the state of said first data sample characteristics.
- Fig. 1 illustrates the steps of the preferred embodiment
- Fig. 2 illustrates the input scanned data format utilised by the preferred embodiment
- Figs. 3-8 illustrate the progression of the color change with increased baking levels
- Fig. 9 illustrates a Kohonen self-organising feature map
- Fig. 10 illustrates a first baking curve
- Fig. 11 illustrates a second baking curve for a different biscuit product.
- a mass production system 1 for producing baked foodstuffs is illustrated schematically in Fig. 1.
- the system 1 comprises a food processing apparatus (e.g. oven) 4, substance characterisation apparatus 3, 6, 7, 8, 9, and control system 11.
- Baked products 2 are imaged by a camera 3 as the products exit the oven 4.
- the image taken by camera 3 is subjected to a number of processing steps preferably implemented using a microcomputer, which will be described in more detail hereinafter.
- the processing includes an image processing or calibration step 6, a first neural network processing step 7, an image processing step 8, and a second neural network step 9 to produce an output indication 10 that indicates the extent of the baking of the baked products 2.
- the output 10 provides an indication of whether the products 2 are under- or over- baked and can be utilised as an input to the control system 11 to determine if the oven parameters such as heat or time need to be adjusted.
- the camera 3 utilised in the preferred embodiment is a three-chip charge coupled device (CCD) camera which produces a 512 X 512 array of red (R), green (G), and blue (B), or RGB, pixel values. This array of pixel values is stored in a "frame grabber" board mounted within the microcomputer.
- the baking products 2 were imaged using two daylight-color-balanced THORN (Registered Trade Mark) fluorescent lamps for direct illumination and a black curtain to exclude ambient illumination. It should be apparent to a person skilled in the art that other forms of illumination may be used, and/or the background curtain may be eliminated in an industrial production process, although some other form of ambient light shielding may by utilised without departing from the scope and spirit of the invention.
- THORN Registered Trade Mark
- the baked products imaged were "SAO" (Registered Trade Mark) biscuits produced in a production line of the Arnott's Biscuits Limited biscuit factory.
- the biscuits were imaged with a color calibration chart 12 of the form shown in Fig. 2.
- the scanned image 12' includes biscuit area 13, background 14, reference white background 15, grey scale 16, and reference colors 17.
- the scanned image 12' and image 12 were men utilised to color calibrate the pixels of the biscuit 13.
- the process of color calibrations to ensure color consistency of sampled images is a well known process to those skilled in the art of computer imaging. For a discussion of the color calibration process, reference is made to Novak, C.L. and S.A.
- each biscuit image can be stored as an RGB file.
- the RGB color space is a well known color space, in common use with computer systems. However, it will be apparent to a person skilled in the art that other color systems such as HSN, XYZ or CIE color space coordinates may also be utilised.
- a projection of the pixel values obtained for biscuit 13 (Fig. 2) is shown for different stages of the baking process.
- Fig. 3 shows a projection 20 of the pixel image of a "raw" biscuit onto an RGB cube 21.
- Figs. 4 to 8 show corresponding projections for biscuits varying from underbaked biscuits (Fig. 4) to overbaked biscuits (Fig. 8). It can be seen from comparing Figs. 4 to 8 that the color characteristics of a biscuit change as that biscuit is subjected to increased levels of baking. The particular progression of pixel data of Figs. 4 to 8 can therefore be utilised to determine the baking state of any particular sample biscuit. In order to utilise the progression from Fig. 4 to Fig. 8 in the baking process, it is necessary to succinctly and compactly describe an approximation to the data samples of Figs. 4 to 8.
- baking curve which is a one-dimensional representation of the important color variations within the three- dimensional data space of Figs. 4 to 8.
- One method of production of a baking curve is to utilise a Kohonen self-organising feature map which is an unsupervised learning technique that is effective in extracting structure from complex experimental data which bears a highly non-linear relationship.
