80298 Munchen

Internal Ref: KURA.221.01WO

MS/BGH/BGH

Applicant

Kuraray Europe GmbH

Philipp-Reis-Str. 4

65795 Hattersheim, Germany

Mobile computing device for performing color fastness measurements

Description

Field of the invention

The invention relates to the measurement of color fastness for textiles.

Background and related art

Color fastness of a textile is how resistant that textile is to any change in its color. This includes how resistant the textile is to staining as well as the fading of colors due to light or washing. There are industry standard techniques that are applied to textile samples to measure various types of color fastness. To assess the color fastness, an observer compares a textile under test to a reference textile and compares them to a color scale (usually a grey or blue scale) that provides a series of labeled contrasts. This observation is made under defined lighting conditions and the observer chooses the contrast on the color scale which best matches those provided on the color scale. Photometric camera systems, which have been modified and calibrated to respond to light in the same way as the human eye, are also defined in some standards for measuring color fastness.

United States patent application US 2014/0232923 Al discloses techniques related to display of device independent color differences. In examples, a color comparison graphical user interface is operated. The GUI displays a color of a sample object. Further, the GUI displays a device independent color difference between the sample color and the reference color.

Summary

The invention provides for a portable computing device, and a computer program in the independent claims. Embodiments are given in the dependent claims.

A difficulty with using photometric camera systems for measuring color contrast is that specialized camera systems are needed. Expensive calibration systems may also be needed to calibrate these specialized camera systems. There has been a long felt need for systems which may be used for measuring color fastness.

However, less expensive and technically simpler camera systems are not suitable for photometric measurements: Cameras such as those used in smart phones and other mobile devices are unsuitable for photometric measurements. However, embodiments may provide for a means of using such a camera for accurately measuring colorfastness of a fabric or textile. This may be achieved by acquiring a digital image that images a color reference region with a conventional camera along with the fabric reference and the fabric sample. Accurate measurement of the color fastness may be achieved when the colorfastness value for individual pixels in a sample region, which images the fabric sample, is calculated. The pixel wise color fastness of the individual pixels is then used to calculate the color fastness value. The pixel wise color fastness calculation typically produces a distribution of color fastness values. A variety of criterion, as described below, can be used to select the color fastness value.

In one aspect the invention provides for a portable computing device that comprises a memory storing machine-executable instructions, a camera configured for acquiring a digital image, and a computational system. A portable computing device as used herein encompasses a computing device which may be carried or moved by a person or individual. For example, a portable computing device may be a smartphone, a tablet computer, a laptop computer, or other moveable computing device. In some embodiments the portable computing device may be battery powered.

Execution of the machine-executable instructions causes the computational system to control the camera to acquire the digital image. Execution of the machine-executable instructions further causes the computational system to identify a reference region within the digital image. Execution of the machine-executable instructions further causes the computational system to identify a sample region within the digital image. Execution of the machine-executable instructions further causes the computational system to identify a color reference region in the digital image.

The reference region may for example be a region which provides a textile which has been dyed. The sample region may include the textile which has been dyed after it has been subjected to a color fastness test such as being exposed to washing or rubbing or other test of color fastness. The color reference region provide either a reference for scales which are used to measure the color fastness value of the sample region or are used to calibrate the camera. In one example, the color fastness reference region comprise at least one original color region and multiple graded color regions to directly calibrate color fastness measurement. A comparison between a region of the original color region and one of the multiple graded color regions is assigned a color fastness value. In another example the color reference region provides regions of known color and/or greyscale to calibrate color measurements of the reference region and the sample region.

Execution of the machine-executable instructions further causes the computational system to calculate a pixel wise sample color distance between a sample color value of individual pixels in the sample region and a reference color region of the reference region. By pixel wise it is understood herein that the calculation is performed for each pixel in the sample region individually. Colors may be expressed in different color scales. For example, a particular color may be indicated by providing coordinates in the color space. The sample color distance is a distance calculated between two color coordinates, in this case between the sample color value of the individual pixels in the sample region and a reference color of the reference region. This pixel wise sample color distance and the sample color distance may, for example, be Euclidian distances calculated using the coordinates of the color space that is used.

Execution of the machine executable instructions further causes the computational system to receive a refence color distance for each of multiple color fastness values. This may take different forms in different examples. If the distance in color between the reference region and pixels in the sample region is in a known color space then the reference color distance is defined by the values of the greyscales themselves.

