GB2385662A

GB2385662A - Classifying workpieces according to their tonal variation

Info

Publication number: GB2385662A
Application number: GB0123984A
Authority: GB
Inventors: Willam Clocksin
Original assignee: MILLENNIUM VENTURE HOLDINGS LT
Current assignee: MILLENNIUM VENTURE HOLDINGS LT
Priority date: 2001-10-05
Filing date: 2001-10-05
Publication date: 2003-08-27
Also published as: GB0123984D0

Abstract

The method comprises capturing an electronic image of a workpiece such as a tile by a camera (8), analysing the electronic image to derive a histogram (H, fig. 2) of tonal variation in the image, deriving a numerical representation of the tonal variation in the workpiece by the use of an algorithm operative upon probabilities (P[0], P[1], ..., fig. 2) derived from the said histogram. The workpieces may be moving past a workstation (7) on which the camera (8) may be mounted. The images may be stored on a control processor, which in turn is used to compute the tone features T, of the workpieces. It also calculates the probability of the occurrence of such tones, and compares the resulting tone features with a predetermined tone to determine the similarities between the tone features and thereby ensures that workpiece is directed to a specific collection point (10) in which all the workpieces with similar tones are collected.

Description

MACHINE SENSING SYSTEMS AND A METHOD THEREFOR Field of the Invention This invention relates to machine sensing systems and a method therefor, and more particularly to a method of in process inspection of a decorative tone of a workpiece such as a ceramic tile.

Background of the Invention One known process for manufacturing ceramic tiles from raw materials requires ceramic particulates of different grain sizes and binding agents to be provided in a given proportion one relative to another. These materials are then mixed together and molded into various shapes of tile product. The shaped tiles are then fired in a kiln to produce the finished tile. The decorative pattern of the tile may be formed by the random mixture of the ceramic particulates different grain sizes and colour where the amounts of the particulates are in a given proportion. Because of the natural variations in the raw materials and the mixing process, there may be some variation in the overall colour and pattern in one tile relative to the next. Therefore, it is advantageous for the manufacture to ensure a consistency of tile product, to inspect the finished tile for a quality referred to as its"tone".

The tone of a tile surface is a subjective judgement related to the overall colour and granule distribution of the tile surface. In the case of the tile user (such as a building contractor) who lays tiles on a floor and/or wall, it is usual

for these tiles on the wall or floor should have the same tone. Otherwise, odd tiles which have a different tone can spoil a uniform effect of a wall or floor. Accordingly, during manufacture of a tile the manufacturer inspects the finished tiles and those tiles of a similar tone are grouped together so that they can be packed into boxes marked with the designation of that particular tone.

Furthermore, once it is known that a given tile recipe tends to produce tiles in certain known tones, such information can be used by the tile manufacturer to produce tiles of a given tone. This is particularly advantageous for fulfilling repeat orders for customers who require tiles to match an existing floor or wall. However, sorting is known to be effected manually and relies entirely on the vision of the person making a selection of the tiles.

US Patent 5 809 165 discloses the use of colour imagery for inspecting the image of a tile. However, it is also known to carry out the inspection of the image using monochrome image information only, that is, statistical features of the monochrome image.

When colour images are examined by a technique called principal value decomposition it has been observed that the principal image axis describes granularity and other textural structure in the image together with the average colour which is in effect a weighted monochrome image. A secondary axis has the maximum deviation from the average colour, and in this sense contains the colour information. There is also a third minor axis which usually corresponds to random noise in the image. The ratio of the variances

along the principal, secondary and minor axes is typically 4000: 300: 40 grey levels squared, suggesting strongly that it is the principal, weighted greyscale, axis that is by far the most important.

There are also signal-to-noise considerations to be made with colour imaging. Colour linescan cameras are significantly more noisy than the best greyscale cameras, which use time-delay integration (TDI) methods to improve their signal-to-noise ratio enormously. Colour TDI cameras have not appeared commercially in volume yet. It can be argued that the signal-tonoise ratio can be improved enormously when averaging over a large number of pixels as is done with the TDI camera. However, the variance of the pixel values in the image cannot be improved in this way. The variance will always deteriorate in the presence of noise.

