66 results about "Maximum Pixel" patented technology

A display device includes, a backlight device, and a backlight control circuit. The backlight device includes a plurality of light sources configured to illuminate a display panel. The backlight control circuit is configured to apply a filter to a target part of the input image to generate a filtered image part. The target part of the input image is displayed in a corresponding region of the display panel illuminated by a target light source of the plurality of light sources. The backlight control circuit is further configured to control a luminance level of the target light source based on a maximum pixel luminance level of the filtered image part and a local average picture level (APL) of the filtered image part.

The invention relates to the field of image processing, in particular to a workpiece feature recognition method for a CNC machining center, and the method comprises the steps: obtaining the LAB value of each pixel point of a workpiece image to be machined and the gradient value of each pixel point, and traversing the pixel point distribution complexity of each neighborhood window of the pixel points, selecting a neighborhood window corresponding to the maximum pixel point distribution complexity as a target window of the pixel points, and taking the product of the calculated color difference and gradient difference between the pixel points and other pixel points in the target window and the window distribution complexity of the target window as the local saliency of the pixel points; screening all edge points based on the local saliency value to obtain a contour edge image; and performing matching in a workpiece database based on the contour image so as to match a corresponding processing procedure. According to the method, the edge points are screened in combination with the local significance of the pixel points and the optimal significance threshold, the contour edge of the workpiece can be extracted more accurately, and then the accuracy of workpiece feature recognition is improved.

The invention discloses a guided wave self-adaptive enhanced time reversal imaging method and system for vehicle structure damage and a computer program product, and belongs to the technical field of aircraft structure health monitoring. The envelope of the damage scattering signal is calculated through Shannon continuous complex wavelet transform and Hilbert transform, and m wave bands with the maximum peak value are selected as potential damage scattering signal wave bands; the damage factor of each potential wave band is calculated, the wave band with the maximum damage factor is a real damage scattering wave band, other signals are set to be zero, and therefore guided wave self-adaption enhancement is achieved, and the signal-to-noise ratio is increased; on the basis of extraction of a damage scattering wave band, a time reversal focusing imaging method is adopted, imaging results generated when all the sensors serve as excitation are superposed, and the point with the maximum pixel value is the damage position. The implementation process is accurate and efficient, and damage positioning can be achieved through the piezoelectric sensor monitoring network on the main load-bearing structure.

The embodiment of the invention discloses an image splicing method and device of a deviation rectification linear array camera, electronic equipment and a medium. The method comprises the following steps: enabling at least two line-scan digital cameras to directly face a checkerboard calibration plate for image acquisition to obtain target images acquired by the line-scan digital cameras respectively; for target images acquired by two adjacent linear array cameras, determining adjacent checkerboard demarcation points as feature points according to a pixel point with the maximum pixel gradient in the target images and pixel points adjacent to the pixel point with the maximum pixel gradient; and according to the image coordinates of the feature points in the target image and the plane coordinates of the feature points in a plane coordinate system established by taking the checkerboard calibration plate as a reference, determining a perspective transformation matrix for transformation between the image coordinates and the plane coordinates. According to the scheme, under the condition that the line-scan digital camera keeps a large view field range, the images of the line-scan digital camera are precisely spliced, and the fusion degree of the spliced images is improved.

The invention discloses a polyp image size estimation method and system based on a deep learning model. The method comprises the following steps: acquiring a plurality of image frames; the frame with the maximum pixel diameter is used as a representative frame; estimating a target polyp depth value by adopting a depth estimation model, and calculating a representative depth value by adopting a median-based mode; and calculating a product of the representative pixel diameter, the representative depth value and a given scale factor to obtain a size estimation value of the target polyp. According to the unified polyp size estimation method, a depth estimation model and a segmentation model are combined, and the requirement for clear calibration of a camera is eliminated by estimating an absolute depth size scale factor; meanwhile, a plurality of visual angles are collected and analyzed, and a frame capable of presenting the maximum apparent diameter is selected, so that the polyp cross section is measured through an approximately vertical visual angle, the influence caused by the angle problem is effectively solved, and more accurate and reliable polyp size measurement can be provided.

