KR101771325B1 - Apparatus and method for deriving adaptive threshold and for classifying region of coated tongue, region of tongue substance, and region of mixed - Google Patents

Apparatus and method for deriving adaptive threshold and for classifying region of coated tongue, region of tongue substance, and region of mixed Download PDF

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KR101771325B1
KR101771325B1 KR1020150151165A KR20150151165A KR101771325B1 KR 101771325 B1 KR101771325 B1 KR 101771325B1 KR 1020150151165 A KR1020150151165 A KR 1020150151165A KR 20150151165 A KR20150151165 A KR 20150151165A KR 101771325 B1 KR101771325 B1 KR 101771325B1
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threshold value
total variation
calculating
region
adaptation
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KR20170050094A (en
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정창진
김근호
장준수
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한국 한의학 연구원
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/45—For evaluating or diagnosing the musculoskeletal system or teeth
    • A61B5/4538—Evaluating a particular part of the muscoloskeletal system or a particular medical condition
    • A61B5/4542—Evaluating the mouth, e.g. the jaw
    • A61B5/4552—Evaluating soft tissue within the mouth, e.g. gums or tongue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation

Abstract

A computing auxiliary device according to an exemplary embodiment of the present invention includes a first adaptation threshold value for a snow and a second adaptation threshold for a snow state in a snowy region of an input image, And a processor for classifying the input image into at least three areas based on the calculated first adaptation threshold value and the second adaptation threshold value.

Figure R1020150151165

Description

[0001] APPARATUS AND METHOD FOR DERIVING ADAPTIVE THRESHOLD AND FOR CLASSIFYING REGION OF COATED TONGUE, REGION OF TONGUE SUBSTANCE, AND REGION OF MIXED [0002]

In order to find the adaptive threshold value in a given color image, we first calculate the threshold value that distinguishes the area, but not the second area. It is about the technique of calculating the threshold of distinction to distinguish the area.

In modern society, interest in health is increasing day by day. Along with this era of interest, as technologies such as data analysis method and tool by real-time data collection are advanced, it becomes possible to monitor health condition and provide personalized health care service.

In addition, convenience and customization of health services and related systems are being strengthened due to diversification of customers 'demands and improvement of expectations according to changes in consumers' consciousness. Based on accumulated personal health data, prevention of lifestyle- Personalized health care projects such as weight management are rapidly growing.

In recent years, the state of the tongue has been used as a variety of measures to judge health.

Sneeze is a part of the tongue that is covered with moss on the surface of the tongue in a heterogeneous color. It occurs due to reflux from the digestive organs, erosion of the nipple, and the like. Therefore, the distribution and color of sutae changes according to the health condition, and in traditional medicine such as China, Japan, and Korea, the change of state is used for diagnosis.

In recent years, an algorithm for analyzing the characteristics of objective sutures has been developed through color tongue images. The most advanced technique for analyzing sutures is classification of the suture regions in the tongue images.

The characteristics of the diagnosis are different according to the method of defining the diagnosis area, and the result of the diagnosis is different. Therefore, it is possible to improve the repeatability and accuracy of the diagnosis by defining the area based on objective and accurate criteria.

Korean Patent Registration No. 1063343 Korean Patent Registration No. 0904084

According to an embodiment of the present invention, a computing auxiliary apparatus includes a calculating unit that calculates a first adaptation threshold value for a snow state and a second adaptation threshold value for a snow state in a snowy region of an input image, And a processing unit for classifying the input image into at least three areas based on the adaptation threshold value.

The calculating unit may set a temporary threshold value and calculate a first total variation and a second total variation for each of the plurality of regions divided by the set temporary threshold value Calculating a sum total variation through a sum of the first total variation and the second total variation calculated and calculating a sum total variation using the calculated total variation, 1 adaptation threshold value and the second adaptation threshold value.

The calculating unit may calculate at least one of the first adaptation threshold value and the second adaptation threshold value as a threshold value corresponding to the calculated minimum sum total variation.

