Product inspection method and apparatus

Claim 2

An automated quality inspection device for evaluating optical component characteristics of a product, the inspection device comprising: means for capturing video frames of product images, each video frame comprising an array of color values, each color value being specified by at least one variable; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; control means for (1) deriving relative component type probability curves from the identified portions of the individual component types, and (2) classifying individual color values as single component types according to the highest relative component type probability for each individual color value; wherein the control means includes processing means for deriving the relative component type probability curves by comparing color values of the identified portions of component types to the color values of an overall sample video frame.

Claim 3

The quality inspection device of claim 1, wherein each color value is specified by at least two variables.

Claim 4

The quality inspection device of claim 1, wherein the control means includes a look-up table for storing the component type classifications of individual color values.

Claim 5

The quality inspection device of claim 3, further comprising means for displaying segmented representations of the product images, wherein each image is segmented according to the component type classifications of its color values.

Claim 6

An automated quality inspection device for evaluating optical component characteristics of a product, the inspection device comprising: means for capturing video frames of product images, each video frame comprising an array of color values, each color value being specified by at least one variable;. component selection means for allowing an operator to identify portions of individual component types within a reference video frame; control means for (1) deriving relative component type probability curves from the identified portions of the individual component types, and (2) classifying individual color values as single component according to the highest relative component type probability for each individual color value; wherein the control means includes processing means for: compiling reference histograms of color values occurring within the identified portions of the reference video frame; calculating color value density curves from the reference histograms; and scaling the density curves by scaling factors to obtain the relative component type probability curves.

Claim 7

An automated quality inspection device for evaluating optical component characteristics of a product, the inspection device comprising: means for capturing video frames of product images, each video frame comprising an array of color values, each color value being specified by at least one variable;. component selection means for allowing an operator to identify portions of individual component types within a reference video frame; control means for (1) deriving relative component type probability curves from the identified portions of the individual component types, and (2) classifying individual color values as single component according to the highest relative component type probability for each individual color value; wherein the control means includes processing means for: compiling reference histograms of color values occurring within the identified portions of the reference video frame; calculating color value density curves from the reference histograms; and scaling the density curves by scaling factors to obtain the relative component type probability curves.

Claim 8

The quality inspection device of claim 5, wherein the control means includes a look-up table for storing the component type classifications of individual color values.

Claim 9

The quality inspection device of claim 5, further comprising means for obtaining the scaling factors from an operator.

Claim 10

The quality inspection device of claim 5, further comprising: means for obtaining the scaling factors from an operator; and means for displaying segmented representations of a sample video frame to the operator; processing means for segmenting the sample video frame according to the component type classifications of its color values, the displayed segmented representations being updated in response to the operator providing new scaling factors.

Claim 11

The quality inspection device of claim 5, wherein the control means includes processing means for calculating the density curves by fitting a continuous function to each reference histogram.

Claim 12

The quality inspection device of claim 5, wherein the control means includes processing means for calculating the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 13

An automated quality inspection device for evaluating optical component characteristics of a product, the inspection device comprising: means for capturing video frames of product images, each video frame comprising an array of color values, each color value being specified by at least one variable; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; control means for (1) deriving relative component type probability curves from the identified portions of the individual component types, and (2) classifying individual color values as single component types according to the highest relative component type probability for each individual color value; wherein the control means has processing means for: compiling reference histograms of color values occurring within the identified portions of the reference video frame; calculating color value density curves from the reference histograms; compiling an overall histogram of color values within a sample video frame; comparing the density curves to the overall histogram to obtain a scaling factor corresponding to each density curve; and scaling the density curves by the scaling factors to obtain the relative component type probability curves.

Claim 14

An automated quality inspection device for evaluating optical component characteristics of a product, the inspection device comprising: means for capturing video frames of product images, each video frame comprising an array of color values, each color value being specified by at least one variable; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; control means for (1) deriving relative component type probability curves from the identified portions of the individual component types, and (2) classifying individual color values as single component types according to the highest relative component type probability for each individual color value; wherein the control means has processing means for: compiling reference histograms of color values occurring within the identified portions of the reference video frame; calculating color value density curves from the reference histograms; compiling an overall histogram of color values within a sample video frame; comparing the density curves to the overall histogram to obtain a scaling factor corresponding to each density curve; and scaling the density curves by the scaling factors to obtain the relative component type probability curves.

Claim 15

The quality inspection device of claim 11, further comprising: means for obtaining probability multipliers corresponding to the relative probability curves from an operator; the processing means being further for adjusting each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 16

The quality inspection device of claim 11, wherein the processing means includes a look-up table for storing the component type classifications of individual color values.