The invention discloses a partition backlight adjusting method and system, and the method comprises the steps: analyzing the background environment brightness and a frame image signal of an HUD display region, and enabling a system to judge whether to start a lowest backlight mode or not after the environment brightness is detected; in the lowest backlight mode, calculating the maximum pixel value of the sub-image in each subarea, and adjusting the backlight and pixel values of the subareas according to the maximum value; in the non-lowest backlight mode, backlight and pixel values of the partitions are determined directly according to the maximum pixel value of the frame image; and the system generates an image pixel compensation curve for each partition, and carries out filtering processing on the backlight value to obtain a filtered backlight value. The pixel value of each subarea is adjusted through the compensation curve and the filtered backlight value, and therefore it is ensured that the display effect is clear and visible under different environment brightness. Therefore, the interference of HUD light leakage at night and overhigh ambient brightness in daytime on display content is effectively relieved, the visual experience of a driver is improved, and the interference in driving is reduced.

The invention relates to the technical field of image processing, and provides an image rain removal method and system based on multi-scale prior injection, and the method comprises the steps: carrying out the subtraction of a gray image formed by a maximum pixel value and a minimum pixel value of a rainy day image along a channel dimension; extracting multi-scale potential background prior features of the initial rough rain-free image through a residual branch of a pre-trained U-Net network; under the guidance of the multi-scale potential background prior features, multi-scale rain stripe features of the rainy day image are extracted through a PITform branch, and first-scale decoder rain stripe features are obtained; and the image passes through a Tform module and a convolution module and then is added with a rainy day image to obtain a rain-removed image. According to the method, the U-Net network is adopted, the prior information is introduced into each scale layer, the network is guided to generate the rain-free image with rich texture details, accurate image recovery is realized, and the rain removal performance is improved.

The embodiment of the invention provides a pleural effusion grading model training method, a pleural effusion grading method and electronic equipment, and relates to the technical field of image processing. The method comprises the following steps: acquiring a pleural effusion ultrasonic image, inputting the pleural effusion ultrasonic image into a pre-trained pleural effusion grading model to obtain an effusion binary mask, performing template matching on the pleural effusion ultrasonic image, identifying at least two reference scale marks with a known physical distance marked on a scale in the image, and determining the pleural effusion grading model according to the identified reference scale marks. Determining the pixel coordinates of each reference scale in the image, performing connected domain analysis on the binary mask, extracting a connected domain with the maximum longitudinal extension length as a target effusion region, calculating the maximum pixel length of the target effusion region in the longitudinal axis direction of the image, and determining the target effusion region according to the physical spacing, the two pixel coordinates and the maximum pixel length. The physical depth of the pleural effusion is calculated, and clinical grading is conducted. Therefore, evaluation from the pleural ultrasound image to the physical depth of the effusion can be realized.

To provide an image display device capable of displaying an image with high picture quality even if a pixel of high luminance that an original image does not have is included in a frame of a video signal.SOLUTION: A maximum luminance information acquisition section 1 extracts meta data indicating a maximum luminance value and acquires maximum luminance information. A cumulative histogram generation section 10 divides a range from lightness corresponding to a minimum pixel value determined by a bit number of a video signal to lightness corresponding to a maximum pixel value into a plurality of lightness levels and, while defining lightness of a pixel having lightness equal to or higher than a threshold which is set based on lightness indicated by the maximum luminance information as 0, generates a cumulative histogram indicating a ratio of cumulative pixel numbers equal to or lower than each lightness level on one or more frames basis. A gradation curve setting section 4 sets a gradation curve formed from an input pixel value and an output pixel value in accordance with a lightness level having maximum lightness indicated by the cumulative histogram, and the input pixel value is converted into the output pixel value in accordance with the gradation curve and supplied to a drive section 5.SELECTED DRAWING: Figure 1

The object of the present invention is to provide an X-ray inspection apparatus that can adjust the sensitivity without passing an article through it multiple times. [Solution] The X-ray inspection apparatus 10 comprises a conveyor 12, an X-ray irradiator 13, an X-ray line sensor 14, a foreign object inspection unit 21b, an HDD 25, a controller 20, and a monitor 30. The foreign object inspection unit 21b performs predetermined processing on the X-ray transmission image of the item G, which is generated based on the detection result of the X-ray line sensor 14, to create a judgment image. Furthermore, the foreign object inspection unit 21b determines whether the item is good or bad by comparing the pixel value indicated by each pixel of the judgment image with a preset threshold. The HDD 25 stores the maximum pixel value Pmax, which is the maximum value of the pixel, as a feature quantity. The controller 20 aggregates the maximum pixel value Pmax to generate statistical data 33. The monitor 30 displays the statistical data 33.

