Exposure control for image capture

Abstract

This document describes techniques and apparatus for exposure control for capturing images. The techniques and apparatus utilize sensor data to analyze a scene and, based on the analysis, determine the likelihood of exposure-related defects in captured images of the scene. The techniques determine multiple different exposure times for multiple image capture devices based on the likelihood. An image merging module then combines the different images captured using the different exposure times to generate a single image with fewer exposure-related defects.

Description

[Background technology]

[0001] background Mobile computing devices often include image capture devices, such as cameras that use complementary metal-oxide semiconductor (CMOS) sensors, to capture images of a scene. While the quality of captured images is constantly improving, conventional image capture devices face many challenges. For example, some image capture devices are unable to capture adequate images of a scene when elements within the scene are moving. While some solutions can be used to improve image quality in a single aspect, these solutions often introduce additional image quality problems. Summary of the Invention

[0002] overview This document describes techniques and apparatus for exposure control for capturing images. The techniques and apparatus utilize sensor data to analyze a scene and, based on the analysis, determine the likelihood of exposure-related defects in the scene captured by one or more image capture devices. The techniques determine multiple different exposure times for the multiple image capture devices based on the likelihood. An image merging module then combines the different images captured using the different exposure times to generate a single image with fewer exposure-related defects.

[0003] In an aspect, a method for exposure control in a computing device is disclosed. The method includes an exposure control device that utilizes captured sensor data to determine potential exposure-related defects in a scene captured by one or more image capture devices. These exposure-related defects may include, but are not limited to, blur defects, in which portions of an image capture appear blurry, and noise defects, in which portions of an image capture may appear noisy, i.e., lacking in sharpness. Such noise defects are also referred to herein as high-noise defects.

[0004] In an aspect, the exposure controller may determine a first exposure time to reduce blur defects and a second exposure time, longer than the first exposure time, to reduce high-noise defects based on the determined likelihood of exposure-related defects. Further, the exposure controller may cause a first image capture device of the one or more image capture devices to capture a first image of the scene using the first exposure time. Also, the exposure controller may cause a second image capture device of the one or more image capture devices to capture a second image of the scene using the second exposure time.

[0005] In embodiments, the first image capture and the second image capture can be provided to an image merging module, which can receive the one or more images and utilize the received images to generate a single image from the one or more image captures of the scene.

[0006] Using the techniques and apparatus described herein, exposure control for an image capture device can be used to minimize exposure-related defects in a single image generated from multiple image captures.

[0007] This summary has been provided to introduce simplified concepts of techniques and apparatus for multi-camera exposure control that are further described below in the detailed description and drawings. This summary is not intended to identify essential features or be used to determine the scope of the claimed subject matter.

[0008] One or more aspects of exposure control for image capture are described in detail below: The use of the same reference numbers in different instances in the description and figures indicates similar elements. [Brief explanation of the drawings]

[0009] [Figure 1]FIG. 1 illustrates an exemplary implementation of a computing device that implements exposure control for an image capture device. [Figure 2] FIG. 2 illustrates aspects of an image merging module for the exemplary embodiment of FIG. 1. [Figure 3] FIG. 1 illustrates an exemplary operating environment in which exposure control for an image capture device may be implemented. [Figure 4] FIG. 1 illustrates several examples of sensors that can be used to collect sensor data. [Figure 5] FIG. 2 illustrates an exemplary implementation of a motion-scene aspect of exposure control for an image capture device. [Figure 6] 6 illustrates aspects of the image merging module of the motion scene embodiment of FIG. 5. [Figure 7] FIG. 2 illustrates an exemplary implementation of an anti-banding aspect of exposure control for an image capture device. [Figure 8] 8 illustrates aspects of an image merging module for the anti-banding implementation of FIG. 7. [Figure 9] FIG. 1 illustrates an exemplary method of exposure control for an image capture device. DETAILED DESCRIPTION OF THE INVENTION

[0010] Although the features and concepts of the described techniques and apparatus for exposure control for capturing images can be implemented in any number of different environments, aspects are described in the context of the following examples.

[0011] Detailed Description Overview This document describes techniques and apparatus for exposure control for capturing images. The exposure control described herein utilizes captured sensor data to determine potential exposure-related defects, which enables an exposure controller to determine one or more exposure times to use for capturing an image.