Claim 17

The quality inspection device of claim 11, wherein the processing means calculates the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 18

The quality inspection device of claim 11, further comprising: means for obtaining probability multipliers from an operator; the processing means being further for adjusting each density scaling factor by the corresponding probability multiplier before scaling the density curves; and means for displaying segmented representations of the sample video frame to the operator, wherein the sample frame is segmented according to the component type classifications of its color values, the segmented representations being updated in response to the operator providing new probability multipliers.

Claim 19

The quality inspection device of claim 11, wherein the control means obtains each scaling factor by summing the product of the corresponding density curve and the overall histogram over a range of color values.

Claim 20

The quality inspection device of claim 16, further comprising means for obtaining probability multipliers corresponding to the relative probability curves from an operator, the processing means being further for adjusting each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 21

An automated color component classification device for identifying component types in a sample product having a plurality of component types, the inspection device comprising: a sample surface for supporting product which is to be inspected; a camera positioned relative to the optical inspection surface to produce video flames of the supported sample product, each video frame containing an array of pixels, wherein each pixel has an associated color value which is specified by at least two variables; a video display monitor which displays video frames to an operator; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; a data processor operably connected to the camera, the video display monitor, and the component selection means, the data processor being programmed to derive relative component type probability curves from the identified portions of the individual component types, and to classify individual color values as single component types according to the highest relative component type probability for each color value; wherein the data processor is programmed to derive the relative component type probability curves by comparing color values of the identified portions of component types to the color values of an overall sample video frame.

Claim 22

An automated color component classification device for identifying component types in a sample product having a plurality of component types, the inspection device comprising: a sample surface for supporting product which is to be inspected; a camera positioned relative to the optical inspection surface to produce video flames of the supported sample product, each video frame containing an array of pixels, wherein each pixel has an associated color value which is specified by at least two variables; a video display monitor which displays video frames to an operator; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; a data processor operably connected to the camera, the video display monitor, and the component selection means, the data processor being programmed to derive relative component type probability curves from the identified portions of the individual component types, and to classify individual color values as single component types according to the highest relative component type probability for each color value; wherein the data processor is programmed to derive the relative component type probability curves by comparing color values of the identified portions of component types to the color values of an overall sample video frame.

Claim 23

The component classification device of claim 18, further comprising a memory look-up table for storing the component type classifications of individual color values.

Claim 24

The component classification device of claim 19, further comprising means for displaying segmented sample video frames, wherein each sample video frame is segmented according to the component type classifications of its color values.

Claim 25

An automated color component classification device for identifying component types in a sample product having a plurality of component types, the inspection device comprising: a sample surface for supporting product which is to be inspected; a camera positioned relative to the optical inspection surface to produce video frames of the supported sample product, each video frame containing an array of pixels, wherein each pixel has an associated color value which is specified by at least two variables; a video display monitor which displays video frames to an operator; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; a data processor operably connected to the camera, the video display monitor, and the component selection means, the data processor being programmed to derive relative component type probability curves from the identified portions of the individual component types, and to classify individual color values as single component types according the highest relative component type probability for each color value; wherein the data processor is further programmed to: compile reference histograms of color values occurring within the identified portions of the reference video frame; calculate color value density curves from the reference histograms; and scale the density curves by scaling factors to obtain the relative component type probability curves.

Claim 26

An automated color component classification device for identifying component types in a sample product having a plurality of component types, the inspection device comprising: a sample surface for supporting product which is to be inspected; a camera positioned relative to the optical inspection surface to produce video frames of the supported sample product, each video frame containing an array of pixels, wherein each pixel has an associated color value which is specified by at least two variables; a video display monitor which displays video frames to an operator; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; a data processor operably connected to the camera, the video display monitor, and the component selection means, the data processor being programmed to derive relative component type probability curves from the identified portions of the individual component types, and to classify individual color values as single component types according the highest relative component type probability for each color value; wherein the data processor is further programmed to: compile reference histograms of color values occurring within the identified portions of the reference video frame; calculate color value density curves from the reference histograms; and scale the density curves by scaling factors to obtain the relative component type probability curves.

Claim 27

The component classification device of claim 21, further comprising a memory look-up table for storing the component type classifications of individual color values.

Claim 28

The component classification device of claim 21, further comprising means for obtaining the scaling factors from an operator.

Claim 29

The component classification device of claim 21, further comprising: means for obtaining the scaling factors from an operator; and means for displaying segmented representations of sample video frames; wherein the sample video frame is segmented according to the component type classifications of its color values, the displayed segmented representations being updated in response to the operator providing new scaling factors.