This invention discloses a method and system for polyp image size estimation based on a deep learning model. The method includes: acquiring multiple image frames; using the frame with the largest maximum pixel diameter as a representative frame; estimating the depth value of the target polyp using a depth estimation model; calculating a representative depth value using a median-based approach; and calculating the product of the representative pixel diameter, the representative depth value, and a given scaling factor to obtain the estimated size of the target polyp. This invention provides a unified polyp size estimation method that combines a depth estimation model and a segmentation model. By estimating the absolute depth-size scaling factor, it eliminates the need for explicit camera calibration. Simultaneously, by acquiring and analyzing multiple viewpoints and selecting the frame that presents the largest apparent diameter, it measures the polyp cross-section from an approximately vertical viewpoint, effectively solving the influence of angle issues and providing more accurate and reliable polyp size measurements.

The invention discloses a line scanning imaging device, a correction method of the line scanning imaging device and a control panel of the line scanning imaging device. The correction method comprises the following steps: obtaining a maximum pixel brightness value in a plurality of first images generated by a plurality of line scanning camera modules when a light source is started, wherein the plurality of first images are spliced in a scanning line extension direction; obtaining a plurality of second images generated by the plurality of line scan camera modules when the light source has a predetermined light intensity, the light intensity of the light source when the maximum pixel brightness value is less than or equal to a first predetermined brightness value being the predetermined light intensity, the brightness value of each pixel in the second image of each line scanning camera module is smaller than or equal to the first preset brightness value; obtaining a plurality of third images generated by the plurality of line scan camera modules when the light source is turned off; and generating correction information according to the plurality of second images and the plurality of third images. The correction method can improve the accuracy of image correction.

Disclosed is a method for detecting a line in an image or a video. The method includes smoothing the image, selecting an area in which a line is to be detected, determining a center pixel in the selected area, applying a window centered on the center pixel, detecting a sum of gradient angle differences of pixels surrounding the center pixel, classifying the center pixel determined as the line candidate into a ridge or a valley, and eliminating a non-maximum pixel.

The present invention discloses a smoothing method for a suspension crossbeam after profile optimization, including: S1. Defining an optimization area on the digital model of the suspension crossbeam, and conducting profile optimization design on the stiffeners within the optimization area; S2. Obtaining the geometric features of the plane where the optimization area is located and converting them into a binary image; S3. Calculating the maximum pixel distance H between adjacent isolated stiffeners X in the binary image; S4. Performing a first morphological operation on the isolated stiffeners X in the binary image using a circular structural element J1 to obtain the overall stiffeners Y; S5. Extracting the central skeleton of the overall stiffeners Y and obtaining the pixel length L of the central skeleton; S6. Performing a third morphological operation on the central skeleton using a circular structural element J2 to obtain the optimized stiffeners Z; S7. Extracting the numerical features of the optimized stiffeners Z and projecting them onto the digital model of the suspension crossbeam to obtain the digital model of the suspension crossbeam after smoothing processing. The smoothing method for the suspension crossbeam after profile optimization provided by the present invention can effectively eliminate the phenomena of uneven profile edges and excessive isolated areas existing after the profile optimization design of the suspension crossbeam.

The present invention relates to a structural vibration analysis method and device based on video images, comprising: acquiring a video image of an object to be tested vibrating along a vibration direction frame by frame, converting each frame of the video image into a grayscale image, intercepting a specified area of each grayscale image frame, and calculating the maximum pixel value in the specified area of each grayscale image frame, and analyzing the vibration frequency and / or vibration amplitude of the object to be tested based on the maximum pixel value in the specified area of each grayscale image frame, wherein the specified area of each grayscale image frame has the same position on each grayscale image frame and includes a boundary in the vibration direction of the object to be tested and a column of pixel points in the neighborhood of the boundary. The technical solution provided by the present invention can accurately analyze and obtain the vibration frequency and / or vibration amplitude of the object to be tested by using the real pixel values corresponding to the grayscale image of the intercepted video image of the object to be tested vibrating along the vibration direction, with low computational complexity and high speed.