If the distance in color between the reference region and pixels in the sample region are not in a calibrated or known color space. Then measurements made on a calibrated grey scale or blue scale may be used to assign the reference color distance for each of the multiple color fastness values.

Execution of the machine-executable instructions further causes the computational system to calculate a pixel wise color fastness value of the individual pixels in the sample region by comparing each reference color distance of each of the multiple color fastness values to the pixel wise sample color distance. The pixel wise color fastness value as used herein encompasses a color fastness value that is calculated for each pixel individually. The calculation of the pixel wise color fastness value is calibrated using a color measurement of the color reference region.

Execution of the machine-executable instructions further causes the computational system to select a color fastness value for the sample region using the pixel wise color fastness value of the individual pixels in the sample region. In this step the pixel wise color fastness value of each of the individual pixels in the sample region is used to select a color fastness value. Different rules or techniques may be used in different examples or embodiments of this.

Selecting the color fastness value using the pixel wise color fastness value may have a number of advantages. Often, when color fastness tests are performed, the sample region in particular may have a non-uniform color. For example, the color fastness may not be a constant throughout an entire textile sample. It may also be the case that within the sample region only a portion of a textile which is imaged actually exhibits signs of the color fastness test. Selecting the color fastness value using the individual or pixel wise color fastness value, may be beneficial because it may enable the exclusion of certain pixels which have not been affected by a color fastness test. In some embodiments, the portable computing system may be battery powered.

In another embodiment execution of the machine-executable instructions further causes the computational system to construct a value histogram by binning the pixel color values within the reference region and calculating the reference color value by determining a characteristic reference value of the value histogram. This may be beneficial because using determining the characteristic value of the value histogram may avoid noise or incorrect data.

In an alternative embodiment the value histogram is constructed using the pixel wise sample color distances instead. In this embodiment, a combined pixel wise sample color distance is calculated by determining a characteristic value of the value histogram. The color fastness value for the sample region is calculated using the combined pixel wise color fastness value.

In another embodiment execution of the machine-executable instructions further causes the computational system to calculate the reference color value by applying a statistical calculation to pixel color values within the reference color region. For example, various statistical tests such as calculating a mean, average or most likely value within the histogram could all be used for the reference color value. For example, it may again aid in the exclusion of noise or incorrect data when calculating the reference color value.

In another embodiment execution of the machine-executable instructions further causes the computational system to render a spatially dependent image of the color fastness value. For example, a grayscale or false color image could be created which provides the spatially dependent image of the color fastness value. This may for example provide more information to the user than simply providing an overall number or value which is assigned.

In another embodiment execution of the machine-executable instructions further causes the computational system to construct a color fastness value histogram from the pixel wise color fastness value of the individual pixels in the sample region. The color fastness value for the sample region is selected by determining a characteristic value of the color fastness value histogram. This for example may be beneficial when dealing with a sample region that does not have a uniform color or which has been affected non-uniformly by the color fastness test, such as washing or rubbing.

In another embodiment the characteristic value of the value histogram is determined by a maximum value of the color fastness value histogram. This applies to both embodiments that use a value histogram. In another embodiment the characteristic value of the value histogram is determined by an average value of the color fastness value histogram. This applies to both embodiments that use a value histogram.

In another embodiment the characteristic value of the value histogram is determined by a median value of the color fastness value histogram. This applies to both embodiments that use a value histogram.

In another embodiment the characteristic value of the value histogram is determined by fitting a curve function to the value histogram. The characteristic value is a fitting parameter or a maximum value of the curve function. This applies to both embodiments that use a value histogram.

In another embodiment the characteristic value of the value histogram is determined by cropping the color fastness value histogram according to a predetermined criterion and then determining the maximum value of the value histogram, the average value of the value histogram, the median value of the histogram, or fitting the curve function to the value histogram. This embodiment may be of particular use because cropping the value histogram may be used to eliminate some portions of the sample region which were for example not affected or inconsistently affected by the color fastness test.

In another embodiment execution of the machine-executable instructions further causes the computational system to crop the value histogram according to a predetermined criterion. The pixels cropped from the histogram are excluded from the calculation of the pixel wise color fastness value. This for example may be useful for dealing with images of the sample region which depict an inconsistent or incomplete color fastness test of a fabric or textile.