[0012] For example, the exposure controller can utilize the captured sensor data to determine the likelihood of exposure-related defects, including blur and high-noise defects, in a scene captured by one or more image capture devices. Based on the determined likelihood of exposure-related defects, the exposure controller can determine a first exposure time to reduce blur defects and a longer second exposure time to reduce high-noise defects. Using the determined first and second exposure times, the exposure controller causes the first and second image capture devices to capture a first image of the scene using the first exposure time and a second image of the scene using the second exposure time. The exposure controller can then provide one or more image captures to an image merging module, which can use the one or more image captures to generate a single image of the scene. In this manner, the exposure controller reduces exposure-related defects.

[0013] Although the described features and concepts of techniques and apparatus for exposure control for image capture devices can be implemented in any number of different environments, aspects are described in the context of the following examples.

[0014] Exemplary Devices 1 illustrates an exemplary implementation 100 of a computing device 102 that implements exposure control for image capture devices in accordance with the techniques described herein. The illustrated computing device 102 may include one or more sensors 104, a first image capture device 106, and a second image capture device 108. As shown, the computing device 102 is used to capture a captured scene 110. The captured scene 110 may be captured by one or more image capture devices (e.g., the first image capture device 106 and the second image capture device 108), which may capture one or more images (e.g., a first image 112 and a second image 114). The first image 112 or the second image 114 may include exposure-related defects, including a blur defect 116 and a high-noise defect 118.

[0015] The computing device 102 includes or is coupled to one or more sensors 104 for capturing sensor data that can be used to determine potential exposure-related defects in the captured scene 110. Exemplary exposure-related defects include blur defects 116 and high-noise defects 118, although other defects, such as banding defects mentioned below, may be present as well.

[0016] Although not required, the present technology can determine the likelihood of exposure-related defects using machine learning based on previous image captures. For example, the use of machine learning can include supervised or unsupervised learning through the use of neural networks, including perceptrons, feedforward neural networks, convolutional neural networks, radial basis function neural networks, or recurrent neural networks. For example, the likelihood of exposure-related defects can be determined through supervised machine learning. In supervised machine learning, a labeled set of previous image captures that identifies features associated with the images can be provided to build a machine-learning model, such as labeled non-imaging data (e.g., accelerometer data, flicker sensor data) and imaging data, based on those exposure-related defects (e.g., blur defects, high-noise defects, or banding defects). Through this supervised machine learning, future image captures can be classified by their exposure-related defects based on the associated features. Additionally, future image captures can be fed back into the dataset to further train the machine-learning model.

[0017] Alternatively, or in addition to machine learning, the technique can determine the likelihood of exposure-related defects via a weighted equation or via a decision tree based on the captured sensor data.

[0018] In the exemplary embodiment 100, two image capture devices (e.g., first image capture device 106 and second image capture device 108) capture images of the captured scene (e.g., first image 112 and second image 114) using a first exposure time and a second, longer exposure time, respectively. However, one or more additional image capture devices may be used to capture one or more additional image captures of the captured scene 110.

[0019] The sensor gain of the image capture device can be adjusted to capture each image at the same or approximately similar brightness. The brightness of an image capture is defined as the gain value times the exposure time. In one example, a second image capture device 108 using a longer second exposure time will capture the second image 114 with a lower gain value than for capturing the first image 112 and will capture the second image 114 with the same brightness value.

[0020] One or more image capture devices may also be used to capture one or more multi-frame image captures, which may be captured sequentially so that an image playback device can generate video from the multi-frame images.

[0021] Image capture devices 106 and 108 may be various types of image capture devices, such as wide-angle image capture devices, telephoto image capture devices, infrared image capture devices, and so on.

[0022] Figure 2 illustrates an exemplary implementation 200 of an image merging module 202 for use in computing device 102 of Figure 1. As shown, image merging module 202 incorporates both or portions of first image 112 and second image 114 to generate a single image 204 of a captured scene (e.g., captured scene 110). Single image 204 may be digitally displayed on a display 206 of computing device 102, provided to another device, and / or stored.

