Method of and apparatus for determining if a given data point falls within a population of data points

Abstract: A method of and apparatus for determining if a given data point having three parameters falls within a population defined by more than four known data points, each data point having the three parameters, the method and apparatus involving determining if the given data point falls within any of a plurality of tetrahedrons, each of the tetrahedrons having four vertices, each of the vertices of each tetrahedron being defined by one of the known data points.

Description: BACKGROUND OF THE INVENTION The invention relates to a method and apparatus for determining if an unfamiliar data point representing various parameters should be classified in a category (e.g. "acceptable"), given a population defined by known data points representing the same parameters, which known data points are considered to belong in that category with regard to those parameters. The invention also has application in the field of color recognition (color analysis). Manufacturers of colored articles, for example, presently require pigment (or color dye) suppliers to provide samples of pigments to the manufacturer and/or to the buyer of the articles. A human being then visually determines which pigment is acceptable for the articles that the manufacturer is planning to manufacture. The pigment supplier then supplies the pigment to the manufacturer who then proceeds to manufacture the articles, possibly after first visually inspecting the pigment to ensure that it matches the sample. After the articles are manufactured, the finished article is again visually inspected for acceptability with regard to color. This is because during the manufacturing process, particularly if the process requires heat (such as in an injection molding process), an error in controlling the process may cause the color of the finished article to deviate from the desired color. The buyer of the finished article may also perform a visual inspection with regard to color. Each of the visual inspections performed before, during, and after the manufacture of the finished article involve subjectivity, and different color experts may have different opinions as to whether or not a colored article is acceptable with regard to color. Further, each visual inspection involves the cost of using a color expert. The prior art provides algorithms for determining if a unfamiliar data point representing various parameters should be deemed "acceptable" given a population of data points that represent the various parameters and that have been determined to be "acceptable". With regard to color data points representing three parameters (for example, a red/green parameter (a*), a yellow/blue parameter (b*), and a lightness parameter (L*)), a prior art method for determining if an unfamiliar data point should be acceptable given a population of known acceptable data points would attempt to fit an ellipsoid around the known acceptable data points and would determine if the unfamiliar data point lies within the ellipsoid to determine if the unfamiliar data point should be acceptable. More particularly, since it is difficult to fit a three dimensional ellipsoid around known acceptable data points, the prior art method will usually define three ellipses, one in each of the three two-dimensional planes a*-b*, b*-L*, and a*-L*. The reason that an elliptical shape was used in the prior art method is that this shape takes into account the fact that a greater amount of deviation may be tolerable for one of the parameters than for another of the parameters. A problem with the prior art approach is that many known acceptable data points are required. The method will not work when there are only a few known acceptable data points. Another problem with this prior art approach is that the ellipsoid that is defined may include regions that would contain unacceptable data points. Attention is directed to the following reference, which discusses some statistical techniques for solving classification problems: Johnson, Richard A., and Dean W. Wichern, Applied Multivariate Statistical Analysis , Prentice Hall, Englewood Cliffs, NJ, Second Edition, pp. 470-531. It should be noted that the techniques disclosed in Applied Multivariate Statistical Analysis generally are useful only for populations that are normally distributed. SUMMARY OF THE INVENTION One embodiment of the invention provides a method of determining if a given data point representing sensed color and having three parameters should be considered to be "acceptable", the method comprising the following steps: (a) providing a database containing a first population of at least four data points that each represent a color known to have been classified as "acceptable", and containing a second population of data points that each represent a color known not to have been classified as "acceptable"; (b) determining, after step (a), which of the data points of the second population is closest to the given data point; (c) defining, after step (b), an ellipsoidal neighborhood around the closest data point of the second population; (d) determining, after step (c) if the given data point is in the ellipsoidal neighborhood defined in step (c), and if so, indicating that the given data point can not be automatically considered to be "acceptable", and if not, proceeding to step (e); (e) determining which of the data points of the first population is closest to the given data point; (f) defining, after step (e), an ellipsoidal neighborhood around the closest data point of the first population; (g) determining, after step (f) if the given data point is in the ellipsoidal neighborhood defined in step (f), and if so, indicating that the given data point can automatically be considered "acceptable", and if not, proceeding to step (h); and (h) determining if the given data point falls within any of a plurality of tetrahedrons, each of the tetrahedrons having four vertices, each of the vertices of each tetrahedron being defined by one of the data points of the first population, and if so, indicating that the given data point can be automatically considered "acceptable", and if not, indicating that the given data point can not be automatically considered to be "acceptable". One embodiment of the invention provides an apparatus for determining if a given data point having at least three parameters falls within a population defined by at least four known data points, each data point having the three parameters, the apparatus comprising: structure for defining a tetrahedron having four vertices, each of the vertices of the tetrahedron being defined by one of the known data points; and structure for determining if the given data point lies within the tetrahedron. One embodiment of the invention provides an apparatus for determining if a given data point, having a predetermined number n of parameters, falls within a population defined by at least n+1 known data points, each known data point having the n parameters, the apparatus comprising: structure for defining a first vector from the given data point to a point on a surface defined by n points of the nil known points; structure for determining the component of the first vector along the normal to the surface defined by the n points; structure for defining a second vector from the n+1 point to the surface defined by the n points; structure for calculating the dot product of the component of the first vector along the normal and the second vector; and structure for determining whether the calculated dot product is positive or negative. The invention provides a method for classifying data in any n-dimensional space. The invention has application to many classification problems including color analysis. The invention can be used with any number of classifications, and any number of parameters. The invention can be used to solve classification problems where populations are not normally distributed, such as the problem of Fig. 11.13 on p. 530 of Applied Multivariate Statistical Analysis . With regard to color, the method of the invention is advantageous in that only a few data points that have previously classified as "acceptable" are needed. The method is conservative and minimizes the probability of accepting a "marginal" or "rejected" color. Further, with regard to color, the method is a true three dimensional approach and does not suffer any of the problems that can occur when trying to fit two-dimensional ellipses around data points in three dimensional space. The method is also superior to any method that attempts to fit a large three dimensional ellipsoid around data points classified as "acceptable" since those large ellipsoids by their symmetrical nature inherently include substantial regions where there are no data points, resulting in a much higher risk of classifying a given data point as "acceptable" when it should be classified as "rejected" or "marginal". Other features and advantages of the invention will become apparent to those of ordinary skill in the art upon review of the following detailed description, claims, and drawings. BRIEF DESCRIPTION OF THE VIEWS OF THE DRAWINGS Fig. 1 is a block diagram of an apparatus in accordance with the present invention. Figs. 2a-2f show a flowchart illustrating a method in accordance with the invention and carried out by the apparatus of Fig. 1. Fig. 3 is a flowchart illustrating in detail a set of steps that are repeated in one of the steps in the method illustrated in Figs. 2a-2f. Before one embodiment of the invention is explained in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments and of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. It is to be understood that in equations