ELECTRONIC DEVICE CAPABLE OF CORRECTING DEPTH INFORMATION AND PERFORMING BOKEH PROCESSING ON IMAGE AND METHOD OF CONTROLLING ELECTRONIC DEVICE

An electronic device and a method for controlling an electronic device are provided, the electronic device includes a first camera used to acquire a first image, a second camera used to acquire a second image, a range sensor used to acquire ToF depth information, and an image signal processor used to acquire an image with bokeh based on the first image, the second image, and the ToF depth information. The image with bokeh is the first image with one or more bokeh portions. The image signal processor is used to acquire a depth information of a stereo image by matching the first image and the second image, correct the depth information of the stereo image based on the first image and the ToF depth information; and acquire the corrected depth information.

TECHNICAL FIELD

The present disclosure relates to an electronic device and a method of controlling an electronic device.

BACKGROUND

A digital single lens reflex (DSLR) camera etc., has been used for generating an image with bokeh. In the image with bokeh, a portion that requires attention is made clearer and a foreground and a background of the portion is blurred. Namely, a portion that needs to get attention is made clearer and a foreground and a background of the portion is blurred.

In recent years, a technique of image processing in which an image with bokeh is generated artificially from an image taken with a camera having a deep depth of field, is widely spread. The camera having a deep depth of field is installed in an electronic device such as a smartphone and captures an image that is in focus from a short-distance portion to a long-distance portion.

A method of using depth information which is included in a stereo image taken through binocular stereo viewing is a technique employed to artificially produce an image with bokeh. By using the technique, an electronic device such as a smartphone can generate an image with bokeh.

However, the depth information may be false in some types of a surface of an object in a subject. For example, if a binocular stereo image has a low texture pattern that does not have a clear change along an epipolar line, or a repeated pattern such as checkered pattern, depth information cannot be accurately calculated. As a result, an image with inappropriate bokeh is generated.FIG. 21shows an example of an image with bokeh in the prior art. Although a region B is actually an in-focus region, incorrect image processing is performed on the region B since depth information in the region B is false due to its repeated pattern.

Algorithms used in an image processing system available for electronic devices such as smartphones cannot be directly improved because the system is usually a black box for manufacturers of the electronic devices. Therefore, it is difficult to correct the depth information based on a stereo image.

SUMMARY

In accordance with the present disclosure, an electronic device may include:

a first camera configured to acquire a first image of a subject;

a second camera configured to acquire a second image of the subject;

a range sensor configured to emit a pulsed light toward the subject and detect a reflected light of the pulsed light reflected from the subject, and acquire time of flight (ToF) depth information; and

an image signal processor configured to acquire an image with bokeh, based on the first image, the second image, and the ToF depth information, the image with bokeh being the first image with one or more bokeh portions,

the image signal processor is configured to acquire a depth information of a stereo image by matching the first image and the second image; correct the depth information of the stereo image based on the first image and the ToF depth information, and acquire the corrected depth information.

In accordance with the present disclosure, a method of controlling an electronic device, the electronic device including a first camera, a second camera, a range sensor, and an image signal processor, and the method including: acquiring, by the first camera, a first image of a subject; acquiring, by the second camera, a second image of the subject; acquiring, by the range sensor, ToF depth information of the subject; acquiring, by the image signal processor, a depth information of a stereo image by matching the first image and the second image; correcting, based on the first image and the ToF depth information, the depth information of the stereo image, and acquiring the corrected depth information; and acquiring, by the image signal processor, based on the corrected depth information, an image with bokeh; the image with bokeh being the first image with one or more bokeh portions.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present disclosure will be described in detail and examples of the embodiments will be illustrated in the accompanying drawings. The same or similar elements and the elements having same or similar functions are denoted by like reference numerals throughout the descriptions. The embodiments described herein with reference to the drawings are explanatory, which aim to illustrate the present disclosure, but shall not be construed to limit the present disclosure.

FIG. 1is a circuit diagram illustrating an example of a configuration of an electronic device100according to an embodiment of the present disclosure. Reference numerals101aand101bdepict subjects (target objects). The subject101ais relatively close and the subject101bis relatively far away.

As shown inFIG. 1, the electronic device100includes a stereo camera module10, a range sensor module (also referred to as range senor)20, and an image signal processor30that controls the stereo camera module10and the range sensor module20. The image signal processor30processes camera image data acquired from the stereo camera module10.