[0023] As mentioned, the image merging module 202 uses the first image 112 for portions of the captured scene (e.g., the captured scene 110) determined to have potential blur defects 116 and the second image 114 for portions of the captured scene (e.g., the captured scene 110) determined to have high noise defects 118. In doing so, the image merging module 202 generates a single image 204 from these image captures that has fewer exposure-related defects. FIG. 2 also illustrates an example in which the single image 204 may be digitally displayed on the display 206 of the computing device 102. Additional image captures of the scene (e.g., the captured scene 110) may be provided to the image merging module 202. The image merging module may then use these additional images in combination with the first image 112 and the second image 114 to generate the single image 204 of the scene (e.g., the captured scene 110). In another aspect, the image merging module 202 may be provided with multiple multi-frame images. An image merging module 202 can then be used to generate a single multi-frame image from the multiple multi-frame images.

[0024] 3 illustrates an exemplary operating environment 300 in which exposure control for an image capture device may be implemented. While this document discloses particular aspects of exposure control for an image capture device implemented on a mobile device (e.g., a smartphone), it should be noted that exposure control for an image capture device may be implemented using any computing device, including, but not limited to, a mobile computing device 102-1, a tablet 102-2, a laptop or personal computer 102-3, imaging eyewear 102-4, a vehicle 102-5, etc.

[0025] 3 includes one or more processors 302, a computer-readable medium 304, one or more sensors 316 capable of capturing sensor data, a user interface 318, one or more image capture devices 320, and a display 322. The computer-readable medium 304 may include an exposure controller 306 as described herein. The exposure controller 306 may include a memory 308, which may incorporate a machine learning component 310 and may store control instructions 312 that, when executed by the processor 302, cause the processor 302 to implement the method of exposure control for an image capture device as described herein. Additionally, the computer-readable medium 304 may include the image merging module 202 and an application 314, such as an image capture application or an image display application, which may operate in cooperation with the method of exposure control for an image capture device as described herein.

[0026] 4 illustrates several examples of sensors 316 that can be used to collect sensor data. For example, a computing device (e.g., computing device 102) can include an imaging sensor 410 or a non-imaging sensor 402. The imaging sensor 410 can include an adjustable gain value and can include a complementary metal-oxide semiconductor (CMOS) sensor 412, etc. Similarly, the non-imaging sensor 402 can also include an adjustable gain value and can include an accelerometer 404, a flicker sensor 406, a radar system 408 that can measure movement in a captured scene, or any other sensor that can provide sensor data for determining potential exposure-related defects.

[0027] 5 illustrates an example implementation 500 of a motion scene aspect of exposure control for an image capture device. As shown, a computing device 102 may utilize a sensor 104, a first image capture device 106, and a second image capture device 108 to capture a first image 504 and a second image 506 of a captured scene 502. The captured scene 502 may include a portion identified as a background 508 and a portion identified as a subject of focus 510. Furthermore, a motion scene 518 may be generated by relative motion 512 of the computing device 102 with respect to the portions of the captured scene 502.

[0028] In this embodiment, the image capture device may move relative to the portion of the scene 502 being captured. In Figure 5, the relative movement 512 is indicated by an arrow. The sensor 104 collects sensor data describing the scene 502 being captured, and the exposure controller 306 then uses this sensor data to determine potential exposure-related defects. However, the sensor data may also be used to identify a focus object 510 within the scene 502 being captured. Additionally, the computing device 102 may use the sensor data to identify the remainder of the scene 502 being captured as a background portion 508.

[0029] In an exemplary implementation 500 of exposure control for image capture devices, a computing device 102 utilizes two image capture devices (e.g., a first image capture device 106 and a second image capture device 108). The first image capture device 106 captures a first image 504 of a captured scene 502 using a first exposure time determined based on a determined likelihood of exposure-related defects. The first exposure time is determined to reduce blur defects 514 in the captured scene 502, e.g., a fast exposure. The speed of the exposure can be related to the magnitude of the determined blur defects 514, e.g., a faster exposure for larger blur (e.g., faster motion requires faster exposure).

[0030] Similarly, the second image capture device 108 captures a second image 506 of the captured scene 502 using a longer second exposure time determined based on the likelihood of exposure-related defects. The second exposure time is determined to reduce noise defects 516 in the captured scene 502. Additionally, the second image may include a motion scene 518 in a background portion 508 of the captured scene 502, which is a blurred image capture indicative of movement within the captured scene 502. In this case, the inclusion of the motion scene 518 creates a realistic indication of movement within the captured scene 502.