The invention provides a display method and electronic equipment, and is applied to the technical field of display driving. The display method comprises the steps of determining a target display scene; determining a target refresh rate and a target pixel turn-on voltage corresponding to the target display scene; wherein the target refresh rate limits the maximum pixel charging duration of the target display scene, the target pixel turn-on voltage is in negative correlation with the maximum pixel charging duration, and the display device is controlled to display the target scene at the target refresh rate and the target pixel turn-on voltage so as to reduce the display power consumption of the display device.

The present application discloses a method for structural shock location based on adaptive time-reversal focusing imaging and image fusion, the method comprising extracting multiple narrowband wave signals of different frequencies of the shock stress wave signal through complex Morlet wavelet transform; setting the initial parameters of the algorithm, for each of the narrowband wave signals, obtaining the adaptive time-reversal focusing imaging result images of all narrowband wave signals based on the set initial parameters of the algorithm, and then calculating the image focusing index of each imaging result image, setting the focusing index threshold of the imaging result image according to the actual shock location imaging accuracy requirements, multiplying and fusion all the imaging result images above the focusing index threshold, and obtaining the grid point coordinates corresponding to the maximum pixel value of the fused image as the predicted shock position. The shock location accuracy of the present application can reach a relatively high level. The present application also discloses a corresponding system.

The present invention relates to an integrated circuit of an image capture device for automatically optimizing the ratio (A1 / A0) of the output image area (A1) to the main window area (A0), and for optimizing (preferably maximizing) the product D1×A1 of the output pixel density (D1) multiplied by the output image area (A1) of the motion detection area that defines an output image (P1) consisting of a predetermined maximum number (N1m) of pixels. The integrated circuit includes an array light sensor for capturing an input image (P0) and a processing unit configured to perform the following actions: - in an image detection mode, a series of consecutive reference images (Prefj) are captured at different times by a subarray of Nr reference light sensors, and a number (Nm) of motion pixels (pm) whose value difference between the two reference images exceeds a predetermined threshold deviation for detecting motion within an area of ​​the reference image is determined, and then switching; - in an image capture mode, the parameters of the captured output image are defined as follows: ● an output window (W1) with an output window area (A1) surrounding at least a predetermined number (Nmt) of the motion pixels (pm), and ● an output pixel density D1 = N1 / A1 ≦ DO, where N1 ≦ N1m; ● capturing an output image (P1) defined by the output window (W1) having the output window area (A1) and the output pixel density (D1) and transferring the output image (P1) including N1 pixels from the integrated circuit.

The invention provides an image coding method and device. The method comprises the following steps: acquiring a pixel value corresponding to a target pixel point with data overflow in a target image after image processing is completed; determining a multiple relationship between the pixel value and a maximum pixel value in a pixel value range which can be represented by the pixel depth of the target pixel point; determining at least one flag bit corresponding to the multiple relation in a plurality of data bits contained in the target pixel point; wherein the at least one flag bit is used for indicating data overflow information of the target pixel point; and encoding a pixel value corresponding to a target pixel point in the target image according to at least one flag bit corresponding to the target pixel point and the remaining data bits in the plurality of data bits to obtain an encoded target image. According to the image processing method and device, the image precision can be improved on the premise that the existing image processing efficiency is not affected.

The invention provides an image acquisition method, device and equipment. The method comprises the following steps: moving a motor to an initial position corresponding to an initial focus; for each segmented interval, determining the exposure duration corresponding to the segmented interval, and determining the stroke step length corresponding to the segmented interval based on the focal points corresponding to the two adjacent rows of the segmented interval; controlling the sensor to expose the minimum pixel row from the synchronous starting moment; for each segmented interval, in the exposure duration corresponding to the segmented interval, controlling the sensor to sequentially expose each pixel row of the segmented interval until the exposure of the maximum pixel row is completed to obtain a current frame image; controlling the motor to move from the starting position from the synchronous starting moment; and for each segmented interval, in the exposure duration corresponding to the segmented interval, controlling the motor to move by adopting the stroke step length corresponding to the segmented interval until the motor is moved to the end position corresponding to the end focus corresponding to the maximum pixel row. According to the scheme, the image definition is improved.