The calculating unit may calculate the temporal threshold value (CIELAB) using at least one of a * or b * of the R, CIE L * a * b * (CIELAB) color space of the sRGB color space and an H value of the HSV color space .

The calculation unit may calculate a first total variation and a second total variation for each of the plurality of regions from which the boundary pixels have been removed by removing pixels corresponding to the plurality of boundary between regions, total variation.

The calculating unit according to an embodiment binarizes color elements in the snow region and calculates the first adaptation threshold value and the second adaptation threshold value based on the binarized color elements.

According to an embodiment of the present invention, there is provided a computing auxiliary apparatus including a calculation unit for calculating adaptation thresholds for a snowy region in a snowy region of an input image, and a computing unit for computing a snowy region, a snowy region, And the like.

The calculating unit may calculate a first total variation and a second total variation for each of the plurality of regions separated by the set temporary threshold value by calculating a temporary threshold value and calculating a first total variation and a second total variation Calculating a sum total variation through a sum of the calculated first total variation and a second total variation and calculating the sum total variation using the calculated total variation, And calculates at least one of the values.

The method according to an embodiment includes the steps of calculating a first adaptation threshold value for the snow and a second adaptation threshold value for the snow condition in the snowy region of the input image and comparing the calculated first adaptation threshold value and the second adaptation threshold value , A saliva area, a saliva area, and a saliva and a sulcus mixed area.

The calculating step according to an embodiment may include the steps of setting a temporary threshold value, a first total variation and a second total variation for each of the plurality of regions divided by the set temporary threshold, calculating a sum total variation through a sum of the first total variation and the second total variation calculated, and calculating a sum total variation by summing the first total variation and the second total variation, And calculating at least one of the first adaptation threshold value and the second adaptation threshold value using the variation.

The step of calculating at least one of the first adaptation threshold value and the second adaptation threshold value using the calculated total variation according to an exemplary embodiment may further include the steps of: And calculating a threshold value as at least one of the first adaptation threshold value and the second adaptation threshold value.

The step of setting the temporary threshold according to an exemplary embodiment may include setting at least one of a * or b * of the R, CIE L * a * b * (CIELAB) color space of the sRGB color space and an H value of the HSV color space And setting the temporary threshold value using the threshold value.

The step of calculating the first total variation and the second total variation according to an embodiment may comprise the steps of removing pixels corresponding to the plurality of inter-region boundaries, And calculating a first total variation and a second total variation for each of the plurality of regions from which the pixels having the removed pixels are removed.

The calculating step according to an embodiment includes the steps of binarizing the color element in the snow area and calculating the first adaptation threshold value and the second adaptation threshold value based on the binarized color element do.

A program according to an embodiment includes a set of instructions for calculating a first adaptation threshold for snow and a second adaptation threshold for a snow in a snowy region of an input image and a second adaptation threshold for the first adaptation threshold and the second adaptation threshold And classifying the input image into at least three or more regions based on the input image.

FIG. 1 illustrates a screening area, a screening area, a screening area, and a screening area classified through a computing auxiliary device according to an exemplary embodiment.
2 is a diagram illustrating a computing auxiliary apparatus according to an embodiment.
FIG. 3 is a view for explaining different regions classified as temporary threshold values. FIG.
4 is a diagram for explaining an embodiment for calculating an adaptive threshold value from a sum total variation.
FIG. 5 is a view for explaining a method of classifying a glaucoma region, a snowy region, a sex and a snowy mixed region according to an embodiment.
6 is a view for explaining a method of calculating an adaptive threshold value by setting a temporary threshold value.

It is to be understood that the specific structural or functional descriptions of embodiments of the present invention disclosed herein are presented for the purpose of describing embodiments only in accordance with the concepts of the present invention, May be embodied in various forms and are not limited to the embodiments described herein.