[0031] Figure 6 illustrates an example embodiment 600 of the image merging module 202 for the motion scene implementation 500 of Figure 5. As shown, the image merging module 202 receives and incorporates a first image 504 of an object of interest 510 to reduce blur imperfections 514 and a second image 506 of a background portion 508 to reduce noise imperfections 516 and generates a motion scene 518 in a single image 602 of a captured scene (e.g., captured scene 502). The single image 602 of a captured scene (e.g., captured scene 502) may be digitally displayed, provided, etc. (e.g., displayed on the display 206 of the computing device 102).

[0032] More specifically, the image merging module 202 generates a single image 602 of a captured scene (e.g., the captured scene 502) by incorporating a first image 504 of a focus object 510 and a second image 506 of a remaining background portion 508 of the captured scene (e.g., the captured scene 502). As shown, the single image 602 has reduced noise defects 516 and blur defects 514, while still exhibiting scene motion in the motion scene 518.

[0033] 7 illustrates an example implementation 700 of an anti-banding aspect of exposure control for an image capture device. As shown, a computing device 102 can utilize a sensor 104 to determine potential exposure-related defects in a captured scene 702. The potential exposure-related defects in the captured scene 702 can include banding defects 710. Banding defects can be caused by the frequency at which the light illuminating the scene operates. The computing device 102 can utilize a first image capture device 106 and a second image capture device 108 to capture a first image 704 and a second image 706 of the captured scene 702. Additionally, blur defects can be present in one or more images due to long exposure times.

[0034] The computing device 102, via the sensor 104, captures sensor data describing the captured scene 702. In this example, a flicker sensor may be particularly useful for detecting the presence of light that flickers at a predetermined frequency. The sensor data may be used to determine possible exposure-related defects in the captured scene 702. The exposure-related defects may include banding defects 710, which may be dark bands in the image due to the flickering of light in the captured scene 702 due to the frequency at which the light, such as fluorescent lighting, operates.

[0035] As mentioned above, the exposure controller 306 determines the first exposure time and the longer second exposure time based on the likelihood of exposure-related defects, such as the banding of Figure 7. The exposure controller 306 then causes the first image capture device 106 and the second image capture device 108 to capture the first image 704 and the second image 706 using the respective exposure times.

[0036] The first exposure time may be a short exposure time determined to reduce blur defects 708 in the portion of the scene 702 being captured. An example of a blur defect 708 may be a portion of the scene that appears less sharp due to lighting if captured using a longer exposure time. The second exposure time may be a longer exposure time of at least 8.33 milliseconds (ms) determined to reduce banding defects 710 in the portion of the scene 702 being captured. An exposure time of 8.33 ms or greater has been determined to be sufficient to capture an image free of banding defects based on the standard operating frequency of most light. In doing so, the exposure controller 306, in conjunction with the image merging module 202, provides an image free of bands.

[0037] Figure 8 illustrates an embodiment 800 of the image merging module 202 for the anti-banding implementation 700 of Figure 7. As shown, the image merging module 202 receives and incorporates a first image 704 to reduce blur defects 708 and also receives and incorporates a second image 706 to reduce banding defects 710 in a single image 802 of a captured scene (e.g., captured scene 702).

[0038] In an aspect, the first image 704 and the second image 706 are provided to the image merging module 202. The image merging module 202 generates a single image 802 of the captured scene (e.g., the captured scene 702) by incorporating the first image 504 to reduce blur defects 708 and the second image 706 to reduce banding defects 710.

[0039] Exemplary Methods 9 illustrates an exemplary method 900 of exposure control for image capture devices. A computing device uses an exposure controller to determine 902 potential exposure-related defects in a scene captured by multiple image capture devices. In this example, the exposure-related defects may include blur defects and high-noise defects, although other defects such as banding defects may also be reduced or corrected by the technique.

[0040] At 904, the exposure controller may determine a first exposure time to reduce blurring effects and a longer second exposure time to reduce high noise defects based on the determined likelihood of exposure-related defects.

[0041] In one aspect, the determination of either the likelihood of an exposure-related defect or the exposure time can be performed via machine learning, hi another aspect, the preceding steps can be performed via a decision tree or any other computational method.

[0042] At 906, determining the first and second exposure times can cause the first and second image capture devices to capture first and second images of the scene using the first and second exposure times, respectively.