Claim 30

The component classification device of claim 21, wherein the data processor calculates the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 31

An automated color component classification device for identifying Component types in a sample product having a plurality of component types, the inspection device comprising: a sample surface for supporting product which is to be inspected; a camera positioned relative to the optical inspection surface to produce video frames of the supported sample product, each video frame containing an array of pixels, wherein each pixel has an associated color value which is specified by at least two variables; a video display monitor which displays video frames to an operator; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; a data processor operably connected to the camera, the video display monitor, and the component selection means, the data processor being programmed to derive relative component type probability curves from the identified portions of the individual component types, and to classify individual color values as single component types according the highest relative component type probability for each color value; wherein the data processor is further programmed to: compile reference histograms of color values occurring within the identified portions of the reference video frame; calculate color value density curves from the reference histograms; compile an overall histogram of color values within a sample video frame; compare the density curves to the overall histogram to obtain a scaling factor corresponding to each density curve; and scale the density curves by the scaling factors to obtain the relative component type probability curves.

Claim 32

An automated color component classification device for identifying Component types in a sample product having a plurality of component types, the inspection device comprising: a sample surface for supporting product which is to be inspected; a camera positioned relative to the optical inspection surface to produce video frames of the supported sample product, each video frame containing an array of pixels, wherein each pixel has an associated color value which is specified by at least two variables; a video display monitor which displays video frames to an operator; component selection means for allowing an operator to identify portions of individual component types within a reference video frame; a data processor operably connected to the camera, the video display monitor, and the component selection means, the data processor being programmed to derive relative component type probability curves from the identified portions of the individual component types, and to classify individual color values as single component types according the highest relative component type probability for each color value; wherein the data processor is further programmed to: compile reference histograms of color values occurring within the identified portions of the reference video frame; calculate color value density curves from the reference histograms; compile an overall histogram of color values within a sample video frame; compare the density curves to the overall histogram to obtain a scaling factor corresponding to each density curve; and scale the density curves by the scaling factors to obtain the relative component type probability curves.

Claim 33

The component classification device of claim 26, further comprising: means for obtaining probability multipliers corresponding to the relative probability curves from an operator; the data processor being further programmed to adjust each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 34

The component classification device of claim 26, further comprising a memory look-up table for storing the component type classifications of individual color values.

Claim 35

The component classification device of claim 26, wherein the data processor calculates the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 36

The component classification device of claim 26, further comprising: means for obtaining probability multipliers from an operator; the data processor being further programmed to adjust each density scaling factor by the corresponding probability multiplier before scaling the density curves; and means for displaying segmented representations of the sample video frame to the operator, wherein the sample video frame is segmented according to the component type classifications of its color values, the segmented representations being updated in response to the operator providing new probability multipliers.

Claim 37

The component classification device of claim 26, wherein the data processor is programmed to obtain each scaling factor by summing the product of the corresponding density curve and the overall histogram over a range of color values.

Claim 38

The component classification device of claim 31, further comprising means for obtaining probability multipliers corresponding to the relative probability curves from an operator, the data processor being further programmed to adjust each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 39

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calibrate the density curves by comparing them to an overall histogram of the color values occurring within an overall sample video frame.

Claim 40

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calibrate the density curves by comparing them to an overall histogram of the color values occurring within an overall sample video frame.

Claim 41

The quality inspection device of claim 33, wherein the data processor includes a look-up table for storing component type classifications of individual color values.

Claim 42

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calibrate the density curves by scaling each density curve by an operator-supplied scaling factor.

Claim 43

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calibrate the density curves by scaling each density curve by an operator-supplied scaling factor.

Claim 44

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; means for obtaining scaling factors from an operator; wherein the data processor is programmed to calibrate the density curves by scaling the density curves by the scaling factors; and wherein the data processor is programmed to display segmented representations of sample video frames on the video display, each sample video frame being segmented according to the component type classifications of its pixels, the data processor being programmed to update the displayed segmented representations in response to the operator providing new scaling factors.

Claim 45

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; means for obtaining scaling factors from an operator; wherein the data processor is programmed to calibrate the density curves by scaling the density curves by the scaling factors; and wherein the data processor is programmed to display segmented representations of sample video frames on the video display, each sample video frame being segmented according to the component type classifications of its pixels, the data processor being programmed to update the displayed segmented representations in response to the operator providing new scaling factors.

Claim 46

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calculate the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 47

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calculate the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 48

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calibrate the density curves by: compiling an overall histogram of color values within a sample video frame; comparing the density curves to the overall histogram to obtain a density scaling factor corresponding to each density curve; and scaling the density curves by their corresponding density scaling factors to obtain corresponding relative probability curves which represent the probability of any single color value occurring in any single component type relative to any other component types.