In another embodiment the pixel wise color fastness value is calculated by finding the reference color distance of each of the graded color regions that is closest in value to the pixel wise sample color distance. This for example may be achieved by binning the color fastness value for each of the pixels.

In another embodiment the pixel wise color fastness value is calculated by calculating the pixel wise color fastness value as a continuous number. In this example, instead of binning the values a continuous number may be assigned to each of the pixels. Normally discreet values are assigned for the color fastness value; however, the use of a continuous number may be useful in removing noise or artifacts from the measure.

In another embodiment execution of the machine-executable instructions further causes the computational system to calculate the graded color value within each of the multiple graded color regions by applying a statistical measure to pixel color values within each of the multiple graded color regions. Execution of the machine-executable instructions further causes the computational system to calculate the original color value for the at least one original color region by applying the statistical measure to pixel color values within the at least one color region. This means of calculating the graded color value and the original color value may be successful because typically these regions are provided as color or painted regions with specific colors. The colors within these regions therefore may be very uniform from pixel-to-pixel. The statistical measure such as an average, mean or maximum value after binning may provide an accurate means of calculating the graded color value and/or the original color value. The graded color value and the original color value may then be used to calculate the reference color difference.

In another embodiment saturated pixels are excluded from calculation of the pixel wise sample color distance, the pixel wise color fastness value, the reference color distance, the reference color value, the original color value, and/or the graded color value. In other words, saturated pixels may be excluded from the calculations described above in general. This may be useful because the saturated pixels may not accurately depict the colors and/or intensity of light as measured. Excluding them may provide for an improved measurement of the color fastness value for the sample region. In another embodiment the digital image is gamma corrected. Execution of the machineexecutable instructions further causes the computational system to reverse the gamma correction before beginning calculation of the sample color distance and the reference color distance. In many devices such as the camera on a smartphone or tablet computer gamma correction is applied automatically; reversing this gamma correction may provide for more accurate determination of the color fastness value. For example, the execution of the machine-executable instructions may cause the computational system to reverse the gamma correction on the entire digital image.

In another embodiment the memory further comprises an optical marking identification module configured for recognizing and locating optical markings within the digital image. Execution of the machine-executable instructions further causes the computational system to identify the reference region within the digital image by locating optical markings within the digital image using the optical marking identification module. For example, the optical markings may be or may be analogous to QR code markings and may be used for identifying spatial locations within the digital image.

In another embodiment execution of the machine-executable instructions further causes the computational system to identify the sample region within the digital image by locating the optical markings using the optical marking identification module.

In another embodiment execution of the machine-executable instructions further causes the computational system to identify a color fastness reference region within the digital image by locating the optical markings using the optical marking identification module. The color fastness reference region corresponds to the location of the color scale in the digital image.

In another embodiment execution of the machine-executable instructions further causes the computational system to determine a sample identifier. The sample identifier is preferably appended to the color fastness value. For example, there may be numbers or optical markings which label the sample identifier. A QR code, barcode, or even a number may be depicted in the digital image. If the sample identifier is a number then an optical character recognition module may be used for reading or identifying the text in the digital image. These embodiments may be beneficial because they may provide for an effective and automatic way of identifying the different regions within the digital image.

In another embodiment the memory further contains an object detection algorithm configured to output the reference region, the sample region, each of the multiple graded color regions, and the at least one original color region in response to receiving the digital image. Execution of the machine-executable instructions further causes the computational system to receive the reference region, the sample region, each of the multiple graded color reference regions, and the at least one original color region from the object detection algorithm in response to inputting the digital image into the object detection algorithm. In this example the optical markings are not used.

As the various regions of the digital image may have consistent color and/or grayscale regions as well as other optical properties, an algorithm can be used to detect their locations without the use of optical markings. This for example may be an algorithm which is designed by hand. In other examples a neural network can be used to identify these regions automatically. The neural network could for example be trained by using a variety of digital images which have been labeled by hand. A deep learning algorithm can then be used to train the neural network.