[0043] At 908, the first image and the second image are provided to an image merging module, which can use the first image and the second image to generate a single image. Optionally, additional image capture devices can be used to capture additional images using additional exposure times. In this example, all additional image captures can be provided to the image merging module and used to generate a single image.

[0044] In another example, determining the likelihood of exposure-related defects can determine the likelihood of banding defects in an image. Thus, the second image may be a band-free image captured using a second image capture device with a second exposure time of at least 8.33 ms. This exposure time meets the minimum requirement for eliminating banding defects caused by the frequencies at which most light operates.

[0045] In another example, the object of focus can be determined by determining the likelihood of exposure-related defects. In this example, a second image can be used to generate a motion scene in the background portion of the scene.

[0046] In general, all components, modules, methods, and operations described herein may be implemented using software, firmware, hardware (e.g., fixed logic circuitry), manual processing, or any combination thereof. Some operations of exemplary methods may be described in the general context of executable instructions stored on computer-readable storage memory that is local and / or remote to a computer processing system, and implementations may include software applications, programs, functions, etc. Alternatively or additionally, all of the functionality described herein may be implemented, at least in part, by one or more hardware logic components, including, but not limited to, field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), etc.

[0047] Some examples will be described below.

[0009] Example 1. A method comprises determining a likelihood of exposure-related defects in a scene captured by a plurality of image capture devices based on captured sensor data. The exposure-related defects include blur defects and high-noise defects. The method comprises determining a first exposure time to reduce blur defects and a second exposure time longer than the first exposure time to reduce high-noise defects based on the determined likelihood; causing a first image capture device of the plurality of image capture devices to capture a first image of the scene using the first exposure time and a second image capture device of the plurality of image capture devices to capture a second image of the scene using the second exposure time; and providing the first and second image captures to an image merging module to generate a single image from the first and second image captures.

[0048] EXAMPLE 2 The method of Example 1, further comprising capturing one or more additional image captures of the scene using an additional image capture device. Providing the first image capture and the second image capture provides the additional image captures to the image merging module.

[0049] Example 3 The method described in Example 1, wherein determining the likelihood of an exposure-related defect is determined, at least in part, via machine learning based on prior image captures.

[0050] EXAMPLE 4 The method described in Example 1, wherein determining the first exposure time or the second exposure time is determined at least in part via machine learning based on prior image captures captured using different exposure times.

[0051] EXAMPLE 5 The method described in Example 1, wherein determining the likelihood of an exposure-related defect is determined by a decision tree used to determine the likelihood of an exposure-related defect based on the captured sensor data.

[0052] EXAMPLE 6 The method described in Example 1, wherein determining the first exposure time or the second exposure time is determined by a decision tree that can be used to determine the first exposure time or the second exposure time based on the likelihood of an exposure-related defect.

[0053] Example 7 The method described in Example 1, wherein the first image capture and the second image capture are captured at the same brightness, where brightness is defined as the product of the sensor gain and the exposure time.

[0054] Example 8 The method described in Example 1, wherein the sensor data includes non-imaging data collected from an accelerometer.

[0055] EXAMPLE 9 The method of Example 1, wherein the sensor data includes radar data collected from a radar system, the radar data being usable to measure motion in a captured scene.

[0056] Example 10 The method described in Example 1, wherein the sensor data includes non-imaging data collected from a flicker sensor that can be used to measure banding defects in a captured scene.

[0057] EXAMPLE 11 The method of Example 10, wherein causing a second image capture device to capture a second image with a second exposure time, the second exposure time being longer than a time associated with a frequency of flicker of light in the captured scene, the frequency being collected by the flicker sensor.

[0058] Example 12 The method described in Example 11, wherein the second exposure time is 8.33 milliseconds or longer. The second image is a band-free image.

[0059] Example 13 The method described in Example 1, wherein the sensor data is imaging data collected by one or more of the plurality of image capture devices.

[0060] EXAMPLE 14 The method of Example 13, further including determining a focus object based on the sensor data and using an image merging module to generate a single image of the scene by incorporating a first image capture of the focus object and a second image capture of a remaining background portion of the scene.

[0061] EXAMPLE 15 The method of example 1 or example 14, wherein a second image capture is incorporated to generate a motion scene in the background portion. The motion scene in the background portion is a blurred image capture showing motion within the scene.

