Abstract:
A system is disclosed for monitoring paint color across regions of a vehicle, for identifying color mismatches, and for dynamically determining the acceptability of an identified mismatch. The system includes a vehicle image acquisition array digital cameras for digitally scanning selected regions of the vehicle and an image analyzer connected to the vehicle image acquisition system. The image analyzer is programmed with standard confidence color curves and includes software programmed with an analysis algorithm to convert an image of a scanned region into a standard image format. Individual color curves are extracted from the standard format to compare the extracted color curves against the standard confidence color curves to determine whether or not the extracted color curves fall within standard confidence color curves. The standard confidence color curves may be adjusted during color testing based upon accumulated extracted color curves.

Description:
TECHNICAL FIELD 
     The disclosed invention relates generally to a system for monitoring paint colors once the colors have been applied to a vehicle. More particularly, the disclosed invention provides a system for monitoring paint color across regions of a vehicle by identifying color mismatches and for dynamically determining the acceptability of an identified mismatch. 
     BACKGROUND OF THE INVENTION 
     In the early days of the automobile the exterior finish was relatively primitive both in method of application and formulation. When first introduced, the Ford Model T was hand painted using a rake-like device which poured on flowing paint, the excess of which was captured in a tub for re-use. The paint itself was a solvent-based varnish. While available in such colors as blue and green these colors appeared to be virtually black unless viewed in bright sunlight due to the varnish paint. This characteristic contributed to the mistaken belief that all Model T&#39;s were black. 
     As with all other areas of automotive technology painting techniques and paint composition have developed dramatically since those early days. Today&#39;s paints, including environmentally-friendly powder coatings which are based on acrylic binder chemistry and thus contain no solvents, are applied in far more sophisticated ways to achieve more durable and appealing results. Today&#39;s paints include aluminum flakes, micas, and other particulates to create desired pigmentation. These paints are highly durable and resist fading even when exposed to the harshest environment. 
     Despite significant advances in knowledge of the composition and characteristics of automotive paint, as new and more complex exterior paint colors are introduced to the automotive industry, unique challenges remain in controlling the quality and accuracy of the application of these colors. While the use of new pigments is one method of creating new colors, another is through the creation of multiple color layers in the paint system, giving rise to a unique appearance. These color systems include, but are not limited to two-tones, tri-coats, and tinted clearcoat systems. With these new technologies come unique challenges in controlling the quality of the paint colors, insuring that not only are the correct color layers applied, but that they are applied correctly over the entire vehicle. 
     In some cases, an error in the color application is not obvious to the casual observer, and the defective vehicle will make it through final assembly before the defect is identified. To correct the error, a paint repair is done to the unit in order to make it acceptable for delivery. This repair is costly in terms of time, labor and material. 
     Accordingly, a simple, automated system is needed to monitor the paint color across critical color regions on the vehicle and to identify when gross color mismatches occur while allowing for the acceptable part-to-part color shifts that occur in production throughout the day. 
     SUMMARY OF THE INVENTION 
     The disclosed invention provides a system for monitoring paint color across regions of a vehicle that is cost-effective and efficient. The disclosed system provides a system that is capable of identifying color mismatches and for dynamically determining the acceptability of an identified mismatch. The disclosed system includes a vehicle image acquisition array for digitally scanning selected regions of the vehicle and an image analyzer connected to the vehicle image acquisition system. The vehicle image acquisition array includes one or more digital cameras either positioned on one or more robots or on a stationary halo. The image analyzer is initially programmed with upper and lower standard confidence color curves such as RGB, L*ab, and XYZ. 
     The image analyzer includes software programmed with an analysis algorithm to convert an image of one of the scanned regions acquired by the vehicle image acquisition array into a standard image format from which actual individual color curves are extracted. The extracted color curves are compared against the standard confidence color curves to determine whether or not the extracted color curves fall within the upper and lower standard confidence color curves by establishing a percentage match for one of the scanned regions. The initially programmed upper and lower standard confidence color curves may be adjusted during color testing based upon accumulated extracted color curves of the selected regions. 
     The image acquisition and analysis process of the disclosed invention allows for the rapid examination of critical color regions on a vehicle to identify defects in color application. The analysis algorithm used to determine the overlap region can be used to identify differences not only in the color curves, but also other identifier curves, such as a reflection spectrum, or even an absorbance spectrum. This technique of the disclosed invention has the potential for use in multiple industries beyond the automotive industry and in fact can find application in any industry that requires color matching, such as the automotive refinish industry, the textile industry, or the printing industry. 