used in the following description of one embodiment of the invention, the symbol "*" is used to denote regular multiplication (other than when used in conjunction with the color convention a*, b*, L*), "″" is used to denote a vector dot product, and "/" is used to denote regular division. Further, in equations used in the following description of one embodiment of the invention; "i", "j", "k", and "l" are use to represent unit vectors along axes in an orthogonal coordinate system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT OF THE INVENTION Although the invention will be disclosed herein using a color analysis application, it should be kept in mind that the invention has application in all fields where it is desired to classify new information based on prior experience. The invention can be used, for example, to determine which of a set of potential university students that each have qualifications defined by a data point having a high school grade parameter, a standardized test score parameter, a parameter representing the student's rank in the high school, and a parameter representing the high school's rank among high schools should be classified as "acceptable", "not acceptable", or "marginal", with regard to appropriateness for admission into the university, based on the university's previous experience. The invention can be used to classify insurance companies as "solvent" or "distressed" using the following parameters: total assets, cost of stocks and bond, loss expenses, surplus, and amount of premiums. The invention can be used to classify home-owners as being "riding-mower owners" or "not riding mower owners" based on the parameters of lot size and income. The invention can be used to classify credit applicants as being a "good risk" or a "bad risk" based on the following parameters: income, age, number of credit cards, and family size. The invention can be used to classify consumers as being "likely to purchase new products" or "relucant to purchase new products" base on the following parameters: education, family size, income, and amount of previous brand switching. The invention can also be used to solve the problem set forth in the Background of the Invention relating to the field of color recognition (color analysis). Classification problems are set forth in Applied Multivariate Statistical Analysis . Other applications will become apparent to those skilled in the art. As will become apparent, the invention can be used with any number of parameters, and any number of classifications. The classifications need not be related to acceptability. Unlike methods disclosed in Applied Multivariate Statistical Analysis, the invention can be used with populations that are not normally distributed. Shown in Fig. 1 is a system 10 for determining if a given data point, having a predetermined number n of parameters, falls within a population defined by at least n+1 known data points, each known data point having the n parameters. The system 10 comprises a microprocessor. In the preferred embodiment, the system 10 comprises a microprocessor driven computer 12. While various types of computers can be used, in the illustrated embodiment an IBM compatible personal computer is used. The computer 12 is a typical computer including memory (not shown), a screen 14, and optionally including a printer 16. The system 10 further comprises means for defining the given data point by measuring the n parameters for the given data point. When the invention is used in the field of color analysis, there are three parameters, and the means for measuring the three parameters for the given data point comprises means for measuring three parameters relating to color. While various other color systems exist, for purposes of example, it will be assumed herein that the three parameters consist of a red/green parameter (a*), a yellow/blue parameter (b*), and a lightness parameter (L*). In the color analysis application, the means for measuring the parameters comprises a measuring apparatus 18 selected from the group consisting of a colorimeter, a spectrophotometer, and an abridged spectrophotometer. The system 10 further includes an analog to digital converter 20 that connects, via communications cables (not shown), the measuring apparatus 18 to the computer 12. The computer 12 is programmed to carry out the method illustrated in Figs. 2a-2f and in accordance with the invention. While various computer programming languages can be employed, the illustrated embodiment of the invention was programmed using a high level programming language. More particularly, the illustrated embodiment of the invention was programmed using the BASIC programming language. The method comprises the step, illustrated as S1, of providing a database containing a first population of at least n+1 data points, each representing color samples for example, which data points are known to have each been classified in a first category ("acceptable", for example), and the database optionally containing a second population of data points having the same set of parameters as the points in the first population, which data points in the second population are known to have been classified in a second category ("non-acceptable", for example). Data points in the database would reflect classification based on actual past experience. With regard to the color analysis application, the classification of data points in the database would reflect prior visual inspections by a human color expert. The color expert, based on a visual inspections, would have classified a color sample as "acceptable" (first population), or would have classified the color sample as "not acceptable" (second population). The parameters of this color sample would than have been entered into the database in a location associated with "acceptable", data points, if the color sample was determined to be "acceptable" or in another location if the color sample was not determined to be "acceptable". The database could be defined on one or more floppy discs, on a hard drive included in the computer 12, or in memory in the computer 12. The method further comprises, after step S1, the step, illustrated as step S2, of measuring n parameters using a measuring apparatus, to define a given data point (x 1 , x 2 , x 3 , ...x n ). Using the color analysis application, in the step S2, the measuring apparatus 18 is used to measure the three parameters a*, b*, and L* for a color sample to define a given data point (x 1 , x 2 , x 3 ) where x 1 represents the value of the a* parameter measured by the measuring apparatus 18, x 2 represents the value of the b* parameter measured by the measuring apparatus 18, and x 3 represents the value of the L* parameter measured by the measuring apparatus 18. The method further comprises, after step S2, steps S3 and S4, which will be described below, and which are repeated for each population. Using the color analysis application, steps S2 and S3 will first be performed for the data points in the "acceptable" classification, and will then be performed for the "not acceptable" classification. In step S3, the respective means for each of the parameters for all the data points in the population are calculated. Using the color analysis application, in step S3, the respective means are calculated for each of the parameters a*, b*, and L* for the data points in the classification. Also, using the color analysis application, in Fig. 2a-2f, the means calculated for the a* parameter for all the data points in the population is denoted as x 1 , the mean calculated for the b* parameter for all the data points in the population is denoted as x 2 , and the mean calculated for the L* parameter for all the data points in the population is denoted as x 3 . The method further comprises, after step S3, the step, illustrated as step S4, of using the respective means x 1 , x 2 , x 3 , ... x n calculated in step S3 for calculating respective standard deviations sd 1 , sd 2 , sd 3 , ...sd n for each of the parameters for all the data points in the population. It should be noted that when one population surrounds another population, such as in the color analysis application where rejected data points surround accepted points, standard deviations need only be calculated for the inner population. When one population surrounds another population, the standard deviations calculated for the inner population are always used when standard deviations are called for in a formula in the following steps. If one population does not surround another population, the standard deviations for the relevant population are used when standard deviations are called for in a formula in the following steps. For example, in step S6 described below, the normalized distance from the given data point (x 1 , x 2 , x 3 , ...x n ) to a point (r 1 , r 2 , r 3 , ...r n ) in the second population is calculated. If one population does not surround another population, the standard deviations sd 1 , sd 2 , sd 3 , ...sd n referred to in step S6 are the standard deviations for the second population. If one population surrounds another, such as in the color analysis application, the standard deviations sd 1 , sd 2 , sd 3 , ...sd n referred to in step S6 are the standard deviations for inner population (the first population in the color analysis application). In the color analysis application, the standard deviation calculated for the first parameter (a*) for all the data points in the population is denoted as sd 1 , the standard deviation calculated for the second parameter (b*) for all the data points in the population is denoted as sd 2 , and the standard deviation calculated for the third parameter (L*) for all the data points in the population is denoted as sd 3 . The