The characteristics of the surface illumination also play a crucial role in colour imaging. Greyscale image inspection systems require the intensity of the light source to be stable with time. Colour systems require both the intensity and the spectral distribution to be stable. If the spectral distribution of the illumination changes, the colour of the observed surface will seem to change. This effect is known as metamerism, and its consequences are well known in the retail industry, where for example, a pair of garments that appear to match well under the artificial fluorescent illumination of the highstreet store, appear to have different colours in natural daylight. This problem gave rise to a method of evaluating fluorescent lighting known as the Colour Rendering Index (CRI). Natural daylight has a maximum CRI of 100.

Fluorescent tubes are available that can give CRIs approaching the maximum, by using carefully matched phosphors. However, fluorescent tubes have spectra consisting of a series of peaks or spikes, while natural light has a continuous spectrum. Even tubes with a CRI of 100 do not produce the equivalent of natural daylight. Furthermore, the characteristics of the illumination used in a machine vision system will change with time as the light source degrades. There is a great deal of ongoing research at present into colour constancy techniques that re-intended to restore the correct colour.

Although human vision performs colour constancy very well, there is no machine vision algorithm that is comparable. Recent research (Funt et al., 1998) has shown that there is no known colour constancy algorithm that can perform well enough for the object recognition.

In practice, a ceramic tile inspection system typically looks for textural variations as well as variations in tone. Colour change is a gradual process that projects on to the greyscale image: a change in colour tends to correlate with a change in grey level. Theoretically certain changes in colour can occur that are indistinguishable in the greyscale image, however in practical application this is extremely rare. In all other cases a change in colour always correlates with a change in grey level.

Therefore, although a first sight it appears that colour imaging ought to give a significant advantage over monochrome, this is not in practice the case. Monochrome systems have higher signal-to-noise ratios, illumination that is easier to control and require far less memory and computational power. For

practical purposes this means that a well designed monochrome machine vision inspection system is faster, more reliable and more cost effective than its colour counterpart. Accordingly, although not further mentioned, it is to be understood the embodiments described with references to the drawings preferably utilise monochrome machine vision inspection systems.

Summary of the Invention According to one aspect of the present invention there is provided a method of classifying workpieces according to their tonal variations, said method comprising capturing an electronic image of a workpiece, analysing said electronic image to derive a histogram of tonal variations in the image, and deriving a numerical representation of the tonal variations in said workpieces by use of an algorithm operative upon statistics derived from said histogram.

In one embodiment in accordance with the present invention there may be provided a method of inspecting the surface tone of a workpiece, comprising moving the workpiece by moving means past a workstation, capturing the electrical image utilising image retaining means mounted on the workstation, storing the image of the workpiece in a control processor and utilising the control processor to compute the equation

so as to determine the textone feature T of the workpiece, P representing the probability of occurrence, and comparing the resulting workpiece textone

with a predetermined stored textone so as to determine the similarities between textones and thereby ensure the workpiece is directed to a specific collection point in which all workpieces with the same substantially identical textones are collected.

In another embodiment in accordance with the present invention the method requires detecting the presence of the workpiece prior to collection of the electrical image by the image retaining means.

The method preferably comprises computing the mean and variance features of a region of interest of the workpiece and utilising the results to assist in computing the textone feature of the region of interest.

In an alternative embodiment a histogram may be generated.

Conveniently the histogram is indexed from 0 to 255.

A further embodiment may require passing the image data signals to a digital high-pass filter.

In yet a further embodiment the method can comprise computing, for each picture element (pixel) of the region of interest, a convolution of that region surrounding the pixel with a given mask matrix. Conveniently, the convolution may give a numerical result in the range-255 to 255.

An embodiment in accordance with the present invention may include computing of an estimator of the histogram.

A method in accordance with the present invention may comprise locating the appropriate estimator by computing a second moment of the probability distribution corresponding to the histogram.

In another embodiment in accordance with the present invention the method requires estimating the probability of occurrence P [i] for each element by dividing H [i] by the total histogram mass M, where M is computed by

Preferably, the probability of occurrence of pixels occurs at locations of zero brightness gradient.

According to a further aspect of the present invention there is provided a machine sensing system comprising capturing means for obtaining an electronic image of a workpiece, analysing means for analysing said electronic image to derive a histogram of tonal variations in the image, and means for deriving a numerical representative of the tonal variations in said workpiece by use of an algorithm operative upon probabilities.

In one embodiment in accordance with the present invention there may be provided a machine sensing system, comprising means for moving a workpiece passed a work station, image retaining means mounted on the workstation for capturing an electrical image of the workpiece, control processor means for storing the electrical image of the workpiece and for computing the equation

so as to determine the textone feature T of the workpiece, P representing the probability of occurrence, and comparator means for comparing the workpiece textone with a predetermined stored textone so as to determine

similarities between textones and thereby ensure the workpieces are directed to a specific collection point in which workpieces having substantially identical textones are collected.