     Implementation of the color verification system of the disclosed invention would allow for the automated checking of critical color points on every vehicle that exits the paint shop. Upon identifying the target color of the vehicle being processed (via vehicle tracking), the standard confidence intervals for the assigned color would be read into the system. The image acquisition sequence would then begin in which the critical color points on the test vehicle would be imaged. From the images, the color curves would be calculated for each color point. These test points are then be compared to the standard confidence intervals, calculating the percentage match for each point measured on the vehicle. If the percent match fails to meet minimum levels for any color point, the vehicle is flagged for a visual inspection. Advantageously, the entire process would take no longer then a few seconds. Use of the process would improve quality, reduce costs, and require limited capital investment. 
     Other advantages and features of the invention will become apparent when viewed in light of the detailed description of the preferred embodiment when taken in conjunction with the attached drawings and the appended claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a more complete understanding of this invention, reference should now be made to the embodiments illustrated in greater detail in the accompanying drawings and described below by way of examples of the invention wherein: 
         FIG. 1  is a diagrammatic view of the rapid color verification system of the disclosed invention in which the vehicle image acquisition array and the image analyzer are shown relative to a subject vehicle; 
         FIG. 2  is an exemplary graph illustrating a color curve extracted from a digital color image; 
         FIG. 3  is an exemplary graph illustrating a color curve with a high confidence interval around each of the three illustrated curves; 
         FIG. 4  is an exemplary graph illustrating sample blue standard curves plotted against a blue test panel curve; and 
         FIG. 5  is a tabulate analysis of the color curve set of  FIG. 3 . 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     In the following figures, the same reference numerals will be used to refer to the same components. In the following description, various operating parameters and components are described for different constructed embodiments. These specific parameters and components are included as examples and are not meant to be limiting. 
     Referring to  FIG. 1 , a diagrammatic view of the rapid color verification system of the disclosed invention, generally illustrated as  10 , is shown relative to a subject vehicle  12 . It should be understood that the system  10  as shown is set forth for illustrative purposes only and is not intended as being limiting. For example, rather than the illustrated vehicle  12  other types of vehicles may be monitored by the system  10 . In addition, the disclosed invention is not limited to monitoring of colors on vehicles, but may also be used for monitoring the paint colors on any one of a variety of articles, including for example refrigerators, aircraft and furniture. The system  10  of the disclosed invention may thus find any use where a paint coat is used. 
     The system  10  includes a vehicle image acquisition array comprising a digital camera  14 . While only a single camera  14  is shown it is to be understood that a greater number of digital cameras may be used. Preferably the camera  14  is of the single lens reflex variety and is equipped with a macro lens such as a 90 mm macro lens. This arrangement is not mandatory as any camera and lens combination having the ability to focus at close distances (for example, ˜28 cm to the sensor) and sufficient resolution would suffice. 
     The camera  14  needs to be able to image a certain area, for example, an area of ˜5 cm×˜3.5 cm may be imaged, although a larger or small range may be imaged. First, the camera  14  takes an image of the area in RAW format. The shutter speed and aperture of the camera  14  are held constant across all sample and control images for a specific color. Preferably the camera  14  is set on the smallest aperture possible (that is, the largest f-stop) for the associated lens to maximize the depth of field of the area being imaged. Lighting can be provided by any light source that supplies an even illumination across the imaged region such as the dual strobe lights  16  and  16 ′. It is to be understood that while the dual strobe lights  16  and  16 ′ are shown. Alternatively, any strobe flash, such as a ring or a macro flash, can be used as can an on-camera flip-up flash. Regardless of the form of lighting, the lighting arrangement must be able to have its intensity adjusted based on the target color to eliminate overexposure. The imaged region must also be free of any glare and reflections from the surrounding lights and area, as this will cause errors in the measured color curves. As is understood by those skilled in the art, higher intensity lights or flashes can reduce reflections better than diffuse light sources. 
     The system  10  further includes an image analyzer  18  connected with the vehicle image acquisition array  14 . The image analyzer  18  includes a program having an algorithm that can rapidly examine a painted area and relatively compare its color curves to a set of preprogrammed, previously calculated confidence intervals to establish percentage match for the particular area of the vehicle being imaged. Typically the color curves would be RGB curves, but it should be noted that the technique of the disclosed invention is not limited to RGB curves, and can be used with other color space systems such as L*ab and XYZ curves and the like. 
     The image acquired by the system  10  is a RAW image. This image is then processed (for example, by Photoshop®) and is set to a previously determined standard color temperature and tint level. The processed images are then converted into a standard image format. Such standard image formats include, without limitation, JPG, TIFF and GIF. The individual color curves are them extracted and saved for each associated image as set forth in the exemplary graph illustrated in  FIG. 2  in which RGB color curves are illustrated with red color being curve  20 , green color being curve  30 , and blue color being curve  40 . 