method further comprises, after the loop comprising steps S3 and S4, the step, illustrated as step S4.1, of determining if any of the parameters (a*, b*, L* in the color analysis application) is closely correlated to another one of the parameters (a*, b*, L* in the color analysis application). This determination is made, in the preferred embodiment, by performing the following steps for each population. First, sample convariances are calculated using the following formula: where n is the number of parameters, where p is the number of points in the population, where the first subscript of x and the subscript of X represents the number of a parameter, where the second subscript of x represents the number of a data point, and where x represents the mean of the parameter designated by the subscript thereof for all the data points in the population. Using the color analysis application, assuming that there are twenty data points in one of the populations, the sample covariance of the a* parameter with respect to the b* parameter will be: S a*b* = (1/20)(Σ j (x a*j -X a* )(x b*j -X b* ) j=1, 2, 3, ...20) Once sample covariances are calculated for each possible combination of parameters for the population, sample correlation coefficient: R ik are calculated as follow: R ik = S ik /(S ii *S kk ) 0.5 Using the color analysis application, the sample covariance of the a* parameter with respect to the b* parameter will be: R a*b* = S a*b* /(S a*a* *S b*b* ) 0.5 If the absolute value of any of the calculated covariances for any of the populations is greater than a predetermined value, 0.25 in the preferred embodiment of the invention, than the determination that is made in step S4.1 is that one of the parameters is closely correlated to another one of the parameters. The method further comprises, after step S4.1 if it is determined in step S4.1 that one of the parameters is closely correlated to another one of the parameters, the step, illustrated as step S4.2, of transforming coordinate systems to the principal component system. This transformation advantageously avoids the necessity of the use of large matrices and of tensors in the steps that follow. Each data point in each population and the given data point are transformed, in the preferred embodiment, using eigenvalue-eigenvector pairs. Methods of calculating eigenvalues and eigenvectors are well known in the art and are discussed in references relating to numerical techniques, such as: Conte, S.D., and Carl de Boor, Elementary Numerical Analysis, McGraw-Hill, Net York, Second Edition, 1972; and Morrison, D.F., Multivariate Statistical Methods, McGraw-Hill, New York, Second Edition, 1976. Eigenvalue-eigenvector pairs (β 1 , e 1 ), (β 2 , e 2 ), (β 3 , e 3 ), ...(β n , e n ) are calculated where β 1 ≥ β 2 ≥ β 3 ≥ ...≥ β n ≥ 0, and where n is the number of parameters. The data points are transformed to the principal component system using the following equation: Y i = e 1i x 1 +e 2i x 2 +e 3i x 3 +...+e ni x n i = 1, 2, 3, ...n where n is the number of parameters, where the subscript of x and of Y is the number of a parameter, where (x 1 , x 2 , x 3 , ...x n ) is a data point to be transformed, where (Y 1 , Y 2 , Y 3 , ...Y n ) is the transformed data point, and where e i is eigenvector having components (e 1i, e 2i , e 3i , ...e ni ). Using the color analysis application, a data point (x 1 , x 2 , x 3 ), where x 1 represents the value of the a* parameter, where x 2 represents the value of the b* parameter, and where x 3 represents the value of the L* parameter, is transformed to a data point (Y 1 , Y 2 , Y 3 ) in the principal coordinate system, where Y 1 represents the value of the a* parameter, where Y 2 represents the value of the b* parameter, and where Y 3 represents the value of the L* parameter, as follows: Y 1 = e 11 x 1 +e 21 x 2 +e 31 x 3 Y 2 = e 12 x 1 +e 22 x 2 +e 32 x 3 Y 3 = e 13 x 1 +e 23 x 2 +e 33 x 3 Respective standard deviations sdY 1 , sdY 2 , sdY 3 , ...sdY n of the transformed variables Y 1 , Y 2 , Y 3 , ...Y n are then calculated by taking the respective square roots of the eigenvalues β 1 , β 2 , β 3 , ...β n. After a data point (x 1 , x 2 , x 3 , ...x n ) has been transformed to a point (Y 1 , Y 2 , Y 3 , ...Y n ), all of the following steps are performed using the transformed data point (Y 1 , Y 2 , Y 3 , ...Y n ) as if it were a data point (x 1 , x 2 , x 3 , ...x n ). In other words, if there has been a transformation in step 4.2 to the principal coordinate system for all the data points in each population and for the given data point, in all formulas that involve a data point (x 1 , x 2 , x 3 , ...x n ) in subsequent steps (described below), the corresponding transformed data point (Y 1 , Y 2 , Y 3 , ...Y n ) is to be used in place of the data point (x 1 , x 2 , x 3 , ...x n ). Similarly, if there has been a transformation in step 4.2 to the principal coordinate system for all the data points in each population and for the given data point, in all formulas that involve standard deviations sd 1 , sd 2 , sd 3 , ...sd n in subsequent steps (described below), the standard deviations sdY 1 , sdY 2 , sdY 3 , ...sdY n are to be used in place of the standard deviations sd 1 , sd 2 , sd 3 , ...sd n . The method further comprises, after step S4.1 if it is determined in step S4.1 that none of the parameters are closely correlated to one another, or after step S4.2 if it is determined in step S4.1 that one of the parameters is closely correlated to another parameter, the step, illustrated as step S5, of determining if there is a second population. If, in step S5, it is determined that there are no points in the second population, a jump is made to step S11 described below. The method further comprises, after step S5 if it is determined in step S5 that there are data points in the second population, the step, illustrated as step S6, of calcultating the normalized distance of the given data point (x 1 , x 2 , x 3 , ...x n ) from each data point in the second population ("not acceptable" data points in the color analysis application). The distance is normalized using the calculated standard deviations sd 1 , sd 2 , sd 3 , ...sd n . Using the color analysis application, assuming that the a*, b*, and L* values for one of the data points in the second population are r 1 , r 2 , and r 3 , respectively, the normalized distance d from the given data point (x 1 , x 2 , x 3 ) to the data point (r 1 , r 2 , r 3 ) in the second population is calculated using the following formula: d =((x 1 -r 1 ) 2 /sd 1 2 +(x 2 -r 2 ) 2 /sd 2 2 +(x 3 -r 3 ) 2 /sd 3 2 ) 0.5 In an application where there are another than three parameters, the normalized difference from a given data point (x 1 , x 2 , x 3 , ...x n ) to a point (r 1 , r 2 , r 3 , ...r n ) in the second population is calculated using the formula: d = ((x 1 -r 1 ) 2 /sd 1 2 +(x 2 -r 2 ) 2 /sd 2 2 +(x 3 -r 3 ) 2 /sd 3 2 +...(x n -r n ) 2 /sd n 2 ) 0.5 The method further comprises, after step S6, the step, illustrated as step S7, of determining which of the data points of the second population is closest to the given data point (x 1 , x 2 , x 3 , ...x n ). This is done by determining which data point in the second population is associated with the smallest distance d calculated in step S6. The method further comprises, after step S7, the step, illustrated as step S8, of determining if the given data point (x 1 , x 2 , x 3 , ...x n ) is in a neighborhood surrounding the data point determined in step S7 to be closest to the given data point (x 1 , x 2 , x 3 , ...x n ). Using the color analysis application, assuming that the a*, b*, and L* values for the data point determined in step S7 to be the data point in the second population that is closest to the given data point (x 1 , x 2 , x 3 ) are nr 1 , nr 2 , and nr 3 , respectively, then the given data point (x 1 , x 2 , x 3 ) is in an ellipsoidal neighborhood surrounding the closest data point (nr 1 , nr 2 , nr 3 ) if a distance dist is less than or equal to 1, where dist is defined as: dist = ((x 1 -nr 1 ) 2 /(c*sd 1 ) 2 +(x 2 -nr 2 ) 2 /(c*sd 2 ) 2 + (x 3 -nr 3 )) 2 /(c*sd 3 ) 2 ) 0.5 The principal axes of this ellipsoidal neighborhood are defined to be c*sd 1 , c*sd 2 , and c*sd 3 in the a*, b*, and L* directions, respectively, where the preferred value of c is 0.3 in the color analysis application. The principal axes of the ellipsoidal neighborhood are advantageously chosen to be functions of the standard deviations of the respective parameters for all the data points of the first population to take into the account the fact that a greater amount of deviation may be acceptable for one parameter than for another parameter. In the color analysis application, the constant c used to multiply sd 1 , sd 2 , and sd 3 was selected to be 0.3 for the following reason. It is generally accepted that the human eye cannot distinguish the difference between a first color and a second color if the square root of the sum of the squares of the differences in respective a*, b*, and L* values of the first color and second color is less than 0.4 (ΔE<0.4 where ΔE=((Δa*) 2 +(Δb*) 2 +(ΔL*) 2 ) 0.5 ) . The inventors of the present invention have determined that using the value 0.3 for the constant c results in a ΔE of less than 0.4 for almost all colors. The values of c can be different from 0.3 as long this results in ΔE being close to 0.4 for all colors. In an application where there are other than three parameters, a given data point (x 1 , x 2 , x 3 , ...x n ) is in a neighborhood around the closest data point (nr 1 , nr 2 , nr 3 , ...nr n ) in the second population if a distance dist is less than or equal to 1, where dist is defined as: dist = ((x 1 -nr 1 ) 2 /(c*sd 1 ) 2 +(x 2 -nr 2 ) 2 /(c*sd 2 ) 2 + (x 3 -nr 3 ) 2 /(c*sd 3 ) 2 + ...