The invention provides an image detection segmentation method and device for image small target clustering. The method comprises the following steps: receiving a multi-resolution original image; converting the original image into a grey-scale map, and obtaining adaptive parameters based on the mean value of the grey-scale map, the maximum pixel value of the grey-scale map and the image brightness intermediate value; performing gamma correction on the original image based on the adaptive parameter to remove an image shadow to obtain a corrected image; performing target detection and segmentation on the corrected image based on a detection segmentation model to obtain the category of a target; under the condition that the target is a small target, clustering the small target by adopting a density clustering algorithm to obtain a clustering result; wherein the small target is a target of which the target size is smaller than a preset size; and outputting the category, confidence and target contour area coordinate points of the small target based on a clustering result. The method can solve the problems that the detection and segmentation algorithm in the prior art is poor in detection and segmentation effect on the shadow part of the image, and messy is generated due to excessive small target segmentation.

The invention discloses a gas leakage detection method and system based on laser speckle imaging, and the method comprises the steps: irradiating the outer wall surface of a to-be-monitored region with laser beam expansion to form laser speckles, and continuously collecting a plurality of monitoring speckle images of the to-be-monitored region; segmenting the monitored speckle image and the corresponding original speckle image without leakage into a plurality of sub-windows, and calculating the frequency spectrum amplitude and the frequency spectrum phase of each sub-window; calculating a correlation coefficient between the two sub-windows according to the spectrum amplitudes and the spectrum phases of the sub-windows corresponding to the original speckle image and the monitoring speckle image; marking the sub-windows in the monitoring speckle image corresponding to all the correlation coefficients smaller than a preset threshold value as gas leakage coarse positioning areas; and for each pixel point in the leakage coarse positioning area, calculating a pixel-level imaging operator of the pixel point, and taking the pixel point corresponding to the maximum pixel-level imaging operator as a gas leakage point.

The invention discloses a display image uniformity statistical method and device and a storage medium, which are used for improving the accuracy of gray uniformity analysis of images shot by a display. Generating a row / column direction minimum bounding rectangular frame for the normalized image; performing intersection processing and connected domain separation processing according to the minimum bounding rectangular frame in the row / column direction, and calculating a gray statistical information value of a separation region; generating a blank image, replacing the gray value of the blank image with the gray statistical information value, and generating a row curved surface image and a column curved surface image; circulating the array of the blank images, generating a dynamic row and column coordinate array of a neighborhood, and generating a vector field image; performing algorithm remapping processing on the normalized image and the vector field image to generate a result image; combining the result images, and filling the maximum pixel value of the multi-channel image into the to-be-processed image to generate the to-be-processed image; and generating a target filling image, and carrying out gray level distribution uniformity analysis according to the gray level value data.

A metrology system is provided including a lighting configuration and camera which are controlled to acquire image stacks of a workpiece, wherein each image stack is acquired utilizing different lighting (e.g., corresponding to different lighting positions). Sets of pixel intensity values from the image stacks are utilized to determine a composite stack which includes one pixel intensity value for each pixel position and focus position. At least some of the pixel intensity values of the composite stack are determined according to a glare reduction process which determines a corresponding pixel intensity value for the composite stack that is less than a maximum pixel intensity value of the corresponding set of pixel intensity values. Focus curve data is determined based at least in part on the pixel intensity values of the composite stack, wherein the focus curve data indicates three dimensional positions of a plurality of surface points on the workpiece.

The application relates to the field of DNI prediction, and discloses a DNI prediction method based on a dense optical flow method and a deep learning network, which comprises the following steps: performing distortion correction on images collected by a full-sky imager; according to images at two time points before and after distortion correction, the speed of each pixel point of a current image is calculated by using a dense optical flow method; when the maximum pixel speed is less than a specified speed threshold, a static model is used to predict the DNI at a future time point, otherwise, a motion model is used to predict the DNI at the future time point; when the motion model is used, a trained deep learning network model is used to predict the clear sky index at the future time point, and then the clear sky index at the future time point is multiplied by the clear sky DNI at the future time point, so that the predicted DNI at the future time point can be obtained. The method disclosed by the application can more accurately reflect the change of the actual DNI, has better prediction accuracy, and is highly adaptable.

