System and method for spectral interpolation using multiple illumination sources

Abstract

The optical detector (110) is provided with a plurality of light sources (Lx) having different spectral characteristics, which are controlled on / off across a range of illumination intensity combinations. The measurement sensor (213) receives the radiated light reflected from the target surface via a restricted optical channel and generates a representative output signal. The local controller (205) or the remote computing device (130) receives the output signal or a corresponding message from the optical detector and generates a low-resolution spectral data set based on the signal for each of the illumination intensity combinations. The low-resolution spectral data set is interpolated based on a machine learning algorithm that is trained using high-resolution data from a reference device to generate a high-resolution spectral data set associated with the target surface.

Description

Technical Field

[0001] The present disclosure relates generally to spectral measurement systems and methods. More particularly, the present disclosure relates to a method for enhancing the spectral resolution of a given measurement system, such as, for example, a color measurement system, by using different combinations of illumination sources. Background Art

[0002] Spectral measurements (e.g., color measurement) are important to many industries, including household consumer products, automotive, and any number of other examples that require portability, accuracy, and cost-effective solutions. Color (particularly the color of a target object) can be characterized by "spectral reflectance," which can be determined directly by the distribution of the proportions of different wavelengths observed over the extent of the visible range in a continuous spectrum. Unfortunately, many conventional high-resolution spectral measurement devices include or implement complex optical systems that make the products too bulky and expensive for many applications.

[0003] Relatively inexpensive and portable spectral measurement devices may be available, but the lower resolution of the associated optical sensors makes these options undesirable due at least to the inaccuracy of the spectral data obtained therefrom. When comparing differences in the way these types of sensors measure color (or generally comparing the difference between two colors), a color difference standard known as ΔE is most typically used. This is a special form of color space "distance" that has been developed to simulate the way the human eye perceives color differences. The process of converting from sensed values ​​and minimizing the ΔE between the sensor reading and the standard color value is where instrument calibration and setup becomes important, and seriously affects the accuracy of the color measurement tool.

[0004] One known issue with tri-stimulus sensor based colorimeters is that they lack the specificity of spectrophotometers due to far fewer independently measurable parameters in the optical spectrum (i.e., an RGB colorimeter might only measure R, G, and B, whereas a spectrophotometer can record high-resolution reflectance curves across the visible spectrum). Intrinsic design differences such as optics, stimulus sources, and detector responses cause colorimeters to produce different results from spectrophotometers and from other colorimeters.

[0005] Due to the aforementioned specificity (and higher perceived accuracy), spectrophotometer measurements are often used as reference standards. Interestingly, there is indeed variation between any two spectrophotometers, and it is common practice to calibrate a spectrophotometer to match the readings of a given reference spectrophotometer. The calibrated accuracy of the device is then based on its agreement with the reference device.

[0006] More generally, all color measurement devices are judged based on their agreement with some reference device. It would therefore be desirable to provide a system and method that would allow a less expensive detector with low spectral resolution to perform accurate measurements that would normally require an expensive high spectral resolution detector. Summary of the invention

[0007] Optical detectors typically have a limited number of narrow spectral bands that they can measure. The total number of bands across a given spectral range defines the spectral resolution of the measurement system. Novel systems and methods as disclosed herein enhance the spectral resolution of a given measurement system by using different combinations of illumination sources.

[0008] In an embodiment, an optical detector as disclosed herein includes a plurality of light sources having different spectral characteristics. In a non-limiting example, the plurality of light sources include at least a first light source having a 6500K light emitting diode (LED), a second light source having a 2700K LED, and a third light source and a fourth light source of an LED having respective spectral characteristics between 2700K and 6500K. The measuring sensor receives light radiated by the plurality of light sources and reflected from a target surface, and further generates an output signal representing the reflected light from the target surface. The controller controls the plurality of light source combinations to be turned off or on across a plurality of illumination intensities. The controller or an external computing device communicatively linked to the optical detector generates a low-resolution spectral data set based on a corresponding output signal from the sensor corresponding to each of the plurality of illumination intensity combinations. The low-resolution spectral data set is programmatically interpolated to generate a high-resolution spectral data set associated with the target surface.