     Each pixel has an associated color value and each color image has a number of bins associated with it. The number of bins depends on the bits per color image. As a preferred but non-limiting example, an 8-bit color image may have associated with it up to 256 bins (2^8=256), or between 0-255 bins. However, higher bit color images may be used, such as 10-bit, 12-bit, 14-bit or higher. The increased number of bits simply provides more bins to analyze. Again using RGB as an example, the color value of RGB with 8 bits per color image is between 0-255 bins. Continuing with this example, the RGB curves represent the number (count) of pixels with an RGB value of an exemplary 8-bit color image within each bin between 0-255 
     The method/algorithm used to compare test images to color “standard” images (as indicated by the user or by computerized identification) starts with the creation of a color fingerprint for each “standard” color&#39;s individual curve. This is done by examining a plurality of color images (preferably at least six color images) that are considered to be color accurate. The color curves from these samples are used to calculate the confidence interval range for the samples. The confidence interval range is characterized as a percentage which represents the alpha range used to calculate the confidence/prediction interval. This figure represents the percentage of correct values desired to be selected at a later time. Particularly, the closer the confidence percentage gets to 100 the wider the confidence internal needs to be in order to assure securing a higher percentage of correct values. Any alpha range may be selected. According to a preferred embodiment of the disclosed invention, the alpha range is between about 90.0% and 99.9%. Again using RGB color curves as an example, an acceptable count range for each individual RGB value is set at between 0-255 as illustrated in  FIG. 3 . 
     With reference to  FIG. 3 , the red color curve  20 , the green color curve  30 , and the blue color curve  40  are again illustrated. In addition to the curves  20 ,  30  and  40 , the upper and lower limits of each curve are also illustrated. Specifically, the lower limit of the red curve  20  is shown as lower limit curve  22  while the upper limit of the red curve  20  is shown as upper limit curve  24 . Similarly, the lower limit of the green curve  30  is shown as lower limit curve  32  while the upper limit of the green curve  30  is shown as upper limit curve  34 . Finally, the lower limit of the blue curve  40  is shown as lower limit curve  42  while the upper limit of the blue curve  40  is shown as upper limit curve  44 . It should be understood that the curves shown in  FIG. 3  are set forth for illustrative purposes only and are not intended as being limiting as other color curves are possible. 
     An important aspect of the disclosed invention is the way in which the system interacts with the vehicle production system. By being able to read the color of the vehicle by way of the electronic tag conventionally attached to the vehicle, the system enables identification of the appropriate color standard with confidence intervals, a relative comparison between the test color and the standard color, and then determines if the applied color is a PASS or a FAIL. If the color is a PASS, then the tested vehicle is allowed to move on. If on the other hand the color is a FAIL, then a notification (in the form of, for example, an alarm or other warning) is communicated with the system operator that the particular vehicle requires further inspection. 
     When checking a test image to see if the color curves fall within the standard confidence intervals, each bin value is checked against the established confidence intervals. If the test count falls within the standard count confidence interval, then that determination is considered as a PASS for that bin. On the other hand, if the  count falls outside of the standard count confidence interval range, then that is considered as a FAIL for that bin. According to the preferred embodiment of the present invention, regions are not tested that are below a set minimum count level in both the test curve and the standard curve because these are regions that do not impact the paint color. 
     Once the PASS bin and FAIL bin determinations are made, the number of PASS bins are then added up and divided by the number of checked bins to provide a percentage match for that individual test curve. As there are three curves (generally, but not exclusively, R, G, and B), the end result of the panel analysis is an R % match, a G % match, and a B % match for a particular test sample. A visual representation of the overlap region is shown as overlap region  50  in  FIG. 4 . A portion of the tabulated analysis of an RGB curve set is shown in  FIG. 5 . The threshold for the PASS/FAIL of a particular test sample is set by the user. 
     As an optional variation of the disclosed invention, in order to allow for acceptable part-to-part color variations, the disclosed invention utilizes a novel method of recalculating the confidence intervals using the most recent “passing” test panels of each individual color. According to this option, the color data could be used to recalculate the confidence intervals in order to provide a living color fingerprint for each color. Specifically, when a new test panel is identified as “passing,” it replaces the oldest test panel used in the previous calculation of the confidence intervals, after which a new confidence interval is calculated and used for the next test. This allows for the confidence intervals to be dynamic and to drift due to part-to-part variations while still allowing the algorithm to identify relative gross color mismatches. The number of test samples used for the confidence interval calculation is set by the user, but, as previously stated, should be at least six samples and may be greater than six samples. However, this variation may not be universally desirable and the disclosed system may be operated without the provision of the living color fingerprint. 
     The foregoing discussion discloses and describes exemplary embodiments of the present invention. Variations of the disclosed invention may be made without deviating from the spirit and scope of the disclosed invention. For example, in addition to the disclosed method of comparing curves, reference may instead be made to a selected metric centered around the shape of the curves. It is also possible to compare curves by averaging the error between the curve determined to be correct and a test curve. Accordingly, one skilled in the art will readily recognize from the foregoing discussion, the accompanying drawings and claims that additional modifications and variations can be made therein without departing from the true spirit and fair scope of the invention as defined by the following claims.