(x n -nr n ) 2 /(c*sd n ) 2 ) 0.5 For applications other than color analysis, a general method of choosing the constant c is used in the preferred embodiment of the invention as follows. First, a value of c is chosen arbitrarily. Second, the formula dist = ((x 1 -nr 1 ) 2 /(c*sd 1 ) 2 +(x 2 -nr 2 ) 2 /(c*sd 2 ) 2 + (x 3 -nr 3 ) 2 /(c*sd 3 ) 2 +...(x n -nr n ) 2 /(c*sd n ) 2 ) 0.5 is used repeatedly to calculate how many data points in each of the first and second populations are in a neighborhood around a data point in the first population. The second step is repeated by defining neighborhoods around each data point in the first population. Third, the calculated information is used to calculate the probability of misclassifying a data point in the second population as being in the first population. The first through the third steps are repeated for different values of c until a value of c is found that provides an acceptable probability of misclassification. Steps S6, S7, and S8 determine whether or not the given data point is in close proximity to one of the data points in the second population. If, in step S8, the value of dist is less than or equal to 1, then, at step S9, the method provides an indication at the screen 14 and/or the printer 16 that is appropriate given that the data point most likely belongs to the second population. In the color analysis application, where the second population contains data points that are "not acceptable," the method provides an indication at the screen 14 and/or the printer 16 that the color represented by the given data point (x 1 , x 2 , x 3 ) requires visual inspection. The method further comprises, after step S8 if the given data point (x 1 , x 2 , x 3 , ...x n ) is determined to not be in the neighborhood defined in step S8, the step, illustrated as step S8.1 of selecting the data points in the second population that are in a neighborhood defined around the given data point (x 1 , x 2 , x 3 , ...x n ). Using the color analysis application, assuming that the a*, b*, and L* values for a data point in the first population are a 1 , a 2 , and a 3 , respectively, then the data point (a 1 , a 2 , a 3 ) is in a large ellipsoidal neighborhood surrounding the given data point (x 1 , x 2 , x 3 ) if a distance dist4 is less than or equal to 1, where sd 1 , sd 2 , and sd 3 are the standard deviations calculated in step S4, and where dist4 is defined as: dist4 = ((x 1 -a 1 ) 2 /(c4*sd 1 ) 2 +(x 2 -a 2 ) 2 /(c4*sd 2 ) 2 + (x 3 -a 3 ) 2 /(c4*sd 3 ) 2 ) 0.5 The principal axes of this ellipsoidal neighborhood are defined to be c4*sd 1 , c4*sd 2 , and c4*sd 3 in the a*, b*, and L* directions, respectively, where the preferred value of c4 in the color analysis application is 1. As was the case in step S8, the principal axes of the ellipsoidal neighborhood are advantageously chosen to be functions of the standard deviations of the respective parameters for all the data points of the first population to take into the account the fact that a greater amount of deviation may be acceptable for one parameter than for another parameter. The value 1 for the constant c4 used to multiply sd 1 , sd 2 , and sd 3 was selected using reasoning similar to the reasoning used to select the value 0.3 for the constant c in step S8, but so as to define an ellipsoid that is relatively large in area. In an application involving data points representing something other than color, a data point (a 1 , a 2 , a 3 , ...a n ) is in a neighborhood around the given data point (x 1 , x 2 , x 3 , ...x n ) in the second population if a distance dist4 is less than or equal to 1, where dist4 is defined as: dist4 = ((x 1 -a 1 ) 2 /(c4*sd 1 ) 2 +(x 2 -a 2 ) 2 /(c4*sd 2 ) 2 + (x 3 -a 3 ) 2 /(c4*sd 3 ) 2 + ...(x n -a n ) 2 /(c4*sd n ) 2 ) 0.5 For applications other than color analysis, the constant c4 should be chosen such that the defined neighborhood defines an area of acceptable deviance. More particularly, the general method of choosing the constant c in step S8 for applications other than color analysis is used to select a value for the constant c4. Step S8.1 limits the size of the n+1 surfaced figures defined in step S8.3 described below. The method further comprises, after step S8.1, the step illustrated as step S8.2 of determining whether or not at least n+1 data points were selected in step S8.1. If at least n+1 data points were not selected in step S8.1, a jump is made to step S17 described below. The method further comprises, after step S8.2 if it is determined in step S8.2 that at least n+1 data points were selected in step S8.1, the step, illustrated as step S8.3, of determining whether or not the given data point lies inside any n+1 surfaced figure formed by joining with straight lines any n+1 of the data points selected in step S8.1, where n is the number of parameters. In the color analysis application, the determination made in step S8.3 is whether or not the given data point lies inside any one of the tetrahedrons defined by all possible combinations of four of the data points selected in step S8.1. In an application involving data points representing something other than color, where there are other than three parameters, the determination made in step S8.3 is whether or not the given data point (x 1 , x 2 , x 3 , ...x n ) lies inside any one of the n+1 surfaced figures defined by all possible combinations of n+1 of the data points selected in step S8.1. The method of step S8.3 of determining whether or not the given data point lies inside a n+1 surfaced figure is identical to the method of step S15, described below in conjunction with Fig. 3. If, in step S8.3, it is determined that the given data point lies inside one of the b+1 surfaced figures defined by all possible combination of n+1 of the data points selected in step S8.1, then, at step S8.4, the method provides an indication at the screen 14 and/or the printer 16 that is appropriate given that the data point most likely belongs to the second population. In the color analysis application, where the second population contains data points that are "not acceptable," the method provides an indication at the screen 14 and/or the printer 16 that visual inspection of the color represented by the given data point (x 1 , x 2 , x 3 ) is necessary. Optionally, the method can be used with more than two classifications. In the color analysis application, the database defined in step S1 can include data points that each represent the a*, b*, and L* values of colors classified as "marginal", and after step S8, if the given data point was not determined to be in a neighborhood defined around the data point in the second population determined to be closest to the given data point, the steps shown in Fig. 2c and Fig. 2d can be repeated as if these "marginal" data points defined the second population. The method further comprises, after step S8.3 if it is determined in step S8.3 that the given data point is not inside any figure defined by joining with straight lines any n+1 of the data points selected in step S8.1, or after step S8.2 if it is determined in step S8.2 that at least n+1 data points were not selected in step S8.1, the step, illustrated as step S10, of calculating the normalized distance of the given data point (x 1 , x 2 , x 3 , x n ) from each data point in the first population. The distance is normalized using the standard deviations sd 1 , sd 2 , sd 3 , ...sd n calculated is step S4. In the color analysis application, assuming that the a*, b*, and L* values for one of the data points in the first population are a 1 , a 2 , and a 3 , respectively, the normalized distance d2 from the given data point (x 1 , x 2 , x 3 ) to the point (a 1 , a 2 , a 3 ) in the first population is calculated using the following formula: d2 = ((x 1 -a 1 ) 2 /sd 1 2 +(x 2 -a 2 ) 2 /sd 2 2 +(x 3 -a 3 ) 2 /sd 3 2 ) 0.5 In an application where there are other than three parameters, the normalized distance d2 from a given data point (x 1 , x 2 , x 3 , ...x n ) to a point (a 1 , a 2 , a 3 , ...a n ) in the first population is calculated using the formula: d2 = ((x 1 -a 1 ) 2 /sd 1 2 +(x 2 -a 2 ) 2 /sd 2 2 +(x 3 -a 3 ) 2 /sd 3 2 +...(x n -a n ) 2 /sd n 2 ) 0.5 The method further comprises, after step S10, the step, illustrated as step S11, of determining which of the data points of the first population is closest to the given data point (x 1 , x 2 , x 3 , ...x n ). This is done by determining which data point in the first population is associated which the smallest distance d2 calculated in step S10. The method further comprises, after step S11, the step, illustrated as step S12, of determining if the given data point (x 1 , x 2 , x 2 , ...x n ) is in a neighborhood surrounding the data point determined in step S11 to be closest to the given data point (x 1 , x 2 , x 3 , ...x n ). Using the color analysis application, assuming that the a*, b*, and L* values for the data point determined in step S11 to be the data point in the first population that is closest to the given data point (x 1 , x 2 , x 3 ) are na 1 , na 2 , and na 3 , respectively, then the given data point (x 1 , x 2 , x 3 ) is in an ellipsoidal neighborhood surrounding the closest data point (na 1 , na 2 , na 3 ) if a distance dist2 is less than or equal to 1, where sd 1 , sd 2 and sd 3 are the standard deviations calculated in step S4, and where dist2 is defined as: dist2 = ((x 1 -na 1 ) 2 /(c2*sd 1 ) 2 +(x 2 -na 2 ) 2 /(c2*sd 2 ) 2 + (x 3 -na 3 )) 2 /(c2*sd 3 ) 2 ) 0.5 The principal axes of this ellipsoidal neighborhood are defined to be c2*sd 1 , c2*sd 2 , and c2*sd 3 in the a*, b*, and L* directions, respectively, where the preferred values of c2 is 0.3 in the color analysis application. As was the case in step S8, the principal axes of the ellipsoidal neighborhood are advantageously chosen to be functions of the standard deviations of the respective parameters for all the data points of the first population to take into the account the fact that a greater amount of deviation may be acceptable for one parameter than for another parameter. The value 0.3 for the constant c2 used to multiply sd 1 , sd 2 , and sd 3 in the color analysis application was selected using reasoning analogous the reasoning used to select the value 0.3 for the constant c in the color analysis application in step S8. In an application involving data points where these are other than three parameters, a given data point (x 1 , x 2 , x 3 , ...x n ) is in a neighborhood around the closest data point (na 1 , na 2 , na 3 ...na n ) in the first population if a distance dist2 is less or equal to 1, where dist2 is defined as: dist2 = ((x 1 -na 1 ) 2 /(c2*sd 1 ) 2 +(x 2 -na 2 ) 2 /(c2*sd 2 ) 2 + (x 3 -na 3 ) 2 /(c 2 *sd 3 ) 2 +...