[0062] EXAMPLE 16: The method described in Example 1, wherein the first image capture and the second image capture are multi-frame image captures, and the single image generated by the image merging module is a multi-frame image, the multi-frame image comprising a plurality of successively captured single-frame image captures.

[0063] Example 17. The method of any preceding example, further comprising digitally displaying the single image generated from the image merging module.

[0064] Example 18 A computing device includes one or more processors, one or more image capture devices, one or more sensors capable of capturing captured sensor data, and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to implement the methods described herein.

[0065] conclusion Although aspects of exposure control for image capture have been described above in language specific to features and / or methods, the subject matter of the appended claims is not necessarily limited to the particular features or methods described. Rather, the specific features and methods are disclosed as exemplary implementations of the claimed exposure control for an image capture device, and other equivalent features and methods are intended to be within the scope of the appended claims. Furthermore, it should be recognized that various aspects have been described, and that each described aspect can be implemented independently or in conjunction with one or more of the other described aspects.

Claims

1. 1. A method comprising: determining likelihood of exposure-related defects in a scene captured by the plurality of image capture devices based on the captured sensor data; the exposure-related defects include blur defects and high-noise defects; The method comprises: determining a first exposure time for reducing the blur defect and a second exposure time, the second exposure time being longer than the first exposure time, for reducing the high noise defect based on the determined likelihood; determining a first sensor gain and a second sensor gain based on the first exposure time and the second exposure time; the first sensor gain is greater than the second sensor gain; The method comprises: causing a first image capture device of the plurality of image capture devices to capture a first image of the scene using the first exposure time and the first sensor gain, and causing a second image capture device of the plurality of image capture devices to capture a second image of the scene using the second exposure time and the second sensor gain; providing the first image capture and the second image capture to an image merging module to generate a single image from the first image capture and the second image capture; A method further comprising:

2. 2. The method of claim 1, wherein one or more additional image captures of the scene are captured using one or more additional image capture devices, and providing the first image capture and the second image capture provides the additional image captures to the image merging module.

3. The method of claim 1 , wherein determining the likelihood of the exposure-related defect is determined, at least in part, via machine learning based on prior image captures.

4. 10. The method of claim 1, wherein determining the first exposure time or the second exposure time is determined at least in part via machine learning based on prior image captures captured using different exposure times.

5. 2. The method of claim 1, wherein the first image capture and the second image capture are captured at the same brightness, the brightness being defined as the product of the first sensor gain and the first exposure time, and equal to the product of the second sensor gain and the second exposure time.

6. The method of claim 1 , wherein the sensor data comprises non-imaging data collected from a radar system operable to measure motion in the captured scene.

7. The method of claim 1 , wherein the sensor data comprises non-imaging data collected from a flicker sensor that can be used to measure banding defects in the captured scene.

8. 8. The method of claim 7, wherein causing the second image capture device to capture the second image at the second exposure time results in the second exposure time being longer than a time associated with a frequency of flicker of light in the scene being captured, the frequency being collected by the flicker sensor.

9. 9. The method of claim 8, wherein the second exposure time is 8.33 milliseconds or longer, and the second image is a band-free image.

10. The method of claim 1 , wherein the sensor data is imaging data collected by the image capture device.

11. determining a focus object based on the sensor data; 2. The method of claim 1, further comprising using the image merging module to generate the single image of the scene by incorporating the first image capture of the object of focus and the second image capture of a remaining background portion of the scene.

12. 12. The method of claim 11, wherein the second image capture is incorporated to create a motion scene in the background portion, the motion scene in the background portion being a blurred image capture showing motion within the scene.

13. 12. The method of claim 1 or claim 11, wherein the first image capture and the second image capture are multi-frame image captures, and the single image generated by the image merging module is a multi-frame image, the multi-frame image comprising a plurality of single-frame image captures captured consecutively.

14. The method of any one of claims 1 to 13, further comprising displaying the single image generated from the image merging module.

15. one or more processors; one or more image capture devices; one or more sensors capable of capturing captured sensor data; a memory storing instructions that, when executed by said one or more processors, cause said one or more processors to implement the method of any one of claims 1 to 14; 1. A computing device comprising:

16. A program for causing one or more processors to execute the method according to any one of claims 1 to 14.

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