In another embodiment in accordance with the present invention the system comprises detecting means for detecting the presence of a workpiece in the field of view of the image retaining means.

Preferably, there may be provided computing means in the central processor for computing the mean and variance features of a region of interest of the workpiece and utilising the results to assist in computing the textone feature of the region of interest.

Conveniently, means are provided for generating a histogram which may be indexed from 0 to 255.

In an alternative embodiment in accordance with the present invention there is provided a digital high-pass filter through which image data signals are arranged to pass.

Means may be provided for computing each picture element of the region of interest, a convolution of a region surrounding the pixel with a given mask matrix. Conveniently, the convolution gives a numerical result in the range-255 to 255.

Means may also be provided for computing an estimator of the histogram.

An appropriate estimator can be located by computing a second moment of the probability distribution corresponding to the histogram.

Furthermore, means can be provided for estimating the probability of occurrence P [i] for each element by dividing H [i] by the total histogram mass M, where M is computed by

According to a further aspect of the present invention there is provided an apparatus for inspecting tonal variations in a workpiece, comprising capturing means for capturing an electronic image of a workpiece, analysing means for analysing said electronic image to derive a histogram of tonal variations in the image, and deriving means for deriving a numerical representation of the tonal variations in said workpiece by use of an algorithm operative upon probabilities derived from said histogram.

In a further embodiment there is provided an apparatus for inspecting the surface tone of a workpiece, comprising means for moving a workpiece passed a work station, image retaining means mounted on the workstation for creating an electrical image of the workpiece, control processor means for storing the electrical image of the workpiece and for computing the equation

so as to determine the textone feature T of the workpiece, P representing the probability of occurrence, and comparator means for comparing the workpiece textone with a predetermined stored textone so as to determine similarities between textones and thereby ensure the workpiece is directed to a

specific collection point in which all workpieces of the same substantially identical textones are collected.

Brief Description of Drawings Embodiments in accordance with the present invention will now be described by way of example with reference to the accompanying drawings, in which: Figure 1 is a diagrammatic side elevational view of an apparatus in accordance with the present invention; Figure 2 is a diagrammatic flow diagram for use in the computation of a text and feature; and Figure 3 illustrates a typical mask matrix defining a high pass filter.

Detailed Description of the Preferred Embodiments Referring to the drawings, Figure 1 shows a machine sensing apparatus 1 for use in a machine sensing system. The apparatus comprises an elongate conveyor belt 2 on which a rectangular workpiece 3, such as a ceramic tile, is conveyed from one end 4 of the conveyor belt to an opposite end 5 at which is located a shaft encoder 6 for controlling the speed at which the conveyor belt moves.

A support gantry 7 supports a camera 8 above and spaced from the conveyor belt 2. A sensor 9 is also supported by the support gantry 7 above

and spaced from the conveyor belt 2 to allow at least one ceramic tile on the conveyor belt to pass beneath the sensor.

The shaft encoder 6, camera 8 and sensor 9 are all arranged to generate electrical digital signals which are fed to a control processor 10. Each of these devices are electrically connected to the central processor by standard electrical circuit connections which are not shown to more clearly illustrate the invention.

When a workpiece 3 is moved from one end 4 towards opposite end 5 of the conveyor belt 2, the presence of the workpiece is detected initially by the sensor 9 when the workpiece 3 enters the field of the viewing range of the camera 8.

The workpiece 3 continues to move through the field of view of the camera 8 and the control processor 10 instructs the camera to acquire an image of the workpiece. The camera 8 operates to achieve such image and sends the image data to the control processor 10 where that image is stored in the memory of the control processor 10.

Three features of a rectangular portion of the image bounded by the tile edges are obtained using algorithms stored in the control processor 10.

The three features are utilised by the control processor 10 to determine the tone of the surface of the workpiece in a manner which approximates human judgement. The control processor generates a tone decision signal which is then transmitted to a standard ancillary sorting apparatus (not shown) so that the workpiece under investigation is directed to a particular one of

several workpiece bins where all workpieces having the same tone are collected.

The image boundary of the workpiece stored in the memory of the control processor is represented in Figure 2 by the reference number 22 and the image of interest is presented by the boundary line 24.