In another embodiment execution of the machine-executable instructions further causes the computational system to determine a size and/or viewing angle of any one of the following: the reference region, the sample region, and the color fastness region, the at least one original color region, and the multiple graded color regions and then to display a repositioning indicator on the display if the size and/or viewing angle is not within a predetermined positioning range. The size and/or the viewing angle of these regions can for example be determined by determining their absolute size in a digital image as well as their shape. If they for example have a distinctive shape, such as a circle or square, then tilting at a viewing angle would cause a distortion of this. The distortion of a circular region may distort to an oval. Determination of the size and/or viewing angle can therefore be determined using standard image processing and/or segmentation techniques. The displaying of the repositioning indicator on the display may be useful because it may provide feedback and instruct the operator of the portable computing device how to move the portable computing device such that the best value for the color fastness value is determined.

In another embodiment execution of the machine-executable instructions further causes the computational system to control the camera to reacquire the digital image multiple times.

Execution of the machine-executable instructions further causes the computational system to determine the color fastness value multiple times after reacquiring the digital image.

Execution of the machine-executable instructions further causes the computational system to calculate a color fastness value calculated from the color fastness value determined multiple times. This may be implemented in a number of different ways. For example, the color fastness value may be selected for the sample region using the pixel wise color fastness values for each individual measurement. In other examples, the pixel wise color fastness value determined in each acquisition may be added to a cumulative histogram. This cumulative histogram may then be used to determine the statistical color fastness value by looking at such values as the mean, average or maximum value. When a histogram is used there may also be a trimming operation to remove noise or tails as was described previously. In another embodiment the portable computing device further comprises a display. Execution of the machine-executable instructions further causes the computational system to display the color fastness value.

In another embodiment execution of the machine-executable instructions further causes the computational system to store the color fastness value in the memory.

In another embodiment execution of the machine-executable instructions further causes the computational system to store the color fastness value in a database via a network condition.

All three of these embodiments may be beneficial because the color fastness value for the sample region is available to a user either directly or via the memory or database.

In another embodiment execution of the machine-executable instructions further causes the computational system to determine an image quality measure of the digital image. The image quality measure may for example be a light level, a white balance check, a noise level, a sample region shape, a reference region shape, and combinations thereof.

Execution of the machine-executable instructions may further cause the computational system to display an image quality warning message on the display if the image quality measure is outside of a predetermined image quality range. This may for example be because it may be useful for identifying image problems as the operator is using the portable computing device and provide a means to immediately correct this problem. In addition or alternatively to displaying the image quality warning the determination of the color fastness value is suspended. This for example may be useful in preventing the operator from measuring the color fastness value in conditions when the image quality is too low. In another embodiment, the color reference region is a color fastness reference region. The color fastness reference region comprises at least one original color region and multiple graded color regions. In this embodiment, receiving the reference color distance for each of the multiple color fastness values using the measurement of the color reference region comprises: calculating a measured color distance between each of the multiple graded color regions and the at least one original color region; and assigning the measured color distance of each of the multiple graded color regions to one of the multiple color fastness values to calibrate the calculation of the pixel wise color fastness value.

In another embodiment, the color refence region comprises multiple predefined color regions each with a predetermined color value in a chosen color space. In this embodiment calibration of the pixel wise color fastness value comprises: determining a measured color value in the chosen color space for each of the multiple predefined color regions; transforming the digital image to the chosen color space using a difference between the predetermined color value and the measured color value of each of the multiple predefined color regions. The transformation of the digital image to the chosen color space calibrates the calculation of the pixel wise color fastness value, wherein the reference color distance of each of the multiple color fastness values is predefined in the chosen color space. The transformation from the digital image to the chosen color space could be a linear transformation.

In another embodiment, the multiple predefined color regions are any one of the following: a grey scale or a polychromatic distribution of colors.

If the polychromatic distribution of colors is used, they may cover or bracket the whole color space or a large portion of it from darkest black to brightest white, saturated red, green, blue as well as cyan, magenta, yellow and all shades in between hues, brightnesses and saturations. If the camera to be linear, or sufficiently close to linear, or linearizable or sufficiently closely linearizable, and the measurement is performed in controlled lighting conditions (as one does in a lighting box under D65 daylight according to ISO standard), one can reduce the space of color that is covered by the calibration markers down to e.g. just the few gray color patches as given in the gray scales. One only needs a reference of overall brightness scale as well as a reference of non-saturated color, both of which are provided by the grayscale.

In another embodiment the color fastness reference region is a grayscale color fastness reference regions.