[0009] In another embodiment, the controller and / or external computing device is functionally linked to a data storage device that includes stored high resolution spectral data corresponding to a reference device.

[0010] In another embodiment, the controller and / or external computing device is configured to predict a high-resolution spectral data set from a reference device and corresponding to the target surface based on the sensed low-resolution spectral data set.

[0011] In another embodiment, an optical detector includes a housing having an outer perimeter and a face. A plurality of light sources reside within the housing and are offset from the face to define an interior within the perimeter of the housing, wherein the face is configured to engage a target surface around the perimeter of the housing such that ambient light is substantially restricted from entering the interior. When the face of the optical detector engages the target surface, the sensor is arranged to receive light radiated by the plurality of light sources and reflected from the target surface.

[0012] In another embodiment, a plurality of light sources and sensors are mounted on a substrate oriented parallel to a face of the housing, and the light sources are disposed circumferentially around a sensor aperture positioned between the sensors and the face of the housing.

[0013] In another embodiment, the measurement sensor is configured to receive light projected from a plurality of light sources via respective restricted optical channels and reflected from a target surface, and the optical detector further comprises an auxiliary sensor configured to measure light reflected from one or more of the plurality of light sources.

[0014] In another embodiment, the controller and / or the external computing device is configured to normalize the output signal from the measurement sensor to the output value from the auxiliary sensor.

[0015] Many other objects, features and advantages of the present disclosure will be readily apparent to those skilled in the art when reading the following disclosure in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an overall diagram representing an embodiment of a system as disclosed herein.

[0017] Figure 2 is a front view of an embodiment of a spectrum measurement device as disclosed herein.

[0018] Figure 3 yes Figure 2 Isometric view of the device represented in .

[0019] Figure 4 It means according to Figure 2 Diagram of exemplary pathways of radiated and reflected light in a device.

[0020] Figure 5 is a graphical waveform diagram representing the spectral detector resolution of an exemplary low-resolution eight-channel detector having eight narrow bandpass optical filters.

[0021] Figure 6 is a graphical waveform diagram representing the spectral detector resolution of an exemplary high resolution thirty-one channel spectrometer.

[0022] Figure 7 is a graphical waveform diagram representing the spectral distribution of an exemplary 6500K LED light source.

[0023] Figure 8 is a graphical waveform diagram representing the spectral distribution of an exemplary 2700K LED light source.

[0024] Fig. 9 is a block diagram representing an exemplary interpolation process according to an embodiment of the method as disclosed herein.

[0025] Fig.10 is a block diagram representing an exemplary algorithm training process according to an embodiment of the method as disclosed herein.

[0026] Fig.11 is a graphical waveform diagram representing an exemplary collection of low-resolution spectral reflectance data.

[0027] Fig.12 is a graphical waveform diagram representing an exemplary set of interpolated spectral reflectance data relative to reference instrument data. DETAILED DESCRIPTION

[0028] Overall reference Figure 1-12 , the spectral measurement system and method according to the present disclosure may now be described in more detail. Where various figures may describe embodiments that share various common elements and features with other embodiments, similar elements and features are given the same reference numerals and their redundant description may be omitted below.

[0029] In such Figure 1 In the embodiment represented in , the spectral measurement system 10 as represented may include, but is not limited to, a measurement device 110 and a computer program product 140. The program product (e.g., program instructions) may typically reside on a computer-readable medium of a user computing device 130 (e.g., typically a mobile computing device such as a smart phone or tablet computer, but also including a desktop computer, etc.) that is external to or remote from the measurement device and executable from a mobile computing device. As further described below, the program instructions may be downloaded in their entirety from a host server (not shown) to the user computing device, or in various embodiments, the computer program product may be a distributed program that includes one or more program modules executable from the user computing device and one or more program modules executable from the host server via a communication network 120. For purposes of the presently disclosed systems and methods, the user computing device may include a processor 144 that may be configured to execute program modules, and may further include an onboard data storage device 142 and / or be functionally linked to a remote data storage device.