The stereo camera module10includes a master camera module (also referred to as first camera)11and a slave camera module (also referred to as second camera)12for the use for binocular stereo viewing as shown inFIG. 1.

The master camera module11includes a first lens11athat is capable of focusing on a subject, a first image sensor11bthat detects an image inputted via the first lens11a, and a first image sensor driver11cthat drives the first image sensor11b, as shown inFIG. 1.

The slave camera module12includes a second lens12athat is capable of focusing on a subject, a second image sensor12bthat detects an image inputted via the second lens12a, and a second image sensor driver12cthat drives the second image sensor12b, as shown inFIG. 1.

The master camera module11acquires a master camera image (also referred to as first image) of the subjects101aand101b. Similarly, the slave camera module12acquires a slave camera image (also referred to as second image) of the subjects101aand101b.

The range sensor module20acquires time of flight (ToF) depth information (ToF depth value) by emitting pulsed light toward the subjects101aand101b, and detecting reflection light from the subjects101aand101b. The ToF depth information indicates an actual distance between the electronic device100and a subject.

The resolution of the ToF depth information detected by the range sensor module20is lower than the resolution of stereo depth information of a stereo image that is acquired based on the master camera image and the slave camera image. A warp processing (ToF depth expansion processing) Y2is performed to expand the ToF information to a field of view (FOV) of the master camera image.

The image signal processor30controls the master camera module11, the slave camera module12, and the range sensor module20to acquire a camera image. The camera image is the master camera image with bokeh. The camera image is acquired based on the master camera image obtained by means of the master camera module11, the slave camera image obtained by means of the slave camera module12, and the ToF depth information obtained by means of the range sensor module20.

Furthermore, as shown inFIG. 1, the electronic device100includes a global navigation satellite system (GNSS) module40, a wireless communication module41, a CODEC42, a speaker43, a microphone44, a display module (also referred to as display, e.g., display screen)45, an input module (also referred to as input apparatus, e.g., touch screen)46, an inertial measurement unit (IMU)47, a main processor48, and a memory49.

The GNSS module40measures the current position of the electronic device100. The wireless communication module41performs wireless communications with the Internet. The CODEC42bi-directionally performs encoding and decoding, using a predetermined encoding/decoding method, as shown inFIG. 1. The speaker43outputs a sound in accordance with sound data decoded by the CODEC42. The microphone44outputs sound data to the CODEC42based on inputted sound.

An IMU47detects the angular velocity and the acceleration of the electronic device100.

The main processor48controls the global navigation satellite system (GNSS) module40, the wireless communication module41, the CODEC42, the speaker43, the microphone44, the display module45, the input module46, and the IMU47.

The memory49stores a program and data required for the image signal processor30, acquired image data, and a program and data required for the main processor48.

The electronic device100having the above-described configuration is a mobile apparatus such as a smartphone in this embodiment, but may be other types of electronic devices including a plurality of camera modules.

<Flow of Data in the Electronic Device100>

FIG. 2is a diagram illustrating an example of a flow of data for generating the camera image of the electronic device100.

As shown inFIG. 2, the image signal processor30controls the master camera module11, the slave camera module12, and the range sensor module20to acquire the camera image based on the master camera image201acquired by the master camera module11, the slave camera image202acquired by the slave camera module12, and the ToF depth information203acquired by the range sensor module20.

The image signal processor30acquires stereo depth information204by matching processing (stereo processing) X1of the master camera image201and the slave camera image202as shown inFIG. 2.

The image signal processor30also extracts person region information (in a broader sense, subject region information)205that defines the region of the subject in the master camera image201by performing AI processing (image processing) X2on the region of the subject.

The image signal processor30further acquires depth information206of the master camera image201by performing combining processing X3on the stereo depth information204and the extracted person region information (subject region information)205.

The image signal processor30also performs an uncertain region detection processing Y1to detect an uncertain region in the master camera image201. The uncertain region is a region on which the matching processing X1cannot be performed based on the master camera image201and the slave camera image202. The uncertain region is a region where parallax cannot be calculated by means of the stereo depth information of a stereo image. Specifically, the uncertain region is a low texture region or a repeated pattern region.

The image signal processor30acquires uncertain region information207relating to the detected uncertain region in the master camera image201.

The image signal processor30also performs a warp processing Y2to match a FOV (field of view) of the ToF depth information with that of the master camera image. The warp processing Y2is performed based on the ToF depth information and camera parameter211. The camera parameter211includes camera parameters of the master camera module11and the range sensor module20.