In another embodiment the color fastness reference region is a blue scale color fastness reference region.

In another embodiment the portable computing device is a smartphone, a mobile telecommunications device, a handheld computing device, or a tablet computer. The use of any of these devices may be advantageous because they are readily carried by an individual and may be used to perform the measurements.

In another embodiment the sample color difference and the reference color distance are calculated using any one of the following color scales: delta-E, delta-Ezooo and delta-EcMc. The use of any of these color coordinate systems may be advantageous because the distances can be used to compare the perceived color contrast directly by the subject.

In another aspect, the invention provides for a method of determining a color fastness value. The method comprises controlling a camera to acquire a digital image. The method further comprises identifying a reference region within the digital image. The method further comprises identifying a sample region within the digital image. The method further comprises identifying a color reference regions in the digital image.

The method further comprises receiving a reference color distance for each of multiple color fastness values. The method further comprises calculating a pixel wise sample color distance between a sample color value of individual pixels in the sample region and a reference color of the reference region.

The method further comprises calculating a pixel wise color fastness value of the individual pixels in the sample region by comparing each reference color distance of each of the multiple color fastness values to the pixel wise sample color distance. The calculation of the pixel wise color fastness value is calibrated using a color measurement of the color reference region. The method further comprises selecting a color fastness value for the sample region using the pixel wise color fastness value of the individual pixels in the sample region.

In another embodiment the method comprises preparing a textile sample and a textile reference region reference according to a color fastness test protocol. The method further comprises arranging a textile reference and sample that were prepared according to the protocol as well as a color scale within the field of view of the camera before acquiring the digital image. The color scale may for example be a gray or color scale prepared specifically for preparing or performing a color fastness test.

In another embodiment the method comprises illuminating the textile sample and the textile reference region using a predetermined lighting system. For example a particular color fastness test protocol, may specify, for example, ISO standardized lighting protocols or an ISO standard light box to be used.

In another aspect the invention provides for a computer program comprising machine executable instructions for execution by a computational system controlling a portable computing device. The computer program may for example be stored on a non-transitory storage medium. The portable computing device comprises a camera configured for acquiring a digital image. Execution of the machine-executable instructions causes the computational system to control the camera to acquire the digital image.

Execution of the machine-executable instructions further causes the computational system to identify a reference region within the digital image.

Execution of the machine-executable instructions further causes the computational system to identify a sample region within the digital image.

Execution of the machine-executable instructions further causes the computational system to identify the color reference region in the digital image.

Execution of the machine-executable instructions further causes the computational system to receive a reference color distance for each of multiple color fastness values.

Execution of the machine-executable instructions further causes the computational system to calculate a pixel wise sample color distance between a sample color value of individual pixels in the sample region and a reference color of the reference region.

Execution of the machine-executable instructions further causes the computational system to calculate a pixel wise color fastness value for each of the individual pixels in the sample region by comparing each reference color distance of each of the multiple color fastness values to the pixel wise sample color distance. The calculation of the pixel wise color fastness value is calibrated using a color measurement of the color reference region.

Execution of the machine-executable instructions further causes the computational system to select a color fastness value for the sample region by using the pixel wise color fastness value of the individual pixels in the sample region. As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as an apparatus, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," "module" or "system." Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer executable code embodied thereon.

Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A 'computer-readable storage medium' as used herein encompasses any tangible storage medium which may store instructions which are executable by a processor or computational system of a computing device. The computer- readable storage medium may be referred to as a computer-readable non-transitory storage medium. The computer-readable storage medium may also be referred to as a tangible computer readable medium. In some embodiments, a computer-readable storage medium may also be able to store data which is able to be accessed by the computational system of the computing device. Examples of computer-readable storage media include, but are not limited to: a floppy disk, a magnetic hard disk drive, a solid state hard disk, flash memory, a USB thumb drive, Random Access Memory (RAM), Read Only Memory (ROM), an optical disk, a magneto-optical disk, and the register file of the computational system. Examples of optical disks include Compact Disks (CD) and Digital Versatile Disks (DVD), for example CD-ROM, CD-RW, CD-R, DVD-ROM, DVD-RW, or DVD-R disks. The term computer readable-storage medium also refers to various types of recording media capable of being accessed by the computer device via a network or communication link. For example, data may be retrieved over a modem, over the internet, or over a local area network. Computer executable code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing. A computer readable signal medium may include a propagated data signal with computer executable code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

'Computer memory' or 'memory' is an example of a computer-readable storage medium. Computer memory is any memory which is directly accessible to a computational system. 'Computer storage' or 'storage' is a further example of a computer-readable storage medium. Computer storage is any non-volatile computer-readable storage medium. In some embodiments computer storage may also be computer memory or vice versa.