(x n -na n ) 2 /(c2*sd n ) 2 ) 0.5 For applications other than color analysis, the constant c2 should be chosen such that the defined neighborhood defined an area acceptable deviance. More particularly, the general method of choosing the constant c in step S8 for applications other than color analysis is used to select a value for the constant c2. Steps S10, S11, and S12 determine whether or not the given data point is in close proximity to a data point in the first population. If, in step S12, the value of dist2 is less than or equal to 1, then, at step S13, the method provides an indication at the screen 14 and/or the printer 16 that is appropriate given that the data point most likely belongs to the first population. In the color analysis application, where the first population contains data points that are "acceptable," the method provides an indication at the screen 14 and/or the printer 16 that the color represented by the given data point (x 1 , x 2 , x 3 ) is likely acceptable. The method further comprises, after step S12 if the given data point (x 1 , x 2 , x 3 , ...x n ) is determined to not be in the neighborhood defined in step S12, the step, illustrated as step S14 of selecting the data points in the first population that are in a neighborhood defined around the given data point (x 1 , x 2 x 3 , ...x n ). Using the color analysis application, assuming that the a*, b*, and L* values for a data point in the first population are a 1 , a 2 , and a 3 , respectively, then the data point (a 1 , a 2 , a 3 ) is in a large ellipsoidal neighborhood surrounding the given data point (x 1 , x 2 , x 3 ), if a distance dist3 is less than or equal to 1, where sd 1 , sd 2 , and sd 3 are the standard deviations calculated in step S4, and where dist3 is defined as: dist3 = ((x 1 -a 1 ) 2 /(c3*sd 1 ) 2 +(x 2 -a 2 ) 2 /(c3*sd 2 ) 2 + (x 3 -a 3 ) 2 /(c3*sd 3 ) 2 ) 0.5 The principal axes of this ellipsoidal neighborhood are defined to be c3*sd 1 , c3*sd 2 , and c3*sd 3 in the a*, b* and L* directions, respectively, where the preferred value of c3 in the color analysis application is 1. As was the case in step S8, the principal axes of the ellipsoidal neighborhood are advantageously chosen to be functions of the standard deviations of the respective parameters for all the data points of the first population to take into the account the fact that a greater amount of deviation may be acceptable for one parameter than for another parameter. The value 1 for the constant c3 used to multiply sd 1 , sd 2 , sd 3 was selected using reasoning similar to the reasoning used to select the value 0.3 for the constant c in step S8, but so as to define an ellipsoid that is relatively large in area. In an application involving data points representing something other than color, a data point (a 1 , a 2 , a 3 , ...a n ) is in a neighborhood around the given data point (x 1 , x 2 , x 3 , ...x n ) in the first population if a distance dist3 is less than or equal to 1, where dist3 is defined as: dist3 = ((x 1 -a 1 ) 2 /(c3*sd 1 ) 2 +(x 2 -a 2 ) 2 /(c3*sd 2 ) 2 + (x 3 -a 3 ) 2 /(c3*sd 3 ) 2 +...(x n -a n ) 2 /(c3*sd n ) 2 ) 0.5 For applications other than color analysis, the constant c3 should be chosen such that the defined neighborhood defines an area of acceptable deviance. More particularly, the general method of choosing the constant c in step S8 for applications other than color analysis is used to select a value for the constant c3. The method further comprises, after step S14, the step illustrated at step S14.1 of determining whether or not at least n+1 data points were selected in step S14. If at least n+1 data points were not selected in step S14, a jump is made to step S17 described below. The method further comprises, after step S14.1 if it is determined in step S14.1 that at least n+1 data points were selected in step S14, the step, illustrated as step S15, of determining whether or not the given data point lies inside any n+1 surfaced figure formed by joining with straight lines any n+1 of the data points selected in step S14, where n is the number of parameters. In the color analysis application, the determination made in step S15 is whether or not the given data point lies inside any one of the tetrahedrons defined by all possible combinations of four of the data points selected in step S14. In an application involving data points representing something other than color, were there are other than three parameters, the determination made in step S15 is whether or not the given data point (x 1 , x 2 , x 3 , ...x n ) lies inside any one of the n+1 surfaced figures defined by all possible combinations of n+1 of the data points selected in step S14. When the method is used specifically with regard to color analysis, step S14 could optionally be omitted and a determination could be made in step S15 as to whether or not the given data point falls within a tetrahedron defined by any points in the first population. The reason for this is that it has been determined through experimentation that color acceptance envelopes tend to be ellipsoidal in shape. The inventors of the present invention have recognized that all ellipsoidal shapes have the property that they are convex when viewed from outside, and that if a straight line joins any two points within the convex shape, then no part of that straight line can lie outside the convex shape. Thus, since every data point in the first population lies within an acceptance envelope, any tetrahedron formed by joining four points in the first population will necessarily be entirely within the acceptance envelope, and the size of the tetrahedron does not need to be limited by performing step S14. The method is thus less restrictive than requiring an ellipsoidal acceptance envelope for the data points in the first population in order to funtion properly--only a convex acceptance envelope is required. Therefore, step S14 can be omitted when the data points in the first population are of a type that fall within a convex acceptance envelope. Step S15 comprises steps shown in Fig. 3. The method of step S15 of determining whether or not the given data point lies inside a n+1 surfaced figure will now be described in detail in conjunction with Fig. 3, and assuming that there are three parameters (n-3). The n+1 surface figure will be a tetrahedron having four vertices, each vertex being defined by one of the data points selected in step S14, and the tetrahedron will have four surfaces with each surface being defined by three of the four vertices. The following steps are repeated for each surfaces of the tetrahedron. In step S15a, two orthogonal vectors are established in the plane of the triangular surface. One side of the triangular surface is used to establish a first one of these vectors. The other one of these vectors is established by using another side of the triangular surface and taking its component normal to the first one of the vectors. Each orthogonal vector is normalized to define a unit orthogonal vector. In step S15b, after step S15a, a vector V is defined from the given data point (x 1 , x 2 , x 3 ) to one of the vertices of the triangular surfaces. (The vector can be defined from the given data point to any one of the vertices of the triangular surfaces). In step S15c, after step S15b, the respective components of the vector V that are parallel to each of the unit orthogonal vectors are found by taking the respective dot products of the vector V with each of the unit orthogonal vectors. The components of the vector V that are parallel to each of orthogonal vectors established in step S15a are subtracted from the vector V to leave a vector N, from the given data point to the triangular surface, which vector N is normal to the triangular surface. In step S15d, after step S15c, the dot product of the vector V with a vector from the vertex of the tetrahedron not included in the triangular surface to one of the vertices of the triangular surface is taken. In step S15e, after step S15d, a determination is made as to whether or not the dot product taken in step S15d is positive. If, for each surface of the tetrahedron, it is determined in step S15e that the dot product taken in step S15d is positive, then the given data point lies within the tetrahedron. If, in step S15, it is determined that the given data point lies inside one of the n+1 surfaced figures defined by all possible combinations of n+1 of the data points selected in step S14, then, at step S16, the method provides an indication at the screen 14 and/or the printer 16 that is appropriate given that the data point most likely belongs to the first population. In the color analysis application, where the first population contains data points that are "acceptable," the method provides an indication