[0030] The term "communication network" as used herein with respect to data communication between two or more devices or otherwise between communication network interfaces associated with two or more devices may refer to any one or a combination of any two or more of a telecommunications network (whether wired, wireless, cellular, etc.), a global network (such as the Internet, a local wired or wireless network, a network link), an Internet Service Provider (ISP), and intermediate communication interfaces, modems, etc.

[0031] The computer program product 140 of the spectral measurement system 100 of the present disclosure may include computer instructions resident on a non-transitory computer-readable medium 142 in a computing device 130, such as, for example, a smart phone or tablet computer. Alternatively, the computer program product may reside in a non-transitory computer-readable medium, such as a server system (not shown) remote from the computing device but communicatively linked to the computing device, such that execution of the computer program product directs execution of the sensing, storing, and comparing methods according to the present disclosure, at least a portion of which includes steps for generating a user interface on a display of the computing device and receiving user input regarding, for example, various display parameters.

[0032] Next reference Figure 2-4 , an embodiment of a spectral measurement device 110 as disclosed herein includes a housing 201 having an aluminum shell 202 along the length of an outer portion and shaped to define an inner portion.

[0033] At a first end of the device, an LED indicator ring 203 is disposed around an actuator such as a button 204. The button may be configured for user interaction to, for example, turn the device and / or associated functions on and off, wherein the LED indicator may be selectively turned on and off depending on the characteristics activated by the button. The LED indicator may be adjusted to produce one or more colors or lighting intensities to correspond to the same number of device states, conditions, etc.

[0034] In the interior near the first end, a controller with device logic can be mounted on a first printed circuit board (PCB) 205. In the illustrated embodiment, a second PCB 208 is provided, which is separated from the first PCB and arranged in parallel with the first PCB, and a measurement sensor 213 and one or more illumination sources Lx, such as an array of light emitting diodes (LEDs) are mounted on the second PCB 208. The second PCB may include appropriate circuits and / or programs combined with the measurement sensor, which are effective for controlling gain, pre-scaler and integration time, for example, in order to fine-tune the sensor for a given environment. In some embodiments, a cover made of rubber or equivalent can be provided, together with an orifice of 1.00 mm to 1.59 mm, which is configured to further allow only reflected light from the target sample to reach the measurement detector. The measurement detector generally has a relatively low resolution, as discussed in more detail below. A power source 206, such as a battery, is provided in a cavity between the first PCB and the second PCB, and is configured to deliver necessary power to the components thereon, for example, via electrical leads or wires. The second PCB also deviates from the second end of the device defined in part by a face shaped to engage the target surface. When the face is arranged to engage a target surface, the housing may thus effectively limit (ideally prevent) ambient light and otherwise enable only light radiated from the light source and reflected back from the target surface of interest to be received by the measurement sensor.

[0035] In this example, a plurality of LEDs (L1, L2, ... Lx) are arranged circumferentially around the measurement sensor 213 to emit light through a restricted optical channel 210 and so as to reflect off a target surface engaged by the second end of the device. In an embodiment, the face of the second end of the device includes a thin transparent protective window that effectively engages the target surface, and the restricted optical channel desirably prevents stray light from contaminating the light detection and associated spectral reflectance determination. A white reflector 215 may be disposed along the side of the optical channel. The reflected light is directed to the measurement sensor through a central channel 209. In an embodiment, the defined peripheral channel may extend away from the central channel at an angle (e.g., 45 degrees) relative to a reference plane of the face of the second end of the device, and the central channel extends at an angle of ninety degrees relative to the reference plane of the face.