A 'computational system' as used herein encompasses an electronic component which is able to execute a program or machine executable instruction or computer executable code. References to the computational system comprising the example of "a computational system" should be interpreted as possibly containing more than one computational system or processing core. The computational system may for instance be a multi-core processor. A computational system may also refer to a collection of computational systems within a single computer system or distributed amongst multiple computer systems. The term computational system should also be interpreted to possibly refer to a collection or network of computing devices each comprising a processor or computational systems. The machine executable code or instructions may be executed by multiple computational systems or processors that may be within the same computing device or which may even be distributed across multiple computing devices.

Machine executable instructions or computer executable code may comprise instructions or a program which causes a processor or other computational system to perform an aspect of the present invention. Computer executable code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages and compiled into machine executable instructions. In some instances, the computer executable code may be in the form of a high-level language or in a pre-compiled form and be used in conjunction with an interpreter which generates the machine executable instructions on the fly. In other instances, the machine executable instructions or computer executable code may be in the form of programming for programmable logic gate arrays.

The computer executable code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).

Aspects of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It is understood that each block or a portion of the blocks of the flowchart, illustrations, and/or block diagrams, can be implemented by computer program instructions in form of computer executable code when applicable. It is further under stood that, when not mutually exclusive, combinations of blocks in different flowcharts, illustrations, and/or block diagrams may be combined. These computer program instructions may be provided to a computational system of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the computational system of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

These machine executable instructions or computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

The machine executable instructions or computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.

A 'user interface' as used herein is an interface which allows a user or operator to interact with a computer or computer system. A 'user interface' may also be referred to as a 'human interface device.' A user interface may provide information or data to the operator and/or receive information or data from the operator. A user interface may enable input from an operator to be received by the computer and may provide output to the user from the computer. In other words, the user interface may allow an operator to control or manipulate a computer and the interface may allow the computer to indicate the effects of the operator's control or manipulation. The display of data or information on a display or a graphical user interface is an example of providing information to an operator. The receiving of data through a keyboard, mouse, trackball, touchpad, pointing stick, graphics tablet, joystick, gamepad, webcam, headset, pedals, wired glove, remote control, and accelerometer are all examples of user interface components which enable the receiving of information or data from an operator.

A 'hardware interface' as used herein encompasses an interface which enables the computational system of a computer system to interact with and/or control an external computing device and/or apparatus. A hardware interface may allow a computational system to send control signals or instructions to an external computing device and/or apparatus. A hardware interface may also enable a computational system to exchange data with an external computing device and/or apparatus. Examples of a hardware interface include, but are not limited to: a universal serial bus, IEEE 1394 port, parallel port, IEEE 1284 port, serial port, RS-232 port, IEEE-488 port, Bluetooth connection, Wireless local area network connection, TCP/IP connection, Ethernet connection, control voltage interface, MIDI interface, analog input interface, and digital input interface.

A 'display' or 'display device' as used herein encompasses an output device or a user interface adapted for displaying images or data. A display may output visual, audio, and or tactile data. Examples of a display include, but are not limited to: a computer monitor, a television screen, a touch screen, tactile electronic display, Braille screen, Cathode ray tube (CRT), Storage tube, Bi-stable display, Electronic paper, Vector display, Flat panel display, Vacuum fluorescent display (VF), Light-emitting diode (LED) displays, Electroluminescent display (ELD), Plasma display panels (PDP), Liquid crystal display (LCD), Organic light-emitting diode displays (OLED), a projector, and Head-mounted display.

Brief description of the drawings

In the following embodiments of the invention are explained in greater detail, by way of example only, making reference to the drawings in which:

Fig. 1 illustrates an example of a portable computing device;

Fig. la illustrates a further example of a portable computing device;

Fig. lb illustrates a further example of a portable computing device;

Fig. 2 illustrates an example of a digital image; Fig. 2a illustrates a further example of a digital image;

Fig. 3 illustrates a method of operating the portable computing device of Fig. 1;

Fig. 4 illustrates an example of a value histogram;

Fig. 5 Illustrates a view of the user interface of the portable computing device; and

Fig. 6 Illustrates a further view of the user interface of the portable computing device.