The image signal processor30also performs a depth correction Y3to correct the depth information206of the uncertain region. The depth correction Y3is performed based on the ToF depth information203, the depth information206, the uncertain region information207and the in-focus position210. Corrected depth information208is acquired by the depth correction Y3. The depth correction Y3will be described in detail later with reference toFIG. 3.

With respect to a non-detection region, for which no ToF depth information is detected (for example, a foreground and a background of the subject), the image signal processor30may acquire the corrected depth information208in the depth correction Y3by replacing depth information of the non-detection region with depth information for correction that meets a user's instruction.

As described above, the image signal processor30acquires the corrected depth information208by correcting the depth information206based on the master camera image201and the ToF depth information203. The depth information206is acquired based on the stereo image which is acquired by the matching processing X1.

The image signal processor30then performs a bokeh processing X4on the master camera image201based on the corrected depth information208such that a camera image with bokeh (also referred to as image with bokeh)209is obtained.

The bokeh processing X4may be performed in consideration of the in-focus position210. For example, the camera image with bokeh209is generated by emphasizing more bokeh as the difference between the corrected depth information208and an average value of the depth information around the in-focus position210increases.

<Method of Controlling the Electronic Device100>

An example of a method of controlling the electronic device100described above will now be described. In particular, an example of a flow of the electronic device100for acquiring a camera image with appropriate bokeh will be described below.

FIG. 3is a diagram illustrating an example of a flow for generating a camera image with bokeh in the electronic device100.FIG. 4Ais a diagram illustrating an example of the master camera image taken by the electronic device100.FIG. 4Bis a diagram illustrating an example of the ToF depth information corresponding to the master camera image shown inFIG. 4A.FIG. 5Ais a diagram illustrating an example of the depth information corresponding to the master camera image shown inFIG. 4A.FIG. 5Bis a diagram illustrating an example of a camera image with bokeh acquired by performing the bokeh processing X4on the master camera image based on non-corrected depth information.

The image signal processor30acquires a correct region defined by the in-focus position210. For example, the correct region is a circular or rectangular region centered on the in-focus position.FIG. 6is a diagram illustrating an example of a flow for acquiring the correct region.

The correct region is an in-focus region which has neither a checkered pattern (i.e., low texture region) nor a plurality of horizontally long plates (i.e., repeated pattern region).

According to the example flow shown inFIG. 6, the display module45displays a master camera image taken by the master camera module11(block S61). As shown inFIG. 7, the master camera image includes the close subject101aand the far subject101b. In this example, the subject101ahas a plate-like member and a checkered pattern drawn on the front surface of the plate-like member. The subject101bis a plurality of horizontally long plates which are arranged in the vertical direction.

A user specifies a correct region (block S62). For example, the user specifies a correct region by tapping a touch panel of the display module45. In this example, as shown inFIG. 7, the correct region Rc is specified on a part of the front surface that is not the checkered pattern. Please note that the electronic device100(e.g., the image signal processor30or the main processor48) may specify a correct region.

The image signal processor30acquires the master camera image (FIG. 4A), the depth information (FIG. 5A) and the ToF depth information (FIG. 4B) by controlling the master camera module11, the slave camera module12, and the range sensor module20(block S32).

FIG. 4Ashows a diagram illustrating an example of the master camera image. The master camera image has the close subject101aand the far subject101b. InFIG. 4A, a region R1indicates a region of the subject101b, a region R2indicates a region of the checkered pattern, a region R3indicates a region obtained by removing the checkered pattern from the subject101a. A background region Rb indicates a background region.

FIG. 5Ashows an example of the depth information corresponding to the master camera image shown inFIG. 4A. A region R1fis a part of the region R1. Depth information in the region R1fis not correct due to low texture. Similarly, a region R2fis a part of the region R2. Depth information in the region R2fis not correct due to repeated pattern.

FIG. 4Bshows that, in a detection region where the ToF depth information is detected, a brighter portion indicates that an object is closer, and shows that a non-detection region where no ToF depth information is detected is shaded. The background region Rb is the non-detection region.

The image signal processor30performs the uncertain region detection processing for detecting an uncertain region in which the stereo processing cannot be performed on the master camera image and the slave camera image (block S33). The block S33will be described in detail later with reference toFIG. 8.