[0036] In the embodiment as shown, a dust cap 212 may be disposed around the second end of the device housing 201 and connected to the housing via, for example, corresponding magnetic elements 211 .

[0037] In an embodiment, a reference detector 216 may be provided on the side of the spectrum measurement detector 213 on the second PCB 208. The measurement detector measures the light that has been reflected from the target measurement surface. In contrast, the reference detector measures the reflected light from one or more LEDs to account for changes in the spectrum of the light source due to changes in temperature. All measurements from the measurement detector may be correspondingly normalized by the controller (or by a remote processing unit configured to analyze the spectrum measurement data from the device) to the value obtained from the reference detector.

[0038] In general, spectral emissivity relationships are known to characterize the reflectivity of a target surface at various wavelengths. Spectral resolution as referred to herein describes the ability of a measurement detector to define a wavelength interval, wherein a higher resolution measurement detector can naturally produce a narrower wavelength range for any one particular channel. Otherwise stated, a measurement detector having a particular wavelength range will have a maximum allowable number of individual wavelengths based on its spectral resolution, wherein a higher resolution device allows for a greater number of individual wavelengths / channels. The measurement detector 213 as used in the exemplary system disclosed herein can be, for example, an eight-channel detector having eight narrow bandpass optical filters, which have a wavelength range as described in Figure 5 The relatively low spectral resolution is shown in the filter transmittance of . As mentioned before, the total number of bands across a given spectral range defines the spectral resolution of the measurement system. Therefore, this type of detector is obviously inferior to, for example, a detector having Figure 6 The filter transmittance is shown in Figure 1 for a high resolution thirty-one channel spectrometer with a spectral resolution that is effective. The systems and methods disclosed herein can enhance the otherwise limited spectral resolution of a measurement detector by using different combinations of illumination sources.

[0039] In certain embodiments, the light source is composed of a plurality of spectrally different but stable illumination sources (L1, L2, ... Lx). For example, the first illumination source L1 may be a light source having a wavelength such as Figure 7 , where the relative emission intensity is higher at lower wavelengths on the spectrum and gradually decreases at higher wavelengths. The second illumination source L2 in the array Lx may be a 6500K LED having a spectral distribution as shown in FIG. Figure 8 , where the relative emission intensity increases at progressively higher wavelengths until peaking at approximately 630 nm. Additional illumination sources may be provided having different spectral distributions, for example between the 2700K and 6500K characteristics described above.

[0040] When the spectral measurement sequence is activated, the controller may deliver control signals to drive the corresponding LEDs (L1, L2, ... Lx) according to various predetermined combinations of illumination intensities. Since the LEDs may have illumination intensities corresponding to the current driven through the LEDs, the control signals may have the effect of regulating the output current from the DC power supply in a manner known in the art, where a maximum output current corresponds, for example, to 100% illumination intensity, and multiple smaller output current values ​​may correspond to similar multiple illumination intensities. For each combination of illumination intensities, the measurement detector 213 captures the corresponding reading and causes it to be recorded.

[0041] Next reference Fig. 9 , an exemplary set of combinations may include a first spectral distribution stage, in which the first light source L1 is controlled to 100% illumination intensity and the second light source L2 is controlled to 0%. In the second spectral distribution stage, the first light source is controlled to 70% illumination intensity and the second light source is controlled to 30%. In the third spectral distribution stage, the first light source is controlled to 30% illumination intensity and the second light source is controlled to 70%. In the fourth spectral distribution stage, the first light source is controlled to 0% illumination intensity and the second light source is controlled to 100%. These stages are of course exemplary only, as alternative combinations and spectral distribution combinations including variations of more than two light sources are also within the scope of the present disclosure. For each different ratio of the illumination intensity of two (or more) light sources, a normalized low-resolution spectral data set is generated. The various low-resolution spectral data sets are then interpolated using, for example, machine learning techniques to provide a resulting high-resolution spectral data set.