Detailed Description

Like numbered elements in these figures are either equivalent elements or perform the same function. Elements which have been discussed previously will not necessarily be discussed in later figures if the function is equivalent.

Fig. 1 illustrates an example of a portable computing device 100. The portable computing device 100 is shown as comprising a computational system 102 that is in communication with a memory 104, a user interface or display 106, and a hardware interface 108. The hardware interface 108 enables the computational system 102 to control the operation and function of various components of the portable computing device 100. The computational system is in communication with a camera 110 via the hardware interface 108. The camera 110 has a field of view 112, which is shown as imaging a color chart 114 as well as a fabric reference 116 and a fabric sample 118. The fabric reference 116 and the fabric sample 118 have been prepared according to a protocol or procedure for measuring color fastness of the fabric.

The memory 104 is shown as containing machine-executable instructions 119. The machine-executable instructions 119 enable the computational system 102 to perform various control and imaging tasks. Execution of the machine-executable instructions 119 causes the computational system 102 to control the camera system to acquire a digital image 120 of the field of view of the camera 110 which is then stored in the memory 104. The memory 104 is further shown as containing a location of a reference region 122. This corresponds to the location of the fabric reference 116. The memory 104 is further shown as containing a location of a sample region 124. This corresponds to the location of the fabric sample 118 in the digital image 120.

The memory 104 is further shown as containing a location of a color reference region 126. This corresponds to the location of a color chart 114 in the digital image 120. The memory 104 is further shown as containing reference color distances 136 assigned to each of multiple color fastness values 132.

The memory 104 is further shown as containing a pixel wise color fastness value 138 for pixels in the sample region 124 that were calculated by comparing the reference color distance 136 of each of the multiple color fastness value to the pixel wise sample color distance 134. The memory 104 is further shown as containing a color fastness value 140 that was selected using the pixel wise color fastness values 138.

The user interface and/or display 106 is further shown as containing a rendering of the color fastness value 140 as well as the digital image 120.

Fig. la illustrates a further example of a portable computing device 100' that is similar to that illustrated in Fig. 1. In the example illustrated in Fig. la, the color reference region comprises at least one reference color region 130 and multiple graded color regions 128. The color reference region in this example is a grey scale or blue scale which could be used for a color fastness test.

The memory 104 is shown as containing a location of a color fastness reference region 126. This corresponds to the location of the color chart 114 in the digital image 120. The memory 104 is further shown as containing the location of graded color regions 128 and the location of original color regions 130 within the multiple color reference regions 126. The memory 104 is further shown as containing color fastness values 132 that are assigned to the various locations of the graded color regions 128. The memory 104 is further shown as containing pixel wise sample color distance 134 between pixels in the sample region 124 and a reference color of the reference region 122. The memory 104 is further shown as containing reference color distances 136 that were calculated by differences between the graded color regions 128 and the original color region or regions 130.

The memory 104 is shown as containing a pixel wise color fastness value 138 for pixels in the sample region 124 that were calculated by comparing the pixel wise sample color distance 134 to the reference color distances 136. The memory 104 is further shown as containing a color fastness value 140 that was selected using the pixel wise color fastness values 138.

Fig. lb illustrates a further example of a portable computing device 100” that is similar to the example illustrated in Fig. 1. In this example, the calibration of the calculation of the pixel wise color fastness value is performed by transforming the digital image to a chosen color space. The digital image 120 contains multiple predefined color regions 141. The memory additionally stores a location 142 of the multiple predefined color regions. The memory further contains a measured color value 144 for each of the predefined color regions 141 determined from the digital image 120. The memory also contains predetermined color values 148 for each of the predefined color regions.

The memory further contains a color transformation algorithm configured to transform the digital image 120 into a digital image 120' in a chosen color space. The color transformation algorithm uses the pairs of predetermined color values 148 and the measured color values 144 for each of the predefined color regions 141 to do this.

The pixel wise color fastness value 138 is then determined using the transformed digital image 120'. The reference color distance 134 is known because the change or delta in the grey values or blue values used for the color scale are defined in the chosen color space.