Claim 49

An automated quality inspection device for evaluating color component characteristics of a product, the inspection device comprising: an optical transducer which produces a video signal representative of color characteristics of the product; a frame grabber which receives the video signal and captures video frames representing images of the product, each video frame containing an array of pixels, each pixel having an associated color value which is specified by at least two variables; a video display upon which a reference video frame is displayed to an operator; component selection means for allowing the operator to identify portions of component type areas from the displayed reference video frame; a data processor operably connected to read pixel color values from the frame grabber and programmed to: (a) compile reference histograms of color values within the identified portions of the reference frame component type areas; (b) calculate color value density curves from the reference histograms; (c) calibrate the density curves relative to each other; (d) classify an individual pixel as the component type having the highest calibrated density curve value at the color value of the individual pixel; wherein the data processor is programmed to calibrate the density curves by: compiling an overall histogram of color values within a sample video frame; comparing the density curves to the overall histogram to obtain a density scaling factor corresponding to each density curve; and scaling the density curves by their corresponding density scaling factors to obtain corresponding relative probability curves which represent the probability of any single color value occurring in any single component type relative to any other component types.

Claim 50

The quality inspection device of claim 38, further comprising: means for obtaining probability multipliers corresponding to the relative probability curves from an operator; the data processor being further programmed to adjust each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 51

The quality inspection device of claim 38, wherein the data processor includes a look-up table for storing component type classifications of individual color values.

Claim 52

The quality inspection device of claim 38, wherein the data processor is programmed to calculate the density curves by fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 53

The quality inspection device of claim 38, further comprising: means for obtaining probability multipliers from an operator; and means for displaying segmented representations of a sample video frame to the operator, wherein the sample video frame is segmented according to the component type classifications of its color values, the segmented representations being updated in response to the operator providing new probability multipliers.

Claim 54

The quality inspection device of claim 38, wherein the data processor is programmed to obtain each density scaling factor by summing the product of the corresponding density curve and the overall histogram over a range of color values.

Claim 55

The quality inspection device of claim 43, further comprising means for obtaining probability multipliers corresponding to the relative probability curves from an operator, the data processor being programmed to adjust each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 58

The method of claim 45, wherein each color value is specified by at least two variables.

Claim 59

The method of claim 45, further comprising the following additional steps: classifying individual color values as individual component types according to their highest relative probability; and storing the component type classifications of individual color values in a look-up table.

Claim 60

The method of claim 45, further comprising the following additional steps: segmenting a video frame based on the classifications of its pixels; and displaying the segmented video frame.

Claim 63

The method of claim 49, further comprising the following additional steps: classifying individual color values as individual component types according to their highest relative probability; and storing the component type classifications of individual color values in a look-up table.

Claim 64

The method of claim 49, further comprising the additional step of obtaining the scaling factors from an operator.

Claim 65

The method of claim 49, further comprising the following additional steps: obtaining the scaling factors from an operator; segmenting the video frame based on the classifications of its pixels; displaying the segmented video frame; and updating the segmented video frame display in response to the operator providing new scaling factors.

Claim 66

The method of claim 49, wherein the step of calculating color value density curves from the reference histograms comprises fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 69

The method of claim 54, further comprising the following additional steps: obtaining probability multipliers corresponding to the relative probability curves from an operator; and adjusting each density scaling factor by the corresponding probability multiplier before scaling the density curves.

Claim 70

The method of claim 54, further comprising the following additional steps: classifying individual color values as individual component types according to their highest relative probability; and storing the component type classifications of individual color values in a look-up table.

Claim 71

The method of claim 54, wherein the step of calculating color value density curves from the reference histograms comprises fitting a set of gaussian-weighted Hermite polynomials to each reference histogram.

Claim 72

The method of claim 54, further comprising the following additional steps: obtaining probability multipliers corresponding to the relative probability curves from an operator; adjusting each density scaling factor by the corresponding probability multiplier before scaling the density curves; segmenting the sample video frame based on the classifications of its pixels; displaying the segmented video frame; and updating the segmented video frame display in response to the operator providing new probability multipliers.

Claim 73

The method of claim 54, wherein the step of comparing the density curves to the overall histogram comprises summing the product of the corresponding density curve and the overall histogram over a range of color values.

Claim 74

The method of claim 59, further comprising the following additional steps: obtaining probability multipliers corresponding to the relative probability curves from an operator; adjusting each density scaling factor by the corresponding probability multiplier before scaling the density curves.