[0042] In certain embodiments involving color measurement, a matching table associated with one or more batches of data may be provided. The matching table may include data correspondence information, such as conversion or mapping information between color spaces or reference data. Each batch may include at least one color sensing device with an individual sensor calibration, and may be configured, for example, to group multiple color sensing devices according to associated ΔE values. As previously described, the ΔE value is a color difference standard that traditionally refers to a color space "distance" related to the color difference perceived when viewed by the human eye. Each batch may be associated with an absolute adjustment that is used to align the color sensing device with a specified reference device by establishing a relationship between the devices. This relationship can be modeled as a mathematical function, where the input is the unadjusted color reading of the device to be aligned, and the output is the color reading of the reference device corresponding to the data stored in the matching table. The mathematical function may correspond to the following equation:

[0043]

[0044] Due to the potential complexity of this function, it may be difficult to find it using purely mathematical techniques. This difficulty can be overcome by reformulating the problem as a supervised learning problem, for which a function approximation algorithm can be used.

[0045] As understood in the art, supervised learning is a branch of machine learning in which a learning algorithm is presented with a set of inputs and corresponding correct outputs. The algorithm can then "learn" the relationship between these inputs and outputs and generate a model that can be used to predict future unknown data based on the data it has been trained to learn. Therefore, in various embodiments, the strategies proposed herein may involve obtaining unadjusted spectral data and reference device data for a set of specially formulated spectral data coefficients. This data can then be used to create a model for use in converting a "raw" low-resolution data set into "adjusted" high-resolution data.

[0046] In an embodiment, the interpolation process can be facilitated by a trained artificial neural network (ANN). ANN is a machine learning method that is able to estimate very complex functions present in a training set. In one embodiment, the ANN can be trained using a standard gradient descent back-propagation algorithm. Fig.10 As illustrated in , a set of training samples is used to "train" the measurement system, which has known spectral data as measured by a high-resolution reference instrument. A large set of samples is collected from a relatively low-resolution measurement device, thereby producing a scan set of sample 1, sample 2, sample 3, and sample 4. Each set of raw data is normalized to produce a low-resolution spectral data set. A machine learning algorithm can then be applied to train the ANN based on specific training samples to predict the high-resolution output from the reference device (i.e., from the measurement device and / or any appropriately aligned or grouped device) corresponding to the raw data generated from the measurement system. In an embodiment, specific training is performed to identify the coefficients that produce the minimum possible error for a given raw sample of spectral data relative to the corresponding high-resolution spectral data. The ANN can now be implemented using a measurement system as disclosed herein to predict high-resolution spectral data based on new or subsequent raw data.

[0047] refer to Fig.11 , the first example data set represents low-resolution spectral reflectance data as captured in its original form for each of a plurality of lighting intensity combinations. Fig.12 , representing the same spectral reflectance data in an interpolated form relative to the reference instrument data, where any given set of spectral data can reliably correspond to the predicted high-resolution values. Thus, even relatively inexpensive detectors with low spectral resolution can be used to make accurate measurements that would normally require much more expensive high-spectral resolution detectors.

[0048] In an embodiment, referring again to the color measurement example, a supervised training set of raw scans and reference scans can be stored in a database. When an unadjusted color reading needs to be aligned, a K-nearest neighbor (KNN) algorithm can search the database for the K scans closest to the unadjusted reading. The KNN algorithm can then return a weighted mean of the K corresponding reference scans. The results of the KNN algorithm are very accurate for colors within or near a specified color set.

[0049] In an embodiment, the system may include a supplemental measurement surface adjustment. For example, the measurement device illuminates the measurement surface with a known light source and captures the portion of this light that is reflected off the surface. The reflection of this light may vary based in part on the surface texture and translucency of the substrate. It is very likely that the desired reference device may capture light from a rough surface texture differently than the misaligned measurement device. These effects may be minimal for surfaces with subtle textures, but some materials (such as brick or carpet) may be so porous that they will greatly affect the response of the reflected light. In order to accurately align with the reference device for a given surface texture, the supplemental adjustment can be used continuously after the absolute adjustment. This allows the absolute adjustment to make the modifications needed to align the spectral data measurement device with the desired reference device, as well as allowing the supplemental adjustment to handle any kind of offset that may occur due to surface texture.