at the screen 14 and/or the printer 16 that the color represented by the given data point (x 1 , x 2 , x 2 ) is likely acceptable. Alternatively, if, in step S15, it is determined that the given data point does not lie inside any one of the n+1 surfaced figures defined by all possible combinations of n+1 of the data points selected in step S14, then, at step S17, the method provides an indication at the screen 14 and/or the printer 16 that is appropriate given that the data point may not belong to the first population. In the color analysis application, where the first population contains data points that are "acceptable," and where the given data point (x1, x2, x3) has not been found to belong to either the first "acceptable" population or the second "not acceptable" population, the method concludes that the given data point may belong to the second population and provides an indication at the screen 14 and/or the printer 16 that the color represented by the given data point (x 1 , x 2 , x 3 ) requires visual inspection. A numerical example illustrating how steps S15a through S15e are carried out will now be set forth. Assume that the method is being used with data points having three parameters, such as data points representing color samples. Also assume that one combination of four data points of the data points selected in step S14 consists of the data points (0, 0, 0), 4, 0, 0), 0, 4, 0), and (0, 0, 4). These four points define a tetrahedron with four triangular surfaces defined as follows: Surface 1 is the triangular surface defined by the data points (0, 0, 0), (4, 0, 0), and 0, 4, 0); Surface 2 is the triangular surface defined by the data points (0, 0, 0), (4, 0, 0), and (0, 0, 4); Surface 3 is the triangular surface defined by the data points (0, 0, 0), (0, 4, 0), and 0, 0, 4); and Surface 4 is the triangular surface defined by the data points (4, 0, 0), (0, 4, 0), and 0, 0, 4). Assume that the given data points consists of the data point (1, 1, 1) and that it is desired in step S15 to determine whether this data point lies within the tetrahedron or without. Surface 1 (defined by data points (0, 0, 0), (4, 0, 0), and (0, 4, 0)) Step 15a : In three dimensions, two orthogonal vectors are necessary to specify surface 1. One of the orthogonal vectors can be taken to be a side of surface 1. Thus, one of the orthogonal vectors may be taken to be: (4i+0j+0k)-(0i+0j+0k) = 4i This vector can be normalized to give a unit vector which will be: i A vector orthogonal to this vector i can be established using another side of surface 1 and taking its component normal to this vector i as follows : (0i+4j+0k)-(0i+0j+0k) = 4j This vector can be normalized to give a unit vector which will be: j Since this unit vector j is perpendicular to the first unit vector (their dot product is zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1) to any point on surface 1. Vector V may be established as: V = (i+j+k)-(0i+0j+0k) = i+j+k Step S15c: All components of vector V parallel to surface 1 are substracted from the vector V to define a normal vector N from the given data point to surface 1 as follows: N = (i+j+k)-(i+j+k)·(i)(i)-(i+j+k)·(j)(j) = i+j+k-i-j = k Step S15d: A vector from the data point not included in surface 1 to surface 1 is defined by: (0i+0j+4k)-(0i+0j+0k) = 4k The dot product of this vector with the normal vector N is: 4k·k=4 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 1 to surface 1 is positive as follows: 4 > 0 Thus, for surface 1, the given point lies on the inside. Surface 2 (defined by data points (0, 0, 0), (4, 0, 0), and (0, 0, 4)) Step 15a: Two orthogonal vectors are necessary to specify surface 2. One of the orthogonal vectors can be taken to be a side of surface 2. Thus, one of the orthogonal vectors may be taken to be: (4i+0j+0k)-(0i+0j+0k) = 4i This vector can be normalized to give a unit vector which will be: i A vector orthogonal to this vector i can be established using another side of surface 2 and taking its component normal to this vector i as follows: (0i+0j+4k)-(0i+0j+0k) = 4k This vector can be normalized to give a unit vector which will be: k Since this unit vector k is perpendicular to the first unit vector (their dot product is zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1) to any point on surface 2. Vector V may be established as: V - (i+j+k)-(0i+0j+0k) = i+j+k Step S15c: All components of vector V parallel to surface 2 are substracted from the vector V to define a normal vector N from the given data point to surface 2 as follows: N = (i+j+k)-(i+j+k)·(i)(i)-(i+j+k)·(k)(k) = i+j+k-i-k = j Step S15d: A vector from the data point not included in surface 2 to surface 2 is defined by: (0i+4j+0k)-(0i+0j+0k) = 4j The dot product of this vector with the normal vector N is: 4j·j = 4 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 2 to surface 2 is positive as follows: 4 > 0 Thus, for surface 2, the given point lies on the inside. Surface 3 (defined by data points (0, 0, 0), (0, 4, 0), and (0, 0, 4)) Step 15a: Two orthogonal vectors are necessary to specify surface 3. One of the orthogonal vectors can be taken to be a side of surface 3. Thus, one of the orthogonal vectors may be taken to be: (0i+4j+0k)-(0i+0j+0k) = 4j This vector can be normalized to give a unit vector which will be: j A vector orthogonal to this vector j can be established using another side of surface 3 and taking its component normal to this vector j as follows: (0i+0j+4k)-(0i+0j+0k) = 4k This vector can be normalized to give a unit vector which will be: k Since this unit vector k is perpendicular to the first unit vector (their dot product is zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1) to any point on surface 3. Vector V may be established as: V = (i+j+k)-(0i+0j+0k) = i+j+k Step S15c: All components of vector V parallel to surface 3 are substracted from the vector V to define a normal vector N from the given data point to surface 3 as follows: N - (i+j+k)-(i+j+k)·(j)(j)-(i+j+k)·(k)(k) = i+j+k-j-k = i Step S15d: A vector from the data point not included in surface 3 to surface 3 is defined by: (4i+0j+0k)-(0i+0j+0k) = 4i The dot product of this vector with the normal vector N is: 4i·i = 4 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 3 to surface 3 is positive as follows: 4 > 0 Thus, for surface 3, the given point lies on the inside. Surface 4 (defined by data points (4, 0, 0), (0, 4, 0), and (0, 0, 4)) Step 15a: Two orthogonal vectors are necessary to specify surface 4. One of the orthogonal vectors can be taken to be a side of surface 4. Thus, one of the orthogonal vectors may be taken to be: (0i+4j+0k)-(4i+0j+0k) = 4j-4i This orthogonal vector can be normalized to give a unit vector which will be: -(2) -0.5 i+(2) -0.5 j A vector orthogonal to this vector -(2) -0.5 i+(2) -0.5 j can be established using another side of surface 4: (0i+0j+4k)-(4i+0j+0k) = 4k-4i The component of the vector 4k-4i in a direction parallel to the vector -(2) -0.5 i+(2) -0.5 j is: (4k-4i)·(-(2) -0.5 i+(2) 0.5 j)(-(2) -0.5 i+(2) -0.5 j) = -2i+2j The component of the vector -2i+2j that is orthogonal to the vector 4k-4i is: 4k-4i-(-2i+2j) = 4k-2i-2j This vector can be normalized to give a unit vector which will be: (-2i-2j+4k)/(24) 0.5 Since this unit vector (-2i-2j+4k)/(24) 0.5 is perpendicular to the first unit vector (their dot product is zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1) to any point on surface 4. Vector V may be established as: V = (i+j+k)-(4i+0j+0k) = -3i+j+k Step S15c: All components of vector V parallel to surface 4 are subtracted from the vector V to define a normal vector N from the given data point to surface 4 as follows: N = (-3i+j+k)-(-3i+j+k)·(-(2) -0.5 i+(2) -0.5 j)(-(2) -0.5 i+ (2) -0.5 j)-(-3i+j+k)·(-2i-2j+4k)(-2i-2j+4k)/24 = (-3i+j+k)-(-2i+2j)-(-2i-2j+4k)/3 = (-1i-1j-1k)/3 Step S15d: A vector from the data point not included in surface 4 to surface 4 is defined by: (0i+0j+0k)-(4i+0j+0k) = -4j The dot product of this vector with the normal vector N is: -4i·(-1i-1j-1k)/3 = 4/3 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not include in surface 4 to surface 4 is positive as follows: 4/3 > 0 Thus, for surface 4, the given point lies on the inside. Thus, since the given data point (1, 1, 1) lies inside of each of the surfaces of the tetrahedron, it lies inside the tetrahedron. As should be apparent to one of ordinary skill in the art, step S15 of the method can be used in an application involving any number n of parameters. For example, assume that the method is being used with data points having four parameters. Also assume that one combination of five data points of the data points selected in step S14 consists of the data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 5, 0, 0), (0, 0, 5, 0) and (0, 0, 0, 5). These four points define a figure with five surfaces defined as follows: Surface 1 is the surface defined by the data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 5, 0, 0), and (0, 0, 5, 0); Surface 2 is the surface defined by the data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 5, 0, 0), and (0, 0, 0, 5); Surface 3 is the surface defined by the data points (0, 0, 0, 0), (0, 5, 0, 0), (0, 0, 5, 0), and (0, 0, 0, 5); Surface 4 is the surface defined by the data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 0, 5, 0), and (0, 0, 0, 5); and Surface 5 is the surface defined by the data points (5, 0, 0, 0), (0, 5, 0, 0), (0, 0, 5, 0), and (0, 0, 0, 5). Assume that the given data point consists of the data point (1, 1, 1, 1) and that it is desired in step S15 to determine whether this data point lies within the above-defined figure or without. Surface 1 (defined by data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 5, 0, 0), and (0, 0, 5, 0)) Step 