The present invention discloses a method for extracting edges of a color image based on reduced concept structure elements and matrix order. The implementation steps are as follows: constructing a pixel window for each pixel point in the color image to be edge-extracted; generating a reduced background matrix for all pixels in each pixel window; generating a reduced object relationship matrix for each pixel window; generating a reduced concept structure element for each pixel; generating a matrix order for each element in each reduced concept structure element; determining the maximum pixel value in each reduced concept structure element; calculating the difference pixels; and extracting the edges in the color image. The present invention overcomes the defects in the prior art of the decreased clarity of the color image processing result and the blurred edge extraction, and improves the clarity and the integrity of the edge after the morphological processing of the color image.

To provide a radiation image processing device capable of acquiring a composite image easy to interpret, a radiation image processing system, a radiation image processing method, and a program.SOLUTION: A console 30 functioning as a radiation image processing device comprises: a control unit 31 which functions as an image acquisition unit for acquiring a plurality of radiation images captured under respectively different imaging conditions, of a subject; a pixel value correction part which corrects a pixel value of at least one of the plurality of radiation images; and an image processing unit 36 which functions as an image composing unit for acquiring one composite image by composing the plurality of radiation images. Each of the plurality of radiation images has an image overlapping area with at least one or more other radiation images. The pixel value correction part corrects the pixel value in such a manner where the maximum value of the pixel value of the at least one radiation image is equal to or less than the maximum value of gradation of the image.SELECTED DRAWING: Figure 5

To mitigate some problems of pixel color mapping used in SLHDR type of HDR video decoding, we teach a high dynamic range video encoding circuit (300) configured to encode a high dynamic range image (IM HDR) having a first maximum pixel luminance (PB CI) with a second image (Im LWRDR) having a lower dynamic range and a corresponding lower second maximum pixel luminance (PB C2), the second image being functionally encoded as a luminance mapping function (400) for a decoder to apply to pixel luminances (Y PQ) of the high dynamic range image to obtain corresponding pixel luminances (PO) of the second image, the encoder comprising a data formatter (304) configured to output the high dynamic range image and metadata (MET) encoding the luminance mapping function (400) to a video communication medium (399), the functional encoding of the second image being further based on a color lookup table (CL(Y PQ)) encoding a multiplier constant (B) for all possible values of pixel luminances of the high dynamic range image, and the formatter being configured to output this color lookup table in the metadata, characterized in that the high dynamic range video encoding circuit comprises a gain determination circuit (302) configured to determine luminance gain values (G PQ) quantifying a ratio of output image luminances equal to a luminance position of a correct normalized luminance position divided by output luminances of luminances of pixels of the high dynamic range image, wherein the high dynamic range video encoding circuit comprises a color lookup table determination circuit (303) configured to determine the color lookup table (CL(Y PQ)) based on values of the luminance gain values for various luminances of pixels present in the high dynamic range image. Similarly, we teach how to embody the same principles in a SLHDR type of video decoder.

The invention relates to the technical field of image coding, in particular to an image compression coding method for adaptively eliminating redundancy, which comprises the following steps of: constructing four grids by taking a certain pixel point in an image frame as a base point and taking the base point as a center, and respectively setting different priorities for non-base points in the four grids; assigning different base point real values according to the priorities of the non-base points and the relationship between the pixel values of the non-base points and the pixel values of the base points; constructing a second matrix; taking a pixel point of a fourth real number in the second matrix as a reference point, taking the reference point as a center to construct four grids, and assigning different reference point real values according to the relationship between the maximum pixel value of the pixel point of the fourth real number and the maximum pixel values of non-reference points with different priorities; extracting position pixel values of different real numbers and fourth real numbers; performing color coding on the pixel value to obtain a color coding sequence number value and a color coding value; and carrying out difference calculation on the color coding sequence number value and the color coding value. The problem that the image compression rate needs to be further improved is solved.

The invention belongs to the technical field of image processing, and particularly relates to an infrared image scene distinguishing method, electronic equipment and a medium, and the method comprises the steps: obtaining a to-be-processed infrared image, and carrying out the histogram statistics of the infrared image, and obtaining a histogram; obtaining the maximum pixel value of the histogram; calculating a first gray difference and a second gray difference according to the histogram; distinguishing the image scene according to the first gray difference, the second gray difference and the maximum pixel value; according to the infrared image scene distinguishing method, scene classification can be carried out on the whole infrared image, the to-be-processed infrared image is acquired, histogram statistics is carried out, the first gray difference, the second gray difference and the maximum pixel value are calculated, and then image scenes are distinguished according to definition conditions.