[0050] In addition to the supplementary adjustment for surface texture, it is also possible in an exemplary embodiment to avoid any real alignment and build a database of unadjusted measurements for a given spectral data set. This can then be used to simply "match" an unknown scan to the closest known spectral data set in the database.

[0051] An exemplary system may include a reference library where all measurement devices may be aligned to each other and to the reference device once the batch-specific absolute adjustment and complementary adjustment techniques have been applied. Thus, the measurement devices will be able to provide reference aligned readings, and a single reference library will work for each batch of measurement devices. Notably, as described above, the reference device is not limited to a specific device, but rather any suitable high resolution device may be defined as a reference device according to the present disclosure.

[0052] Once all the data required to build an alignment model for a single spectral measurement device has been obtained, the measurement device itself must be able to access the data. The data can be stored in direct memory on the measurement device. However, one problem will be whether new alignment methods can be created, which causes the measurement device to become obsolete. Updating individual devices to work with new alignment methods would be very inefficient.

[0053] As mentioned above, a solution to this is to implement an embodiment that is configured to store, maintain and update the data in a cloud database, which can be accessed by a measuring device, for example, via an Internet-connected client device. The database according to this exemplary embodiment can be utilized in two main ways, and each mode naturally requires a certain level of Internet connectivity to access the cloud database. Currently, Bluetooth radio technology can allow a device to dock with one or more Internet-connected client devices, which can be configured to serve as a cloud client. Alternatively, the measuring device can include onboard Internet connectivity, which allows the measuring device to serve as its own cloud client. Alternatively, or in conjunction with these exemplary embodiments, the measuring device can include onboard Internet connectivity, which allows the measuring device to be connected to an external device used as a cloud client or cloud service.

[0054] The cloud client can interact with the cloud service in a variety of ways. In one embodiment, the cloud client can be connected to the cloud service. The measuring device can be connected to an Internet-enabled device that will act as a cloud client. The measuring device can relay its unique ID to the cloud service via the cloud client. The cloud service can respond with alignment data unique to the measuring device. This information can be stored in the Internet-enabled device, where a software application can perform processing to utilize the alignment data and process the alignment spectral data measurement of the reference. Alternatively, this information can be transmitted to the measuring device, which can perform processing to utilize the alignment data and the alignment spectral data measurement of the reference.

[0055] In one embodiment, the above-mentioned software applications that interact with the cloud service and store data on the client internet-enabled device can be packaged as a software application programming interface (API) and can be made available for use by application and software developers. If new alignment methods are developed or more reference libraries are added to the cloud database, the cloud service can permit seamless updates of the information stored in the client application, and the API can automatically detect and / or request updates when an internet connection is made or when internet connectivity exists. This allows the device to become "smarter" over its lifetime without the need for recalls or cumbersome device firmware updates.

[0056] In another embodiment, all or part of the alignment operation may be performed by a cloud service. Rather than relying on a cloud client to perform the alignment calculations, it may simply relay the raw sensor readings and unique device ID to the cloud service and allow any or all adjustments to be performed remotely in the cloud. While this requires internet connectivity to receive the adjusted scans, this embodiment may be desirable for a variety of reasons (e.g., connectivity, distributability, etc.).

[0057] In one embodiment, various functionalities of its measurement devices may be restricted (e.g., for safety purposes). In one setup, many raw sensor readings may be recorded at a single time, and then requests may be received to adjust them through many alignment methods simultaneously, with the alignment methods being executed in accordance with any future alignment methods. Allowing all calculations to be performed by a cloud service may require internet connectivity, but also adds a lot of freedom from the end user's perspective.