The image signal processor30acquires a corrected depth information by correcting the depth information corresponding to the uncertain region (block S34). Examples of depth information corresponding to the uncertain region are those having no depth value, or having no value but interpolated using depth values of surrounding portions. The block S34will be described in detail later with reference toFIG. 14.

The image signal processor30performs the bokeh processing X4on the master camera image to acquire the camera image with bokeh209(block S35). As shown inFIG. 5B, a camera image with bokeh that is obtained by the bokeh processing X4has bokeh in a region R2fthat should not have bokeh. This is because a depth information in the region R2fwhich is a part of the region R2is not correct due to a repeated pattern as shown inFIG. 5A.

<Detail of the Uncertain Region Detection Processing>

An example of a flow of the uncertain region detection processing (i.e., the block S33) will be described with reference toFIGS. 8 to 11.

FIG. 8is a diagram illustrating an example of a flow of the uncertain region detection processing.FIG. 9is a diagram illustrating an autocorrelation calculation model in the flow of the uncertain region detection processing.FIG. 10Ais a diagram illustrating an example of a relationship between the movement of a reference region relative to a region of interest in an uncertain region that is labeled as a low texture region and the similarity obtained by means of the autocorrelation.FIG. 10Bis a diagram illustrating an example of a relationship between the movement of a reference region relative to a region of interest in an uncertain region that is labeled as a repeated pattern region and the similarity obtained by means of the autocorrelation calculation.FIG. 11is a diagram illustrating an example of an uncertain region labeled as the low texture region and an example of an uncertain region labeled as the repeated pattern region.

The image signal processor30performs an autocorrelation calculation with a reference region of the master camera image being moved by a predefined movement amount relative to a region of interest, thereby calculating a degree of similarity (characteristic value) between the region of interest and the reference region (block S81).

Specifically, as shown inFIG. 9, the image signal processor30performs an autocorrelation calculation on the master camera image with a reference region Rf being moved relative to a region of interest Ri by a predefined movement amount in an epipolar line direction, thereby calculating a degree of similarity (characteristic value) between the region of interest Ri and the reference region Rf. The epipolar line is that for a parallel stereo image.

In computing the similarity in the autocorrelation calculation in the uncertain region detection processing, a sum of squared difference (SSD) method, a sum of absolute difference (SAD) method, a normalized cross correlation (NCC) method, a zero means normalized cross correlation (ZNCC) method, or a summed normalized cross correlation (SNCC) method may be used.

The image signal processor30detects a region in which a change in the calculated degree of similarity with respect to a predefined movement amount is smaller than a predefined value (block S82), and labels the detected region as a low texture region.

For example, if, in a region, a frequency (characteristic value) that is based on an average value of the peak intervals or a mode value of peaks of the similarity with respect to a predefined movement amount is less (smaller) than a predefined label reference value, the image signal processor30labels the region as a low texture region (seeFIGS. 10A and 11).

The image signal processor30may further classify low texture regions based on the magnitude of the change in (change in the depth of low point of) similarity.

The image signal processor30detects a region in which there are a plurality of peaks in the calculated degree of similarity with respect to a predefined movement amount (block S83), and labels the detected region as a repeated pattern region.

For example, if, in a region, a frequency (characteristic value) that is based on an average value of the peak intervals or a mode value of peaks of the similarity with respect to a predefined movement amount is equal to or more (greater) than a predefined label reference value, the image signal processor30labels the region as a repeated pattern region (seeFIGS. 10B and 11).

The image signal processor30combines the labeled repeated pattern region and the labeled low texture region (block S84).

FIG. 12shows a diagram illustrating an example of a flow of combining operations in the block S84.FIG. 13Ais a diagram illustrating an example of a relationship between frequency (characteristic value) and texture labeling.FIG. 13Bis a diagram illustrating an example of a relationship between differences in similarity.

In the combining flow (block S84), first, the image signal processor30performs an autocorrelation calculation on the master camera image by moving the reference region Rf relative to the region of interest Ri by a predefined movement amount in a direction orthogonal to the epipolar line, thereby calculating the degree of similarity (characteristic value) between the region of interest Ri and the reference region Rf (block S121).

Please note that the autocorrelation calculation in the direction orthogonal to the epipolar line may be omitted if the processing time of the image signal processor30needs to be reduced.

The image signal processor30calculates the characteristic value (frequency) based on the degree of similarity calculated in the above-described autocorrelation calculation (block S122), links pixels having similar characteristic values in uncertain regions, classifies linked pixel groups of the uncertain regions (seeFIG. 13A), and labels the groups (block S123).