Embodiments in accordance with the concepts of the present invention are capable of various modifications and may take various forms, so that the embodiments are illustrated in the drawings and described in detail herein. However, it is not intended to limit the embodiments according to the concepts of the present invention to the specific disclosure forms, but includes changes, equivalents, or alternatives falling within the spirit and scope of the present invention.

The terms first, second, or the like may be used to describe various elements, but the elements should not be limited by the terms. The terms may be named for the purpose of distinguishing one element from another, for example without departing from the scope of the right according to the concept of the present invention, the first element being referred to as the second element, Similarly, the second component may also be referred to as the first component.

It is to be understood that when an element is referred to as being "connected" or "connected" to another element, it may be directly connected or connected to the other element, . On the other hand, when an element is referred to as being "directly connected" or "directly connected" to another element, it should be understood that there are no other elements in between. Expressions that describe the relationship between components, for example, "between" and "immediately" or "directly adjacent to" should be interpreted as well.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, the terms "comprises ", or" having ", and the like, are used to specify one or more of the features, numbers, steps, operations, elements, But do not preclude the presence or addition of steps, operations, elements, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries are to be interpreted as having a meaning consistent with the meaning of the context in the relevant art and, unless explicitly defined herein, are to be interpreted as ideal or overly formal Do not.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference symbols in the drawings denote like elements.

FIG. 1 illustrates a screening area, a screening area, a screening area, and a screening area classified through a computing auxiliary device according to an exemplary embodiment.

The color characteristics of the tongue and the surface of the tongue are different from each other.

Most classification schemes are categorized by thresholds. Due to the inherent characteristics of the tongue color between the measurers and the image measurement environment, it is inevitable that the accuracy of region classification will be reduced if a certain threshold value is used in all situations. The computing auxiliary device according to an exemplary embodiment can derive an adaptive threshold value corresponding to the color characteristics of the image, the snow and the skin, thereby improving the accuracy of the area classification.

In the case of neurotrophic keratinization, the color value of the corresponding pixel indicates the color characteristics mixed with the nasopharyngeal nasopharyngeal (white) when acquired with the image because the temporal nipple is fine. Accordingly, the computing auxiliary apparatus according to an exemplary embodiment of the present invention can distinguish a common color image 110 from a snowy region 120, a snowy region 140, and a mixed region 130 between a snowy region and a snowy region. And the color of the suture can be more accurately analyzed.

Specifically, the computing assist apparatus according to an exemplary embodiment of the present invention is a method of finding an adaptive threshold value from a given color tongue image. The computing assist apparatus calculates a threshold value for distinguishing a region, which is not a tongue and a tongue, We can calculate the threshold value for discriminating the mixed region of the sulcus and the snowy region from the region of the sulcus.

In order to calculate the adaptive threshold value, the computing auxiliary device according to an exemplary embodiment computes a total variation (TV, total variation) within two regions separated by a certain threshold value (temporary threshold value) TV, Total variation) to calculate the sum total variation (STV), thereby calculating the threshold value at which the sum total variation (STV) becomes minimum.

Since the spermatozoa and spermatozoa are heterogeneous, the color values change greatly in the boundary region between the two regions, and the change in the color value is relatively small in the sperm and the sperm region. When the total variation (STV) is minimized by these characteristics, an optimized adaptive threshold value for distinguishing the two regions can be derived.

As a result, using the present invention, it is easy to apply to a mobile analysis system which is not easy with existing patented technology because it is robust to an image measurement environment.

2 is a diagram illustrating a computing assistance device 200 according to one embodiment.

The computing aid device 200 according to an exemplary embodiment can derive an adaptive threshold value corresponding to color characteristics of an image, a snow texture, and a sketch, thereby increasing the accuracy of area classification.

For this, the computing auxiliary device 200 according to an exemplary embodiment may include a calculation unit 210 and a processing unit 220. [

The calculating unit 210 may calculate a plurality of adaptation threshold values for the snow in the snowy region of the input image.