[0058] A cloud-based method as disclosed herein may include providing a sensor ID to (one or more) client devices, for example, via a spectral data measurement device. As described above, the client device may include an Internet-connected device, a measurement device, etc., or may be an independent device that communicates with the measurement device. The client device may, for example, request calibration data from a cloud service. Response data and / or matching data may be returned to the client device. The client device may perform adjustment operations corresponding to the response data and / or matching data.

[0059] Alternatively, the alignment can be substantially performed by the cloud service. The measurement device sends the raw sensor reading and sensor ID to the client device, which sends the raw sensor data and sensor ID together with the adjustment type to the cloud service. The cloud service can then perform operations corresponding to the raw sensor reading, sensor ID, and / or adjustment type. The aligned spectral data and / or matching data can be returned to the client device.

[0060] The optical detector is provided with a plurality of light sources having different spectral characteristics, which are controlled on / off across a range of illumination intensity combinations. The measuring sensor receives the radiated light reflected from the target surface via a restricted optical channel and generates a representative output signal. The local controller or remote computing device receives the output signal or corresponding message from the optical detector and generates a low-resolution spectral data set based on the signal for each of the illumination intensity combinations. The low-resolution spectral data set is interpolated based on a machine learning algorithm that is trained using high-resolution data from a reference device to generate a high-resolution spectral data set associated with the target surface. In an embodiment, an auxiliary sensor measures light reflected from one or more light sources, wherein the output signal from the measuring sensor is normalized to an output value from the auxiliary sensor.

[0061] Throughout the specification and claims, unless the context indicates otherwise, the following terms at least take the meanings explicitly associated herein. The meanings identified below do not necessarily limit the terms, but only provide illustrative examples of the terms. The meanings of "a", "an", and "the" may include plural references, and the meaning of "in" may include "in" and "on". The phrase "in one embodiment" as used herein does not necessarily refer to the same embodiment, although it may refer to the same embodiment.

[0062] Depending on the embodiment, certain actions, events, or functions of any algorithm described herein may be performed in a different order, may be added, combined, or omitted entirely (e.g., not all described actions or events are necessary for the practice of the algorithm). Additionally, in some embodiments, actions or events may be performed simultaneously, for example, through multithreading, interrupt handling, or on multiple processors or processor cores or other parallel architectures, rather than sequentially.

[0063] Various illustrative logic blocks, modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software or a combination of the two. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules and steps have been described above generally in terms of their functionality. Whether this functionality is implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. The described functionality can be implemented in different ways for each specific application, but this implementation decision should not be interpreted as causing deviations from the scope of the present disclosure. In various embodiments, software can be executed by a processor so that the processor performs operations. These operations can correspond to the algorithms and / or flow charts illustrated in the accompanying drawings and described herein.

[0064] The various illustrative logical blocks and modules described in conjunction with the embodiments disclosed herein may be implemented or executed by a machine such as a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof, which is designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be a controller, a microcontroller or a state machine, a combination thereof, etc. A processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0065] The steps of the methods, processes or algorithms described in conjunction with the embodiments disclosed herein may be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM or any other form of computer-readable medium known in the art. An exemplary computer-readable medium may be coupled to a processor so that the processor can read information from a memory / storage medium and write information to a memory / storage medium. In an alternative, the medium may be integrated into a processor. The processor and the medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the medium may reside in a user terminal as discrete components.

[0066] Unless specifically stated otherwise or otherwise understood within the context of use, conditional language used herein (such as "can," "might," "may," "for example," etc., among others) is generally intended to convey that some embodiments include and other embodiments do not include certain features, elements, and / or states. Thus, such conditional language is generally not intended to imply that features, elements, and / or states are in any way required for one or more embodiments, or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether such features, elements, and / or states are included or are to be performed in any particular embodiment.