- Kohonen 's self-organising feature map is provided in Neural Network Architectures: An Introduction by Judith E. Dayhoff, published 1990 by Nan Nostrand Reinhold at pages 163-191 , the contents of which are hereby incorporated by cross-reference.
- the SOM 30 has three input nodes 31 which correspond to the red, green and blue color components of pixel values from the digitised color image of the biscuit 13 (Fig. 2).
- the SOM 30 includes N output nodes 32. Every input node 31 is connected by means of edges e.g. 33, to each of the output nodes 1 to N.
- edges e.g. 33 to each of the output nodes 1 to N.
- the use of a one-dimensional SOM 30 means that, upon training, the SOM network 30 will map the entire set of RGB pixel values 31 from a biscuit image to a one-dimensional array of points or output nodes 32.
- the best or winning node c is then altered in conjunction with nodes within the neighbourhood of c (for example, node c - 1 and c + 1).
- the alteration, for each output node j in the neighbourhood of c proceeds by first calculating ⁇ U; as follows:
- ⁇ (t) ⁇ 0 (1 - VT)
- Eqn 6 where ⁇ 0 takes on values in the range of 0.02 to 0.05, t is the current training iteration, and T is the total number of training iterations to be done.
- d (t) d,, (1 - VT)
- Eqn 7 where d 0 can be chosen to initially be, say, a third of the width of the output nodes.
- four biscuit samples of "SAO" from Arnott's Biscuits Limited were scanned to yield 47,967 pixels per biscuit with three separate R, G, and B values for each pixel. The four sets of pixels were then shuffled in a random sequence and used as training input to an SOM of the form of Fig. 9 having ten output nodes for a total of 20 training passes.
- Fig. 10 there is shown a plot of the ten output node weight values U- within a three-dimensional color cube 35.
- the ten points e.g., 36
- curve 37 is hereby denoted to be the final "baking curve" of the input data.
- Fig. 11 the process was repeated for a second form of biscuit, comprising Arnott's "MILK COFFEE” (Registered Trade Mark) biscuit for a 15 node output SOM and the results 41 are shown plotted within the color cube 40, with the training data being passed dirough the SOM a total of 50 times.
- MILK COFFEE Registered Trade Mark
- the color curve 41 of Fig. 11 is also shorter than that of Fig. 10 as the "MILK COFFEE" form of biscuit exhibits more consistent browning than the "SAO" form of biscuits, which have blisters that can cause uneven browning in color.
- the SOM 30 of Fig. 9 can be utilised as neural network 7 of Fig. 1 to produce, for each input pixel, an output node indicator having the closest position to the input pixel.
- the closest matching output node 32 (Fig. 9) for each pixel of an image can be subjected to image processing 8 (Fig. 1) which can take the form of histogramming, thereby producing a histogram profile of the scanned baking product 2.
- a second neural network 9, which takes the form of a supervised feed forward neural network, can then be subjected to "training" by imaging a large number of biscuits 2 having known baking characteristics, feeding the images through SOM 7, and forming a histogram 8.
- the histogram 8 can then form the input data to a supervised back propagation neural network which can be trained, in the normal manner, to classify the color level of the baking product (e.g. the biscuit) 2.
- the samples can be continuously fed through the steps 6 to 9 until the neural network 9 is properly trained to produce output 10 indicating the level of baking.
- the output 10 can then be utilised by control system 11 which can take the form of human or automatic process adjustment to adjust the conditions within oven 4 to improve the baking products 2.
- the specified system provides automatic segmentation of the biscuit subject from diverse backgrounds. This is accomplished by the histogramming process which "weighs" each image pixel as a reducing function of its distance from the histogram points. Thus, pixels that are significantly distant from the baking curve discovered by the self-organising map are down-weighted to the extent that they make little or no contribution to the overall histogram. In practice, this means that it is not necessary for biscuits to be imaged with a specially prepared background. Any background with colors sufficiently dissimilar to the biscuit colors under consideration will suffice. This is of practical benefit when applying the system to on-line monitoring and oven control, as the imaging background may well be a conveyor belt of inconsistent color.