15a: In four dimensions, three orthogonal vectors are necessary to specify surface 1. One of the orthogonal vectors can be taken to be a side of surface 1. Thus, one of the orthogonal vectors may be taken to be: (5i+0j+0k+0l)-(0i+0j+0k+0l) = 5i This vector can be normalized to give a unit vector which will be: i A vector orthogonal to this vector i can be established using another side of surface 1 and taking its component normal to this vector i as follows: (0i+5j+0k+0l)-(0i+0j+0k+0l) = 5j This vector can be normalized to give a unit vector which will be: j A vector orthogonal to each of the vectors i and j can be established using another side of surface 1 and taking its component normal to vector i or vector j as follows: (0i+0j+5k+0l)-(0i+0j+0k+0l) = 5k This vector can be normalized to give a unit vector which will be: k Since the unit vectors i, j, and k are perpendicular to one another (the dot products of 1 with j, i with k, and j with k are all zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1, 1) to any point on surface 1. Vector V may be established as: V = (i+j+k+l)-(0i+0j+0k+0l) = i+j+k+l Step S15c: All components of vector V parallel to surface 1 are subtracted from the vector V to define a normal vector N from the given data point to surface 1 as follows: N = (i+j+k+l)-(i+j+k+l)·(i)(i)-(i+j+k+l)·(j)(j)-(i+j+k+l)·(k)(k) = i+j+k+l-i-j-k = l Step S15d: A vector from the data point not included in surface 1 to surface 1 is defined by: (0i+0j+0k+5l)-(0i+0j+0k+0l) - 5l The dot product of this vector with the normal vector N is: 5l·l = 5 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 1 to surface 1 is positive as follows: 5 > 0 Thus, for surface 1, the given point lies on the inside. Surface 2 (defined by data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 5, 0, 0), and (0, 0, 0, 5)) Step 15a: Three orthogonal vectors are necessary to specify surface 2. One of the orthogonal vectors can be taken to be a side of surface 2. Thus, one of the orthogonal vectors may be taken to be: (5i+0j+0k+0l)-(0i+0j+0k+0l) = 5i This vector can be normalized to give a unit vector which will be: i A vector orthogonal to this vector i can be established using another side of surface 2 and taking its component normal to this vector i as follows: (0i+5j+0k+0l)-(0i+0j+0k+0l) = 5j This vector can be normalized to give a unit vector which will be: j A vector orthogonal to each of the vectors i and j can be established using another side of surface 2 and taking its component normal to vector i or vector j as follows: (0i+0j+0k+5l)-(0i+0j+0k+0l) = 5l This vector can be normalized to give a unit vector which will be: l Since the unit vectors i, j, and l are perpendicular to one another (the dot products of i with j, i with l, and j with l are all zero), step S15a has been completed. Step S15b: A vector V in defined from the given point (1, 1, 1, 1) to any point on surface 2. Vector V may be established as; V = (i+j+k+l)-(0i+0j+0k+0l) = i+j+k+l Step S15c: All components of vector V parallel to surface 2 are subtracted from the vector V to define a normal vector N from the given data point to surface 2 as follows: N = (i+j+k+l)-(i+j+k+l)·(i)(i)-(i+j+k+l)·(j)(j)-(i+j+k+l)·(l)(l) = i+j+k+l-i-j-l = k Step S15d: A vector from the data point not included in surface 2 to surface 2 is defined by: (0i+0j+5k+0l)-(0i+0j+0k+0l) = 5k The dot product of this vector with the normal vector N is: 5k·k = 5 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 2 to surface 2 is positive as follows: 5 > 0 Thus, for surface 2, the given point lies on the inside. Surface 3 (defined by data points (0, 0, 0, 0), (0, 5, 0, 0), (0, 0, 5, 0), and (0, 0, 0, 5)) Step 15a: Three orthogonal vectors are necessary to specify surface 3. One of the orthogonal vectors can be taken to be a side of surface 3. Thus, one of the orthogonal vectors may be taken to be: (0i+5j+0k+0l)-(0i+0j+0k+0l) = 5j This vector can be normalized to give a unit vector which will be: j A vector orthogonal to this vector j can be established using another side of surface 3 and taking its component normal to this vector j as follows: (0i+0j+5k+0l)-(0i+0j+0k+0l) = 5k This vector can be normalized to give a unit vector which will be: k A vector orthogonal to each of the vectors j and k can be established using another side of surface 3 and taking its component normal to vector j or vector k as follows: (0i+0j+0k+5l)-(0i+0j+0k+0l) = 5l This vector can be normalized to give a unit vector which will be: l Since the unit vectors j, k, and l are perpendicular to one another (the dot products of j with k, j with l, and k with l are all zero), step S15a has been completed. Step S15b: A vector V in defined from the given point (1, 1, 1, 1) to any point on surface 3. Vector V may be established as: V = (i+j+k+l)-(0i+0j+0k+0l) = i+j+k+l Step S15c: All components of vector V parallel to surface 3 are subtracted from the vector V to define a normal vector N from the given data hint to surface 3 as follows: N = (i+j+k+l)-(i+j+k+l)·(j)(j)-(i+j+k+l)·(k)(k)-(i+j+k+l)·(l)(l) = i+j+k+l-j-k-l = i Step S15d: A vector from the data point not included in surface 3 to surface 3 is defined by: (5i+0j+0k+0l)-(0i+0j+0k+0l) = 5i The dot product of this vector with the normal vector N is: 5i·i = 5 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 3 to surface 3 is positive as follows: 5 > 0 Thus, for surface 3, the given point lies on the inside. Surface 4 (defined by data points (0, 0, 0, 0), (5, 0, 0, 0), (0, 0, 5, 0), and (0, 0, 0, 5)) Step 15a: Three orthogonal vectors are necessary to specify surface 4. One of the orthogonal vectors can be taken to be a side of surface 4. Thus, one of the orthogonal vectors may be taken to be: (5i+0j+0k+0l)-(0i+0j+0k+0l) = 5i This vector can be normalized to give a unit vector which will be: i A vector orthogonal to this vector i can be established using another side of surface 4 and taking its component normal to this vector i as follows: (0i+0j+5k+0l)-(0i+0j+0k+0l) = 5k This vector can be normalized to give a unit vector which will be: k A vector orthogonal to each of the vectors i and k can be established using another side of surface 4 and taking its component normal to vector i or vector k as follows: (0i+0j+0k+5l)-(0i+0j+0k+0l) = 5l This vector can be normalized to give a unit vector which will be: l Since the unit vectors i, k, and l are perpendicular to one another (the dot products of i with k, i with l, and k with l are all zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1, 1) to any point on surface 4. Vector V may be established as: V = (i+j+k+l)-(0i+0j+0k+0l) = i+j+k+l Step S15c: All components of vector V parallel to surface 4 are subtracted from the vector V to define a normal vector N from the given data point to surface 4 as follows: N = (i+j+k+l)-(i+j+k+l)·(i)(i)-(i+j+k+l)·(k)(k)-(i+j+k+l)·(l)(l) = i+j+k+l-i-k-l = j Step S15d: A vector from the data point not included in surface 4 to surface 4 is defined by: (0i+5j+0k+0l)-(0i+0j+0k+0l) = 5j The dot product of this vector with the normal vector N is: 5j·j = 5 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 4 to surface 4 is positive as follows: 5 > 0 Thus, for surface 4, the given point lies on the inside. Surface 5 (defined by data points (5, 0, 0, 0), (0, 5, 0, 0), (0, 0, 5, 0), and (0, 0, 0, 5)) Step 15a: Three orthogonal vector are necessary to specify surface 5. One of the orthogonal vectors can be taken to be a side of surface 5. Thus, one the orthogonal vectors may be taken to be: (0i+5j+0k+0l)-(5i+0j+0k+0l) = -5i+ 5j This vector can be normalized to give a unit vector which will be: -i/(2 0.5 )+j/(2 0.5 ) A vector orthogonal to this vector -i/(2 0.5 )+j/(2 0.5 ) can be established using another side of surface 5 and taking its component normal to this vector -i/(2 0.5 )+j/(2 0.5 ) as follows: -5i+5k-(-5i+5k)·(-i/(2 0.5 )+j/(2 0.5 ))(-i/(2 0.5 )+j/(2 0.5 )) = -5i+5k-(-5i/2+5j/2) = -5i/2-5j/2+5k This vector can be normalized to give a unit vector which will be: -i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 ) A vector orthogonal to each of the vectors -5i/2-5j/2+5k and -i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 ) can be established using another side of surface 5 and taking its component normal to the vector -5i/2-5j/2+5k and to the vector -i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 ) as follows: (-5i+5l)-(-5i+5l)·(-i/(2 0.5 )+j/(2 0.5 ))(-i/(2 0.5 )+j/(2 0.5 ))-(-5i+5i)·(-i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 ))(-i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 )) = (-5i+5l)-(-5i/2+5j/2)-5(-i-j+2k)/6 = -5i/2-5j/2-5k/2+5l This vector can be normalized to give a unit vector which will be: -i/(12 0.5 )-j/(12 0.5 )-k/(12 0.5 )+3l/(12 0.5 ) Since the unit vectors are perpendicular to one another (the dot product of any one of them with another one of them is zero), step S15a has been completed. Step S15b: A vector V is defined from the given point (1, 1, 1, 1) to any point on surface 5. Vector V may be established as: V = (i+j+k+l)-(5i+0j+0k+0l) = -4i+j+k+l Step S15c: Find the components of the vector V that are parallel to each of the unit orthogonal vectors by taking the respective dot products of the vector V with each of the unit orthogonal vectors. The dot product of the vector V with the unit orthogonal vector -i/(2 0.5 )+j/(2 0.5 ) is: (-4i+j+k+l)·(-i/(2 0.5 )+j/(2 0.5 )) = 4/(2 0.5 )+l/(2 0.5 ) = 5/(2 0.5 ) The dot product of the vector V with the unit orthogonal vector -i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 ) is: (-4i+j+k+l)·(-i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 )) = 4/(6 0.5 )-l/(6 0.5 )+2/(6 0.5 ) = 5/(6 0.5 ) The dot product of the vector V with the unit orthogonal vector -i/(12 0.5 )-j/(12 0.5 )-k/(12 