[0067] The foregoing detailed description has been provided for purposes of illustration and description. Therefore, while various exemplary embodiments set forth herein have been described, it is not intended that these references be construed as limitations on the scope of the invention, except as set forth in the claims that follow.

Claims

1. A system (10) for generating high-resolution spectral data for a target surface based on low-resolution spectral data, the system comprising: The optical detector (110) further comprises: Multiple light sources (Lx) with different spectral characteristics, a sensor (213) arranged to receive light radiated by the plurality of light sources and reflected from a target surface, the sensor being further configured to generate an output signal representative of the reflected light from the target surface; a controller (205) configured to control the plurality of light sources to be turned off or on across a plurality of predetermined lighting intensity combinations; wherein the controller and / or a computing device (130) communicatively linked to receive a message corresponding to the output signal from the optical detector is further configured to: generating a plurality of corresponding normalized low-resolution spectral data sets based on the corresponding output signal from the sensor corresponding to each of the plurality of predetermined illumination intensity combinations, wherein each normalized low-resolution spectral data set includes one low-resolution spectral data set at each of a plurality of detection wavelengths within a given wavelength range, and The plurality of corresponding normalized low-resolution spectral data sets are programmatically interpolated to generate one high-resolution spectral data set associated with the target surface.

2. The system of claim 1, wherein: Also included is a data storage device comprising stored high resolution spectral data corresponding to the reference device.

3. The system of claim 2, wherein: The controller is configured to predict a set of high-resolution spectral data from the reference device and corresponding to the target surface based on the sensed normalized set of low-resolution spectral data.

4. The system of claim 2, wherein: The computing device is configured to predict a set of high-resolution spectral data from the reference device and corresponding to the target surface based on the sensed normalized set of low-resolution spectral data.

5. A system according to any one of claims 1 to 4, wherein: The plurality of light sources includes at least a first light source having a 6500K light emitting diode (LED) and a second light source having a 2700K LED.

6. The system of claim 5, wherein: The plurality of light sources include a third light source and a fourth light source having LEDs having respective spectral characteristics between 2700K and 6500K.

7. The system of any one of claims 1 to 4, wherein the optical detector comprises: a housing (201) having an outer periphery and a surface, The plurality of light sources reside within the housing and offset from the face to define an interior within the perimeter of the housing, the face being configured to engage a target surface about the perimeter of the housing, wherein ambient light is substantially restricted from entering the interior, and When the face of the optical detector engages the target surface, the sensor is arranged to receive light radiated by the plurality of light sources and reflected from the target surface.

8. The system of claim 7, wherein: The plurality of light sources and the sensor are mounted on a substrate oriented parallel to the face of the housing, and the light sources are disposed circumferentially around a sensor aperture positioned between the sensor and the face of the housing.

9. The system of claim 8, wherein: The controller resides within the housing of the optical detector.

10. The system of claim 8, wherein: The optical detector is configured to transmit the output signal to a remote computing device including the controller.

11. A system according to any one of claims 8 to 10, wherein: The sensor is a first measurement sensor configured to receive light projected from the plurality of light sources through respective restricted optical channels (210) and reflected from the target surface.

12. The system of claim 11, wherein: The restricted optical channels (210) respectively corresponding to the plurality of light channels extend at a certain angle between the plurality of light sources and the target surface, and The first measurement sensor receives the light reflected from the target surface via a central channel (209) extending at an angle of ninety degrees relative to a reference plane of the face of the housing.

13. The system of claim 12, wherein the restricted optical channels respectively corresponding to the plurality of optical channels extend at an angle of approximately 45 degrees relative to the reference plane of the housing face.

14. A system according to any one of claims 12 or 13, wherein: The defined interior includes reflectors disposed along sides of the optical channel, and The optical detector also includes an auxiliary sensor (216) configured to measure light reflected from one or more of the plurality of light sources via the reflector.

15. The system of claim 14, wherein: The controller and / or the computing device is configured to normalize the output signal from the first measurement sensor to an output value from the auxiliary sensor.

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