For example, the calculating unit 210 may calculate a first adaptation threshold value for the snow and a second adaptation threshold value for the snow state in the snowy region of the input image. To this end, the calculating unit 210 may binarize the color elements in the snow region, and may calculate the first adaptation threshold value and the second adaptation threshold value based on the binarized color elements.

The first adaptation threshold value is a threshold value that distinguishes between the area where the suture appears and the area where the suture does not appear in the snow area, and distinguishes between the snow area and the snow area and the snow area.

Also, the second adaptation threshold value is a threshold value for distinguishing between a region in which the saliva appears and a region in which the saliva does not appear.

That is, through the first adaptation threshold value and the second adaptation threshold value, it is possible to distinguish between the snowy region, the snowy region and the snowy region, and the snowy region.

The calculating unit 210 may calculate a first adaptation threshold value and a second adaptation threshold value for the object according to characteristics of the input image.

For this, the calculating unit 210 may set a temporary threshold value. For example, the calculation unit 210 may calculate the temporal threshold value (CIELAB) using at least one of a * and b * of the R, CIE L * a * b * (CIELAB) color space of the sRGB color space, Can be set.

Next, the calculating unit 210 may calculate a first total variation and a second total variation for each of the plurality of regions divided by the set temporary threshold.

In addition, the calculating unit 210 may calculate the sum total variation through the sum of the calculated first total variation and second total variation. At this time, the calculating unit 210 may calculate at least one of the first adaptation threshold value and the second adaptation threshold value using the calculated total variation. For example, the calculator 210 may calculate the threshold value corresponding to the minimum value of the calculated sum total variation as at least one of the first adaptation threshold value and the second adaptation threshold value.

The processing unit 220 according to an embodiment can classify a plurality of regions, for example, a snowy region, a snowy region, and a snowy and snowy mixed region based on the calculated first and second adaptation threshold values have.

Meanwhile, the calculating unit 210 removes pixels corresponding to a plurality of inter-region boundaries, and calculates a first total variation and a second total variation (hereinafter referred to as " first total variation " ) May be calculated. This will be described in detail with reference to FIG.

FIG. 3 is a view for explaining different regions classified as temporary threshold values. FIG.

Reference numeral 310 denotes an image classified into different regions with a temporary threshold value. Among them, reference numeral 311 is classified into a speech region, and a pixel 312 corresponding to a boundary can be removed to calculate a first total variation and a second total variation.

Reference numeral 320 denotes an image generated by removing a pixel 312 corresponding to a boundary in the image of the reference numeral 310, and reference numeral 321 denotes a reference area. At this time, the computing assist device may calculate a first total variation and a second total variation from the image 320 using an algorithm.

For example, the computing unit of the computing aid may calculate a first total variation and a second total variation using Equation (1).

[Equation 1]

Figure 112015105359317-pat00001

In Equation (1)

Figure 112015105359317-pat00002
(
Figure 112015105359317-pat00003
,
Figure 112015105359317-pat00004
), And A 'denotes an area separated by a temporary threshold value.

Conventionally, no specific method for deriving the adaptive threshold value is specified, and the first total variation and the second total variation used in the patents used in the present invention are not used. That is, according to the present invention, an algorithm is classified into a sulcus region, a mixed region of the skin and the sulcus, and a sulcus region.

Therefore, the present invention can be implemented with a robust algorithm for image measurement environment, which is easy to apply to a mobile analysis system which was not easy with existing patented technology.

4 is a diagram for explaining an embodiment for calculating an adaptive threshold value from a sum total variation.

Referring first to FIG. 4A, graph 410 represents the sum total variation for CIE a *. The sum total variation can be expressed as the sum of the first total variation and the second total variation, which represents the minimum value at the point 411. [ Reference numeral 411 corresponding to the minimum value can be interpreted as a point where the CIE is 15 and the sum total variation is 15 points. Therefore, it can be classified into a stomach area or a snowy area based on the adaptation threshold value (CIE = 15).