- the computation of the histogram 8 from the map produced by the SOM is performed in detail as follows.
- the purpose is to obtain histograms in which each bin represents the weighted count of pixels falling within a fuzzy portion of the baking curve 35,40.
- the fuzzy portion is defined by a Gaussian weighting function with parameters ⁇ x and ⁇ v .
- the parameter ⁇ x denotes the spread of the Gaussian weighting function about the baking curve, and it is determined by consideration of the likely color variation around the curve. It is chosen sufficiently small enough to enable the automatic segmentation process previously described.
- the parameter ⁇ v denotes the spread of the Gaussian weighting function along the baking curve for a particular histogram bin. Normally, ⁇ v is greater than ⁇ x .
- a practical implementation of the above technique involves the following steps: a.
- the SOM nodes are interpolated to obtain a large number of sampling points.
- the number of sampling points is determined by ⁇ x so as to limit to an acceptable level the aliasing effect caused by sampling the baking curve at discrete points.
- the Nyquist result in sampling theory applies in this step.
- b. The biscuit pixels are histogrammed at the interpolated sampling points. The distance of each pixel (in RGB color space) from each sampling point is computed, and the Gaussian function with spread ⁇ x is used to compute the weighted contribution of that pixel to the histogram bin at that particular sampling point.
- the histogram produced in (b) is treated as a 1-D signal and filtered with a second Gaussian function with a spread J ⁇ - ⁇ . In the process, it is subsampled to
- ⁇ y is chosen so as to limit to an acceptable level the aliasing caused by this further subsampling.
- the system may be applied to multi-dimensional data of diverse kinds such as combinations of color, visual texture and/or three-dimensional structure sensing. In some such situations, the set of state points may require a 2-D or 3-D SOM whereas the baking curve requires only a 1-D SOM.
- steps 6 to 9 can be implemented in many different ways, including dedicated neural network hardware and associated computer hardware or in the form of a software simulation of the neural network system.
- the preferred method of implementation of steps 6 to 9 is in the form of software implementation on a standard microcomputer system as this allows for easy alteration when it is desired to alter the form of baking products.
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Chemical & Material Sciences (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Food Science & Technology (AREA)
- Theoretical Computer Science (AREA)
- Analytical Chemistry (AREA)
- Medicinal Chemistry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
Description
Claims
Priority Applications (5)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
JP8517937A JPH10511786A (en) | 1994-12-13 | 1995-12-01 | Data recognition system |
NZ296487A NZ296487A (en) | 1994-12-13 | 1995-12-01 | Baking foodstuffs by determining state of a substance from interdependent colour property by forming image and projecting it into colour space and comparing with reference |
AU41114/96A AU4111496A (en) | 1994-12-13 | 1995-12-01 | Data recognition system |
GB9710197A GB2311369A (en) | 1994-12-13 | 1995-12-01 | Data recognition system |
DE19581867T DE19581867T1 (en) | 1994-12-13 | 1995-12-01 | Data recognition system |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
AUPN0023 | 1994-12-13 | ||
AUPN0023A AUPN002394A0 (en) | 1994-12-13 | 1994-12-13 | Data recognition system |
Publications (1)
Publication Number | Publication Date |
---|---|
WO1996018975A1 true WO1996018975A1 (en) | 1996-06-20 |
Family
ID=3784530
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/AU1995/000813 WO1996018975A1 (en) | 1994-12-13 | 1995-12-01 | Data recognition system |