Fig. 2 shows an exemplary view of a digital image 120 as would be used for the example illustrated in Fig. la. In the digital image 120 the location of the reference region 122 and the location of the sample region 124 are visible. An image of the color chart 114 is indicated by the location of the multiple color reference regions 126. Within this region 126 are pairs of graded color regions 128 and original color regions 130. Above each of the pairs of graded color regions 128 and original color regions 130 are color fastness values 132. Also visible are optional optical markings 200 which are optionally used to identify the location of the various original color regions 130 and graded color regions 128 within the multiple color reference regions 126.

Fig. 2a shows an exemplary view of a digital image 120 as would be used for the example illustrated in Fig. lb. In the digital image 120 the location of the reference region 122 and the location of the sample region 124 are also visible. An image of the color chart 114 is visible. Within this region 126 are multiple predefined color regions 141. Also visible are optional optical markings 200 which are optionally used to identify the location of the predefined color regions 141.

Fig. 3 shows a flowchart which illustrates a method of operating the portable computing device 100 of Fig. 1. First, in step 300, the computational system 102 controls the camera 110 to acquire the digital image 120. Next, in step 302, the reference region 122 is identified in the digital image 120. Next, in step 304, the location of the sample region 124 is identified in the digital image 120. Then, in step 306, a color reference region 126 is identified in the digital image 120. This for example may be done using optional optical markings 200 or may be done using an algorithm or neural network.

Next, in step 308, a reference color distance for each of multiple the color fastness values 140 is received. Next, in step 310, a pixel wise sample color distance 134 is calculated between a sample color value of individual pixels in the sample region 124 and a reference color of the reference region 122. Next, in step 314, a pixel wise color fastness value 138 is calculated by comparing the reference color distances 136 of each of the multiple color fastness values to the pixel wise sample color distance 134. Finally, in step 316, a color fastness value 140 is selected for the sample region 124 using the pixel wise color fastness values 138.

Fig. 4 shows an example of a value histogram 400 that was constructed using the pixel wise color fastness values 138. The histogram 400 comprises bins 402 for each of the color fastness values 132. In the histogram it can be seen that there are some values which can be ignored 406 and then the histogram 400 can be looked at in different ways to determine the color fastness value 140. For example, the bin with the maximum 404 may be taken in one example and various averages or mean values may also be calculated.

Although in the example shown in Fig. 4 is for a value histogram for the pixel wise color fastness value 130. A value histogram could also be constructed for the pixel wise sample color distances.

Fig. 5 illustrates one example of the user interface or display 106. The display contains a view of the current digital image 120 as well as a repositioning indicator 500 which tells the operator how to move the portable computing device to improve the quality of the color fastness value 140. In this case, there is an arrow showing which direction the portable computing device or the field of view of the camera should be moved.

Fig. 6 shows a further view of the user interface or display 106. Again, the digital image 120 is shown. In this case, an image quality warning 600 is displayed. In this case the system detected that there was insufficient light to perform an accurate measurement of the color fastness value 140. There are additional instructions 602 which tells the operator what to do to correct the situation. List of Reference Numerals

100 portable computing device

102 computational system

104 memory

106 user interface / display

108 hardware interface

110 camera

112 field of view

114 color chart

116 fabric reference

118 fabric sample

119 machine executable instructions

120 digital image

122 location of reference region

124 location of sample region

126 location of color reference region

128 location of graded color region

130 location of original color region

132 color fastness values

134 pixel wise sample color distance

136 reference color distances

138 pixel wise color fastness values

140 color fastness value

141 multiple predefined color regions

142 location of multiple predefined color regions

144 measured color values

146 transformed digital image

148 predetermined color values200 optical markings

300 control the camera to acquire the digital image identify a reference region within the digital image identify a sample region within the digital image identify a color reference region in the digital image receive a reference color distance for each of multiple color fastness value to each of the multiple graded color regions calculate a pixel wise sample color distance between a sample color value of individual pixels in the sample region and a reference color of the reference region calculate a pixel wise color fastness value of the individual pixels in the sample region by comparing the reference color distance of each of the multiple color fastness values to the pixel wise sample color distance select a color fastness value for the sample region using the pixel wise color fastness value of the individual pixels in the sample region value histogram bins maximum value values to ignore repositioning indicator image quality warning instructions