As shown in FIG. 4B, the computing assistant can divide the snow image 420 into a snowing region or a snowing region based on an adaptation threshold value (CIE = 15).

FIG. 5 is a view for explaining a method of classifying a glaucoma region, a snowy region, a sex and a snowy mixed region according to an embodiment.

If a certain threshold value is used, the accuracy of area classification is inevitably lowered. However, the present invention can increase the accuracy of region classification by deriving an adaptive threshold value corresponding to the color characteristics of the image and the snow and the skin.

To this end, a method according to an embodiment calculates a first adaptation threshold value for the snow and a second adaptation threshold value for the eye in the snowy region of the input image, and calculates a first adaptation threshold value and a second adaptation threshold value , It is possible to classify the salivary region, the sulcus region, and the mixed region of the sulcus and the sulcus.

In particular, a method according to an embodiment may allocate a tongue area representing a tongue of color to memory (step 501).

Next, the method according to one embodiment may calculate an adaptation threshold for the skin (step 502). The adaptation threshold value for the skin distinguishes between the non-skin region and the non-skin region, and it can be used as a reference for distinguishing the mixed region of the salivary gland and the glandular region or the glandular region.

The method according to one embodiment may derive a non-surface area using the adaptation threshold for the calculated surface (step 503).

The non-snowy region is an area in which the snowfall is distributed over a threshold value, and can be interpreted as a snowfall region.

On the other hand, the method according to one embodiment may calculate an adaptation threshold value for the obstacle (step 504).

The adaptation threshold value for the spermatozoa distinguishes the area other than the spermatozoa and the spermatozoa, and can be used as a reference for distinguishing the mixed region and the sulcus region of the sulcus and the sulcus, or the sulcus region and the sulcus region.

The method according to one embodiment may utilize the adaptation threshold values for the anatomy to derive the anatomy region and the anatomy / anatomy region (step 505).

6 is a view for explaining a method of calculating an adaptive threshold value by setting a temporary threshold value.

In order to calculate the adaptive threshold value by setting the temporary threshold value, a temporary threshold value must first be set (step 601). For example, the method according to the present invention may use at least one of the a * or b * of the R, CIE L * a * b * (CIELAB) color space of the sRGB color space and the H value of the HSV color space, Can be set.

Next, a method according to the present invention includes removing a pixel corresponding to a boundary between a plurality of regions (Step 602), and calculating a first total variation and a second total variation for each of the plurality of regions from which pixels corresponding to the boundary are removed A second total variation is calculated (step 603).

The method according to the present invention calculates the sum total variation through the sum of the calculated first total variation and second total variation and calculates the sum total variation using the calculated total variation 1 adaptation threshold value and the second adaptation threshold value. At this time, the color element may be binarized in the snow region, and the first adaptation threshold value and the second adaptation threshold value may be calculated based on the binarized color element.

The method according to the present invention may repeat the calculation of the sum total variation for all the threshold candidates and derive a threshold that satisfies the sum total variation of the iteration result (step 605).

As a result, according to the present invention, it is possible to derive an adaptive threshold value that matches the color characteristics of the image, the snow and the eye, thereby improving the accuracy of the area classification.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device , Or may be permanently or temporarily embodied in a transmitted signal wave. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI > or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (16)