As described above, the characteristic value or the frequency is calculated based on the average value of the intervals between the peaks or the mode value of the peaks of the degree of similarity. The low texture regions are labeled based on the depth of the low point of the degree of similarity. The clear texture regions are excluded from the autocorrelation calculation in advance (FIG. 13B).

<Details of the Depth Information Correction Processing>

An example of a flow of depth information correction processing (i.e., the block S34) will be described with reference toFIGS. 14 to 20.

FIG. 14is a diagram illustrating an example of a flow of the depth information correction processing.FIG. 15is a diagram illustrating an example of criteria for excluding an incorrect set of the depth information and the ToF depth information.FIG. 16Ashows a diagram illustrating an example in which peripheral regions Rp1and Rp2are added toFIG. 5A.FIG. 16Bshows a diagram in which the peripheral regions Rp1and Rp2are added toFIG. 4B.FIG. 17shows a diagram illustrating an example of the depth information after replacement processing.

FIGS. 16A, 16B and 17show diagrams when there is no leakage (or seepage) in the uncertain region. The “leakage” occurs due to the processing for smoothing the depth information of the uncertain region. The processing is automatically performed by the electronic device100(e.g., the image signal processor30) even if the uncertain region does not include an in-focus position.

FIG. 18Ais a diagram in which the peripheral regions Rp1and Rp2are added toFIG. 5A.FIG. 18Bis a diagram in which the peripheral regions Rp1and Rp2are added toFIG. 4B.FIG. 19is a diagram illustrating another example of the depth information after replacement processing.FIGS. 18A, 18B and 19show the diagrams when there is leakage. The uncertain region R2expands due to the smoothing processing and infiltrates the peripheral region Rp2.

FIG. 20is a diagram illustrating an example of a camera image after the depth information correction processing according to the present disclosure.

The image signal processor30defines criteria (block S141). The criteria are used to exclude incorrect data in the peripheral regions Rp1and Rp2.

As shown inFIGS. 16A and 16B, the peripheral region Rp1is a surrounding region which surrounds the uncertain region R1, and the peripheral region Rp2is a surrounding region which surrounds the uncertain region R2. A peripheral region is a region adjacent to the uncertain region. The peripheral region is in contact with the uncertain region. Alternatively, a peripheral region may be separated from the uncertain region by a gap. It is desirable that the gap is larger than the amount of leakage of the uncertain region.

In S141, the criteria are defined based on the correct region Rc that is an in-focus area which has neither a low texture region nor a repeated pattern region. For example, in order to define the criteria, the image signal processor30calculates an average value Ave1(first average value) of the ToF depth information of the correct region Rc and an average value Ave2(second average value) of the depth information of the correct region Rc.

As shown inFIG. 15, a graph with the ToF depth information on the horizontal axis and the depth information on the vertical axis is divided into four quadrants Q1, Q2, Q3and Q4by the average values AVE1and AVE2.

The quadrant Q2is an incorrect region where ToF depth information is less than the first average value AVE1and depth information is greater than the second average value AVE2. The quadrant Q4is an incorrect region where ToF depth information is greater than the first average value AVE1and depth information is less than the second average value AVE2.

A set of depth information and ToF depth information in the Q2and the Q4is considered incorrect and excluded as an illegal value. On the other hand, the Q1and the Q3are correct regions and thus a set of depth information and ToF depth information in the Q1and Q3is not excluded.

The image signal processor30excludes a set of incorrect depth information and ToF depth information in the peripheral region based on the criteria (block S142). For example, one or more sets of depth information and ToF depth information in the Q2and the Q4are excluded from data used in the next block S143.

The image signal processor30calculates a relationship between ToF depth information of the peripheral region and depth information of the peripheral region (block S143). The calculation in the block S143may be performed by statistical method, e.g., regression analysis or histogram analysis etc. Accuracy of the calculated relationship is high since the incorrect values in the peripheral region are excluded in advance.

As a result of the block S143, the following model can be obtained:

where Y is a depth value, X is a ToF depth value, and a and b are parameters estimated by the regression analysis.

The image signal processor30estimates depth information in the uncertain region based on the relationship (i.e., the model defined by the formula (1)) (block S144). The estimation processing is performed for pixels where ToF depth information could be detected. That is to say, in the example shown inFIG. 4B, estimation processing is not performed for pixels in the background region Rb since it is a non-detection region where ToF depth information could not be detected.