Country Status (8)
Country | Link |
---|---|
JP (1) | JPH10511786A (en) |
CN (1) | CN1170469A (en) |
AU (1) | AUPN002394A0 (en) |
CA (1) | CA2207326A1 (en) |
DE (1) | DE19581867T1 (en) |
GB (1) | GB2311369A (en) |
NZ (1) | NZ296487A (en) |
WO (1) | WO1996018975A1 (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2002312762A (en) * | 2001-04-12 | 2002-10-25 | Seirei Ind Co Ltd | Grain sorting apparatus utilizing neural network |
CN106778912A (en) * | 2017-01-13 | 2017-05-31 | 湖南理工学院 | A kind of full-automatic apparatus for baking and method for cake |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110111648A (en) * | 2019-04-17 | 2019-08-09 | 吉林大学珠海学院 | A kind of programming training system and method |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1990000733A1 (en) * | 1988-07-14 | 1990-01-25 | Garibaldi Pty. Ltd. | Computerised colour matching |
WO1992012500A1 (en) * | 1990-12-31 | 1992-07-23 | Neurosciences Research Foundation, Inc. | Apparatus capable of figure-ground segregation |
GB2256708A (en) * | 1991-06-11 | 1992-12-16 | Sumitomo Heavy Industries | Object sorter using neural network |
US5185850A (en) * | 1990-05-22 | 1993-02-09 | Toyo Ink Mfg. Co., Ltd. | Color transformation method and apparatus for transforming physical to psychological attribute using a neural network |
WO1994006092A1 (en) * | 1992-09-07 | 1994-03-17 | Agrovision Ab | Method and device for automatic evaluation of cereal grains and other granular products |
-
1994
- 1994-12-13 AU AUPN0023A patent/AUPN002394A0/en not_active Abandoned
-
1995
- 1995-12-01 JP JP8517937A patent/JPH10511786A/en active Pending
- 1995-12-01 NZ NZ296487A patent/NZ296487A/en unknown
- 1995-12-01 GB GB9710197A patent/GB2311369A/en not_active Withdrawn
- 1995-12-01 WO PCT/AU1995/000813 patent/WO1996018975A1/en active Application Filing
- 1995-12-01 CA CA002207326A patent/CA2207326A1/en not_active Abandoned
- 1995-12-01 DE DE19581867T patent/DE19581867T1/en not_active Withdrawn
- 1995-12-01 CN CN95196801A patent/CN1170469A/en active Pending
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO1990000733A1 (en) * | 1988-07-14 | 1990-01-25 | Garibaldi Pty. Ltd. | Computerised colour matching |
US5185850A (en) * | 1990-05-22 | 1993-02-09 | Toyo Ink Mfg. Co., Ltd. | Color transformation method and apparatus for transforming physical to psychological attribute using a neural network |
WO1992012500A1 (en) * | 1990-12-31 | 1992-07-23 | Neurosciences Research Foundation, Inc. | Apparatus capable of figure-ground segregation |
GB2256708A (en) * | 1991-06-11 | 1992-12-16 | Sumitomo Heavy Industries | Object sorter using neural network |
WO1994006092A1 (en) * | 1992-09-07 | 1994-03-17 | Agrovision Ab | Method and device for automatic evaluation of cereal grains and other granular products |
Non-Patent Citations (1)
Title |
---|
PROCEEDINGS OF THE 1991 INTERNATIONAL CONFERENCE ON ARTIFICIAL NEURAL NETWORKS (ICANN-91), Vol. 1, 24-28 June 1991, USUI et al., "Internal Color Representation Acquired by a Five-Layer Neural Network", pp. 867-72. * |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2002312762A (en) * | 2001-04-12 | 2002-10-25 | Seirei Ind Co Ltd | Grain sorting apparatus utilizing neural network |
CN106778912A (en) * | 2017-01-13 | 2017-05-31 | 湖南理工学院 | A kind of full-automatic apparatus for baking and method for cake |
Also Published As
Publication number | Publication date |
---|---|
JPH10511786A (en) | 1998-11-10 |
AUPN002394A0 (en) | 1995-01-12 |
CN1170469A (en) | 1998-01-14 |
DE19581867T1 (en) | 1997-12-11 |
CA2207326A1 (en) | 1996-06-20 |
GB9710197D0 (en) | 1997-07-09 |
NZ296487A (en) | 2000-01-28 |
GB2311369A (en) | 1997-09-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
RU2613319C2 (en) | Method for assessment and quality control of food products on dynamic production line | |