The image 20 comprises a rectangular array of integers in a range 0 to 255 representing the brightness of the image. Each integer is known more commonly as a"pixel". The three features of the image which are obtained by the control processor 10 are (a) the mean value, (b) the variance of tone, and (c) the textone of the rectangular portion 22 of the workpiece image defined by the external edges of the workpiece. Such rectangular portion 22 is referred to as"the region of interest", but in reality the region of interest is inset from the edges of the workpiece by 3% of the external workpiece dimensions, as shown by the rectangular line 24 in Figure 2, so that unpredictable reflectance effects from the edges of the workpiece are avoided.

The mean 26 and variance 28 of the region of interest are obtained using techniques which are well known to any competent practitioner versed in image processing techniques.

However, the textone 30 is obtained differently as follows: Initially there is provided a graphical representation of the light frequency distribution utilizing abutting rectangles in which (a) the width of each rectangle is equal to that of each category and (b) the height is proportional to the frequency in each interval so that each rectangle represents

its class frequency. That is, a histogram is produced which is indexed from 0 to 255 over the region of interest of the workpiece image.

A digital high-pass filter, such as that illustrated in Figure 3, is applied to each pixel of the region of interest so that a convolution of a region surrounding the pixel with a given mask matrix can be computed by the control processor as illustrated at 32 in Figure 2. This convolution gives a numerical result in the range-255 to 255. The absolute value of this result is taken and the frequency of this result is tabulated in a histogram, as illustrated at 34 in the flow diagram of Figure 2.

Therefore, once each pixel of the region of interest has been processed in the same manner the final result will be a histogram describing the frequency of the occurrence of the absolute value of the high pass filtered pixels.

The characteristics of the high pass filter are determined by the values of said mask matrix, and are illustrated in Figure 3.

Next, an estimator in the form of a probability distribution 36 of the histogram is obtained. Since the histogram of the high-pass-filter image describes an expotential distribution, the appropriate estimator is obtained by computing the second memory 38 of the probability distribution 36 corresponding to the histogram. Each element of the probability distribution

P [i] can be adequately estimated by dividing H (i) by the total mass of the total histogram mass M, where M is calculated as :-

Given probability of occurrence P, the second moment T is defined by the equation

In the second of these two equations the value T defines the textone feature and the appearance in the equation of the value (i + 1) is because P [0] describes the probability of the occurrence of pixels at locations of zero brightness gradient. Such information is significant and is therefore taken into account by a non-zero coefficient when index i = 0. A suitable computer program is preferably used to compute the equation.

The mean 26, variance 28 and textone 30 features are used to model the tone of the workpiece 3. This is used in several possible embodiments.

A first embodiment measures the features of two standard reference samples of different tones, called master tiles. These features are stored in the control processor 10. At a subsequent time during production, the control processor compares the features measured from each workpiece with the stored features using a simple Euclidian distance metric. The tone of the workpiece is assigned from the tone of the master tile it most closely resembles according to the distance metric.

A second embodiment measures the features of tiles being produced and computes a trend of the feature values. Measurements that are significantly deviant from the trend (say more than 2.5 standard deviations) are assigned to an alternative tone class.

Therefore, there has been disclosed herein a method and apparatus in accordance with the present invention in which the control processor is a stand alone, or also in which the control processor is connected to a supervisory controller.

Although particularly suited to determining the tone grading of ceramic tiles, the method of the present invention can be applied to determining the tone grading of other flat materials such as wood, metal, plastics, composites and glasses, wherein the workpiece is transported past the apparatus on a conveyor belt.

Though the invention has been shown and described with respect to exemplary embodiments thereof, various other changes, omissions and additions in the form and detail thereof may be made therein without departing from the scope of the invention.

Claims

1. A method of classifying workpieces according to their tonal variations, said method comprising capturing an electronic image of a workpiece, analysing said electronic image to derive a histogram of tonal variations in the image, and deriving a numerical representation of the tonal variations in said workpiece by use of an algorithm operative upon probabilities derived from said histogram.

2. A method as claimed in claim 1, comprising moving the workpiece by moving means past a workstation, capturing the electrical image utilising image retaining means mounted on the workstation, storing the image of the workpiece in a control processor and utilising the control processor to compute the equation

so as to determine the textone feature T of the workpiece, P representing the probability of occurrence, and comparing the resulting workpiece textone with a predetermined stored textone so as to determine the similarities between textones and thereby ensure the workpiece is directed to a specific collection point in which all workpieces with the same substantially identical textones are collected.