The image signal processor30replaces the depth information of the uncertain region with the estimated depth information of the uncertain region, thereby acquiring the corrected depth information (block S145). As shown inFIG. 17, incorrect depth information in the region R2fhas been completely replaced with the estimated depth information. As a result, depth values in the region R2become uniform.

In contrast with this, in a case shown inFIG. 19, incorrect depth information in a region R4remains due to the “leakage” described above. The region R4is a region where the uncertain region R2expands (i.e., “leakage” portion).

The image signal processor30smooths the depth information of the uncertain region based on the depth information of the peripheral region (block S145). Thereby, the remaining incorrect depth information in the region R4can be modified and depth values in the region R2become uniform.

Please note that the blocks S142to S146are performed for each uncertain region. In case ofFIG. 16A, the blocks S142to S146are respectively performed for the uncertain regions R1and R2.

According to the flow described above, a camera image with appropriate bokeh can be generated. For example, as shown inFIG. 20, an inappropriate bokeh in the checkered pattern has been corrected. The bokeh region B inFIG. 21has been corrected to be an in-focus region.

In at least one embodiment, the blocks S141and S142may be omitted if an exclusion processing is not performed.

In at least one embodiment, the block S146may be omitted if a smoothing processing is not performed. For example, if a peripheral region is set to be separated from the uncertain region by a gap which is larger than the amount of leakage of the uncertain region, the block S146may be omitted.

In at least one embodiment, the uncertain region may be dilated by performing morphological processing between the S141and the S142.

According to the present disclosure, the uncertain region detection processing is performed to detect an uncertain region where depth information may be false, and the depth information correction processing is performed to correct depth information of the detected uncertain region based on the relationship between the ToF depth information of a peripheral region adjacent to the uncertain region and the depth information of the peripheral region. Thereby, an image with appropriate bokeh can be generated even if a relationship between the stereo depth information and the ToF depth information is not clear, or even if depth information is false due to low texture or repeated pattern of an object.

Further, according to the present disclosure, incorrect data in the peripheral region are excluded before calculating the relationship by means of the criteria based on the correct region defined by the in-focus position. Thereby, an accuracy of the relationship can be improved and a high accuracy of correcting depth information can be achieved.

In the description of embodiments of the present disclosure, it is to be understood that terms such as “central”, “longitudinal”, “transverse”, “length”, “width”, “thickness”, “upper”, “lower”, “front”, “rear”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inner”, “outer”, “clockwise” and “counterclockwise” should be construed to refer to the orientation or the position as described or as shown in the drawings in discussion. These relative terms are only used to simplify the description of the present disclosure, and do not indicate or imply that the device or element referred to must have a particular orientation, or must be constructed or operated in a particular orientation. Thus, these terms cannot be constructed to limit the present disclosure.

In addition, terms such as “first” and “second” are used herein for purposes of description and are not intended to indicate or imply relative importance or significance or to imply the number of indicated technical features. Thus, a feature defined as “first” and “second” may comprise one or more of this feature. In the description of the present disclosure, “a plurality of” means “two or more than two”, unless otherwise specified.

Various embodiments and examples are provided in the above description to implement different structures of the present disclosure. In order to simplify the present disclosure, certain elements and settings are described in the above. However, these elements and settings are only by way of example and are not intended to limit the present disclosure. In addition, reference numbers and/or reference letters may be repeated in different examples in the present disclosure. This repetition is for the purpose of simplification and clarity and does not refer to relations between different embodiments and/or settings. Furthermore, examples of different processes and materials are provided in the present disclosure. However, it would be appreciated by those skilled in the art that other processes and/or materials may also be applied.

Any process or method described in a flow chart or described herein in other ways may be understood to include one or more modules, segments or portions of codes of executable instructions for achieving specific logical functions or steps in the process, and the scope of a preferred embodiment of the present disclosure includes other implementations, in which it should be understood by those skilled in the art that functions may be implemented in a sequence other than the sequences shown or discussed, including in a substantially identical sequence or in an opposite sequence.

The storage medium mentioned above may be read-only memories, magnetic disks, CD, etc.

Although embodiments of the present disclosure have been shown and described, it would be appreciated by those skilled in the art that the embodiments are explanatory and cannot be construed to limit the present disclosure, and changes, modifications, alternatives and variations can be made in the embodiments without departing from the scope of the present disclosure.