Pedreschi et al. | Development of a computer vision system to measure the color of potato chips | |
Sapirstein et al. | Instrumental measurement of bread crumb grain by digital image analysis | |
Wu et al. | Colour measurements by computer vision for food quality control–A review | |
Brosnan et al. | Improving quality inspection of food products by computer vision––a review | |
Arce-Lopera et al. | Luminance distribution as a determinant for visual freshness perception: Evidence from image analysis of a cabbage leaf | |
Gökmen et al. | A non-contact computer vision based analysis of color in foods | |
Andresen et al. | Quality assessment of butter cookies applying multispectral imaging | |
Zheng et al. | Object measurement methods | |
Kvaal et al. | Multivariate feature extraction from textural images of bread | |
Nashat et al. | Multi-class colour inspection of baked foods featuring support vector machine and Wilk’s λ analysis | |
Wu et al. | Food colour measurement using computer vision | |
Cevoli et al. | Storage of wafer cookies: Assessment by destructive techniques, and non-destructive spectral detection methods | |
WO1996018975A1 (en) | Data recognition system | |
AU4111496A (en) | Data recognition system | |
Yin et al. | Image processing techniques for internal texture evaluation of French fries | |
Hamey et al. | Pre-processing colour images with a self-organising map: baking curve identification and bake image segmentation | |
WO2003031956A1 (en) | System and method for classifying workpieces according to tonal variations | |
Wasnik et al. | Digital image analysis: Tool for food quality evaluation | |
Khan et al. | Automatic quality inspection of bakery products based on shape and color information | |
Phetphoung et al. | Automatic sushi classification from images using color histograms and shape properties | |
Gunasekaran | Computer vision systems | |
Patil et al. | Smart Phone Camera based Weighing Scale for Kitchens in Household Applications | |
Judal et al. | Role of machine vision system in food quality and safety evaluation | |
CN114943737B (en) | Flaky pastry quality evaluation method and device and readable storage medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
WWE | Wipo information: entry into national phase |
Ref document number: 95196801.7 Country of ref document: CN |
|
AK | Designated states |
Kind code of ref document: A1 Designated state(s): AL AM AT AU BB BG BR BY CA CH CN CZ DE DK EE ES FI GB GE HU IS JP KE KG KP KR KZ LK LR LS LT LU LV MD MG MK MN MW MX NO NZ PL PT RO RU SD SE SG SI SK TJ TM TT UA UG US UZ VN |
|
AL | Designated countries for regional patents |
Kind code of ref document: A1 Designated state(s): KE LS MW SD SZ UG AT BE CH DE DK ES FR GB GR IE IT LU MC NL PT SE BF BJ CF CG CI CM GA GN ML MR NE SN TD TG |
|
DFPE | Request for preliminary examination filed prior to expiration of 19th month from priority date (pct application filed before 20040101) | ||
121 | Ep: the epo has been informed by wipo that ep was designated in this application | ||
WWE | Wipo information: entry into national phase |
Ref document number: 296487 Country of ref document: NZ |
|
ENP | Entry into the national phase |
Ref document number: 2207326 Country of ref document: CA Ref document number: 2207326 Country of ref document: CA Kind code of ref document: A |
|
ENP | Entry into the national phase |
Ref document number: 1997 836848 Country of ref document: US Date of ref document: 19971202 Kind code of ref document: A |
|
RET | De translation (de og part 6b) |
Ref document number: 19581867 Country of ref document: DE Date of ref document: 19971211 |
|
WWE | Wipo information: entry into national phase |
Ref document number: 19581867 Country of ref document: DE |
|
122 | Ep: pct application non-entry in european phase |