3. A method as claimed in claim 1 or 2, comprising detecting the presence of the workpiece prior to collection of the electrical image by the image retaining means.

4. A method as claimed in any preceding claim, comprising computing mean and variance features of a region of interest of the workpiece and utilising the results to assist in computing the textone feature of the region of interest.

5. A method as claimed in any preceding claim, comprising generating a histogram.

6. A method as claimed in claim 5, including indexing the histogram from 0 to 255.

7. A method as claimed in claim 5 or 6, comprising passing the image data signals to a digital high-pass filter.

8. A method as claimed in any preceding claim, comprising computing, for each picture element (pixel) of a region of interest of the workpiece, a convolution of that region surrounding the pixel with a given mask matrix.

9. A method as claimed in claim 8, wherein the convolution gives a numerical result in the range-255 to 255.

10. A method as claimed in any preceding claim, including computing an estimator of the histogram.

11. A method as claimed in claim 10, comprising locating the appropriate estimator by computing a second moment of the probability distribution corresponding to the histogram.

12. A method as claimed in claim 10 or 11, comprising estimating the probability of occurrence P [i] for each element by dividing H [i] by the total histogram mass M, where M is computed by

13. A method as claimed in any preceding claim, wherein the probability of occurrence of pixels occurs at locations of zero brightness gradient.

14. A method as claimed in any preceding claim wherein analysing said electronic image includes utilising a central processor to compare the features measured from each workpiece with stored features using a simple Euclidian distance metric.

15. A machine sensing system comprising capturing means for obtaining an electronic image of a workpiece, analysing means for analysing said electronic image to derive a histogram of tonal variations in the image, and means for deriving a numerical representative of the tonal variations in said workpiece by use of an algorithm operative upon statistics.

16. A system as claimed in claim 15, comprising image retaining means mounted on the workstation for creating an electrical image of the workpiece, control processor means for storing the electrical image of the workpiece and for computing the equation

so as to determine the textone feature T of the workpiece, P representing the probability of occurrence, and comparator means for comparing the workpiece textone with a predetermined stored textone so as to determine similarities between textones and thereby ensure the workpiece is directed to a specific collection point in which all workpieces of the same substantially identical textones are collected.

17. A system as claimed in claim 15 or 16, comprising detecting means for detecting the presence of a workpiece in the field of view of the image retaining means.

18. A system as claimed in claim 16 or 17, including computing means in the central processor for computing the mean and variance features of a region of interest of the workpiece and utilising the results to assist in computing the textone feature of the region of interest.

19. A system as claimed in any of claims 16 through 18, comprising means for generating a histogram.

20. A system as claimed in claim 19, wherein the histogram is indexed from 0 to 255.

21. A system as claimed in any of claims 16 through 20, comprising a digital high-pass filter through which image data signals are arranged to pass.

22. A system as claimed in any of claims 16 through 21, comprising means for computing each picture element of the region of interest, a convolution of a region surrounding the pixel with a given mask matrix.

23. A system as claimed in claim 22, wherein the convolution gives a numerical result in the range-255 to 255.

24. A system as claimed in any of claims 16 through 23, comprising means for computing an estimator of the histogram.

25. A system as claimed in any of claims 16 through 24, comprising means locating the appropriate estimator by computing a second moment of the probability distribution corresponding to the histogram.

26. A system as claimed in any of claims 16 through 25, comprising

means for estimating the probability of occurrence P [i] for each element by dividing H [i] by the total histogram mass M, where M is computed by

27. An apparatus for inspecting tonal variations in a workpiece, comprising capturing means for capturing an electronic image of a workpiece, analysing means for analysing said electronic image to derive a histogram of tonal variations in the image, and deriving means for deriving a numerical representation of the tonal variations in said workpiece by use of an algorithm operative upon probabilities derived from said histogram.

28. An apparatus as claimed in claim 27, comprising means for moving a workpiece past a workstation, image retaining means mounted on the workstation for capturing an electrical image of the workpiece, control processor means for storing the electrical image of the workpiece and for computing the equation

29. A method of inspecting the surface tone of a workpiece substantially as hereinbefore described with reference to and as illustrated in Figures 1 to 3 of the accompanying drawings.

30. A machine sensing system substantially as hereinbefore described with reference to and as illustrated in Figures 1 to 3 of the accompanying drawings.

31. An apparatus for inspecting the surface tone of a workpiece substantially as hereinbefore described with reference to and as illustrated in Figures 1 to 3 of the accompanying drawings.