Implemented at least temporarily by the computer:
A calculating unit for calculating a first adaptation threshold value for the snow and a second adaptation threshold value for the snow state in the snowy region of the input image; And
The calculating unit calculates,
A temporary threshold value is set, a first total variation and a second total variation are calculated for each of a plurality of regions divided by the set temporary threshold value, and the calculated first total variation Calculating a sum total variation through a sum of a first total variation and a second total variation and calculating a sum total variation by summing the first adaptation threshold and the second adaptation Calculating at least one of the threshold values,
A processing unit for classifying the input image into at least three areas based on the calculated first adaptation threshold value and the second adaptation threshold value,
And a computing device.
delete The method according to claim 1,
The calculating unit calculates,
And calculates a threshold value corresponding to a minimum value of the calculated sum total variation as at least one of the first adaptation threshold value and the second adaptation threshold value.
The method according to claim 1,
The calculating unit calculates,
wherein the temporal threshold value is set using at least one of R * of the sRGB color space, a * or b * of the CIE L * a * b * (CIELAB) color space, and H value of the HSV color space.
The method according to claim 1,
The calculating unit calculates,
Computing a first total variation and a second total variation for each of the plurality of regions from which the boundary pixels have been removed, .
The method according to claim 1,
The calculating unit calculates,
Binarize the color elements in the snow region, and compute the first adaptation threshold value and the second adaptation threshold value based on the binarized color elements.
Implemented at least temporarily by the computer:
A calculating unit for calculating adaptive threshold values for the snow in the snowy region of the input image; And
The calculating unit calculates,
A temporary threshold value is set, a first total variation and a second total variation are calculated for each of a plurality of regions divided by the set temporary threshold value, and the calculated first total variation Calculating a sum total variation through a sum of a first total variation and a second total variation and calculating at least one of the adaptation threshold values using the calculated total variation ,
A processing unit for classifying the snowy region, the snowy region, and the snowy region and the snowy region based on the calculated adaptive threshold values
And a computing device.
delete In a method at least temporarily implemented by a computer,
Calculating a first adaptation threshold value for the snow and a second adaptation threshold value for the snow state in the snowy region of the input image; And
Wherein the calculating step comprises:
Setting a temporary threshold value;
Calculating a first total variation and a second total variation for each of the plurality of regions separated by the set temporary threshold;
Calculating a sum total variation through a sum of the calculated first total variation and second total variation; And
Calculating at least one of the first adaptation threshold value and the second adaptation threshold value using the calculated total variation;
Classifying the first region, the second region, and the second region based on the calculated first adaptation threshold value and the second adaptation threshold value
≪ / RTI >
delete 10. The method of claim 9,
Calculating at least one of the first adaptation threshold value and the second adaptation threshold value using the calculated total variation,
Calculating a threshold value corresponding to a minimum value of the calculated sum total variation as at least one of the first adaptation threshold value and the second adaptation threshold value
≪ / RTI >
10. The method of claim 9,
Wherein the setting of the temporary threshold comprises:
setting the temporary threshold value using at least one of R * of the sRGB color space, a * or b * of the CIE L * a * b * (CIELAB) color space, and H value of the HSV color space
≪ / RTI >
10. The method of claim 9,
Wherein calculating the first total variation and the second total variation comprises:
Removing pixels corresponding to the boundary between the plurality of regions; And
Calculating a first total variation and a second total variation for each of the plurality of regions from which pixels corresponding to the boundary have been removed,
≪ / RTI >
10. The method of claim 9,
Wherein the calculating step comprises:
Binarizing color elements in the snow region; And
Calculating the first adaptation threshold value and the second adaptation threshold value based on the binarized color element
≪ / RTI >
A computer-readable recording medium having recorded thereon a program for carrying out the method according to any one of claims 9 to 14.
21. A program stored on a recording medium, the program being executable on a computing system:
A command set for calculating a first adaptation threshold value for the snow and a second adaptation threshold value for the snow state in the snowy region of the input image; And
Wherein the instruction set to be calculated includes:
A set of instructions for setting a temporary threshold;
A set of instructions for calculating a first total variation and a second total variation for each of the plurality of regions separated by the set temporary threshold;
Calculating a sum total variation through a sum of the calculated first total variation and second total variation; And
And calculating at least one of the first adaptation threshold value and the second adaptation threshold value using the calculated total variation,
And classifying the input image into at least three regions based on the calculated first adaptation threshold value and the second adaptation threshold value,
≪ / RTI >
KR1020150151165A 2015-10-29 2015-10-29 Apparatus and method for deriving adaptive threshold and for classifying region of coated tongue, region of tongue substance, and region of mixed KR101771325B1 (en)

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