0.5 )+3l/(12 0.5 ) is: (-4i+j+k+l)·(-i/(12 0.5 )-j/(12 0.5 )-k/(l2 0.5 )+3l/(12 0.5 )) = 4/(12 0.5 )-l/(12 0.5 )-l/(12 0.5 )+3/(12 0.5 ) = 5/(12 0.5 ) The components of the vector V parallel to surface 5 are as follows: (5/(2 0.5 ))(-i/(2 0.5 )+j/(2 0.5 )) = -5i/2+5j/2 (5/(6 0.5 ))(-i/(6 0.5 )-j/(6 0.5 )+2k/(6 0.5 )) = -5i/6-5j/6+5k/3 (5/(12 0.5 ))(-i/(12 0.5 )-j(12 0.5 )-k/12 0.5 )+3l/12 0.5 )) = -5i/12-5j/12-5k/12+5l/4 All components of vector V parallel to surface 5 are subtracted from the vector V to define a normal vector N from the given data point to surface 5 as follows: N = (-4i+j+k+l)-(-5i/2+5j/2-5i/6-5j/6+5k/3-5i/12-5j/12-5k/12+5l/4) = -3i/12-3j/12-3k/12-3l/12 Step S15d: A vector from the data point not included in surface 5 to surface 5 is defined by: (0i+0j+0k+0l)-(5i+0j+0k+0l) = -5i The dot product of this vector with the normal vector N is: -5i·(-3i/12-3j/12-3k/12-3l/12) = 15/12 Step S15e: A determination is made as to whether or not the dot product of the vector N with the vector from the data point not included in surface 5 to surface 5 is positive as follows: 15/12 > 0 Thus, for surface 5, the given point lies on the inside. Thus, since the given data point (1, 1, 1, 1) lies inside of each of the surfaces of the figure, it lies inside the figure. Thus, a novel method is provided for classifying data in any n-dimensional space. As previously stated, the invention has application to many classification problems and can be used with any number of classifications, and any number of parameters. The invention can be used to solve classification problems where populations are not normally distributed, such as the problem of Fig. 11.13 on p. 530 of Applied Multivariate Statistical Analysis . With regard to color, the method is advantageous in that only a few data points that have previously classified as "acceptable" are needed. The method is conservative and minimizes the probability of accepting a "marginal" or "rejected" color. Further, with regard to color, the method is a true three dimensional approach and does not suffer any of the problems that can occur when trying to fit two-dimensional ellipses around data points in three dimensional space. The method is also superior to any method that attempts to fit a large three dimensional ellipsoid around data points classified as "acceptable" since those large ellipsoids by their symmetrical nature inherently include substantial regions where there are no data points, resulting in a much higher risk of classifying a given data point as "acceptable" when it should be classified as "rejected" or "marginal".

Claims: A method of determining if a given data point representing sensed color and having three parameters should be considered to be "acceptable", said method comprising the following steps: (a) providing a database containing a first population of at least four data points that each represent a color known to have been classified as "acceptable", and containing a second population of data points that each represent a color known not to have been classified as "acceptable"; (b) determining, after step (a), which of the data points of the second population is closest to the given data point; (c) defining, after step (b), an ellipsoidal neighborhood around the closest data point of the second population; (d) determining, after step (c) if the given data point is in the ellipsoidal neighborhood defined in step (c), and if so, indicating that the given data point can not be automatically considered to be "acceptable", and if not, proceeding to step (e); (e) determining which of the data points of the first population is closest to the given data point; (f) defining, after step (e), an ellipsoidal neighborhood around the closest data point of the first population; (g) determining, after step (f) if the given data point is in the ellipsoidal neighborhood defined in step (f), and if so, indicating that the given data point can automatically be considered "acceptable", and if not, proceeding to step (h); and (h) determining if the given data point falls within any of a plurality of tetrahedrons, each of the tetrahedrons having four vertices, each of the vertices of each tetrahedron being defined by one of the data points of the first population, and if so, indicating that the given data point can be automatically considered "acceptable", and if not, indicating that the given data point can not be automatically considered to be "acceptable". A method in accordance with claim 1 wherein step (h) is performed without limiting the size of any of the tetrahedrons included in the plurality of tetrahedrons. A method in accordance with claim 1 and further comprising, after step (a) and prior to step (h), the step of calculating the respective standard deviations for each of the parameters for all the data points in the first population, and wherein, in step (h), the plurality of tetrahedrons is a subset of all possible tetrahedrons that could be defined using the data points of the first population, each tetrahedron of the subset being defined such that, for each of the parameters, each of the vertices of the tetrahedron is within a predetermined distance from the given data point, the predetermined distance for each of the parameters being a function of the calculated standard deviation for the parameter. A method in accordance with claim 1 wherein the three parameters consist of a red/green parameter (a*), a yellow/blue parameter (b*), and a lightness parameter (L*). An apparatus for determining if a given data point representing a sensed color and having at least three parameters relating to the sensed color falls within a population defined by at least four known data points, each data point having the three parameters, said apparatus comprising: means for defining a tetrahedron having four vertices, each of the vertices of the tetrahedron being defined by one of the known data points; and means for determining if the given data point lies within the tetrahedron. An apparatus in accordance with claim 5 wherein said means for defining a tetrahedron does so without limiting the size of the tetrahedron. An apparatus in accordance with claim 5 and further comprising means for calculating the respective standard deviations for each of the parameters for all the data points in the population, and wherein said means for defining a tetrahedron does so such that, for each of the parameters, each of the vertices of the tetrahedron is within a predetermined distance from the given data point, the predetermined distance for each of the parameters being a function of the standard deviation calculated for the parameter by said standard deviation calculating means. An apparatus in accordance with claim 5 and further comprising means for defining the given data point by measuring the three parameters for the given data point. An apparatus in accordance with claim 1 wherein the three parameters consist of a red/green parameter (a*), a yellow/blue parameter (b*), and a lightness parameter (L*). An apparatus in accordance with claim 9 wherein said means for measuring the three parameters comprises a measuring apparatus selected from the group consisting of a colorimeter, a spectrophotometer, and an abridged spectrophotometer. An apparatus in accordance with claim 5 and further comprising means for determining if the given data point is in close proximity to any of the known data points. An apparatus in accordance with claim 11 wherein said means for determining if the given data point is in close proximity to any of the known data points comprises means for calculating the respective standard deviations for each of the parameters for all of the known data points and for defining the given data point as being in close proximity to a selected one of the known data points if, for each of the parameters, the given data point is within a predetermined distance from the selected one of the known data points, the predetermined distance for each of the parameters being a function of the calculated standard deviation for the parameter. An apparatus in accordance with claim 11 wherein said means for determining if the given data point is in the neighborhood of any of the known data points comprises means for determining which of the known data points is closest to the given data point, means for defining an ellipsoidal neighborhood around the closest data point, and means for determining if the given data point is in the ellipsoidal neighborhood. An apparatus for determining if a given data point, the data point representing a sensed color and having a predetermined number n of parameters relating to the sensed color, falls within a population defined by at least n+1 known data points, each known data point having the n parameters, said apparatus comprising: means for defining a first vector from the given data point to a point on a surface defined by n data points of the n+1 known data points; means for determining the component of the first vector along the normal to the surface defined by the n data points; means for defining a second vector from the n+1 data point to the surface defined by the n data points; means for calculating the dot product of the component of the first vector along the normal and the second vector; means for determining whether the calculated dot product is positive or negative; and means indicating whether or not said sample falls within said population of known data points depending on whether said calculated dot product is positive or negative.

Inventors: BEMIS PETER F, KESWANI SANTOSH T, BEMIS, PETER F., KESWANI, SANTOSH T.
Applicants: BEMIS MFG, FERRO CORP, BEMIS MANUFACTURING COMPANY, FERRO CORPORATION
Application year: 1992
Main classification: G06T7
IPC codes: G06K9, G06T7, G01J3