JPH02105152A - Cutout mask creation system - Google Patents

Abstract

(57) [Abstract] This bulletin contains application data before electronic filing, so abstract data is not recorded.

Description

[Detailed description of the invention] [Industrial application field]

The present invention relates to a cutout mask creation system for extracting a predetermined part, for example, a background part, from an input image to create a printing plate film, etc., and in particular, to detect edge points from a color image to extract a characteristic background part. This invention relates to an improvement of a cutout mask creation system for creating a cutout printing plate film.

[Conventional technique tif]

When cutting out and using only the necessary parts from a photographic manuscript, for example, trimming a square photographic manuscript into a heart shape, or extracting only the object from the photographic manuscript,
When a photograph is to be used in combination with other photographic originals, clipping processing is generally performed. In other words, you can use a tracing machine to trace the outline of the required image on layout paper and designate a cutout, or cover the photo original with tracing paper, draw the outline of the necessary part, and then cross out unnecessary parts with diagonal lines. to specify the crop,
According to such clipping specifications, a clipping mask or the like is created and composition is executed. However, printed matter does not only consist of a single color area as mentioned above, but also includes gradual color changes, negative and positive films for color photographs, etc. When creating a film version by extracting only a specific object, such as a person or furniture, the area to be cut out overlaps with areas with different brightness, saturation, and hue, so the above method cannot produce a faithful image. Film version cannot be created automatically. Therefore, in the case of a color film original, the operator creates a mask corresponding to the cutout area by projecting the film image, and then overlaps the mask with the film original to create the desired film version of the color original ( Y-
Due to the complexity of the image of the film original, the efficiency of film plate creation is significantly reduced and the number of days required for the printing process is significantly increased. There were problems such as. In order to solve these problems, the film image is converted into an image signal and projected onto a display so that the operator can
A method is known in which a cutting line (for example, a cutting vector) is generated by accurately specifying the outline of an image to be cut out using a pointing tool such as a mouse or a cursor. However, there is a problem in that it takes a very long time for the operator to accurately specify the outline of the image to be cropped using a cursor or the like, and it places a heavy burden on the operator. In order to solve these problems, we have developed a method that automatically extracts the cutout line by specifying the color elements that make up the background in the image with a cursor, and A method has been considered in which the operator specifies a part (for example, the upper left corner) in advance, finds the density change axis of the background part, and automatically extracts the cutout line by density classification that takes into account the density change axis. There is. However, since the same color elements as the background part are extracted, if the substance part also has similar color elements, the substance part may be removed, or the shadow of the substance part may fall on the background part, causing the outline to be distorted. This method has a problem in that it is not possible to accurately extract the contour when the contour is not clear.

[Problem to be achieved by the invention]

The present invention was made in order to solve the above-mentioned conventional problems, and it is possible to quickly create a cutout line, and also,
The purpose of the present invention is to provide a cutout mask creation system capable of extracting contour lines more accurately than in a fully automatic system.・

[Means to achieve the task]

The present invention provides a cutting mask creation system for extracting a predetermined portion from an input image, and provides an area specifying means for an operator to roughly specify a partial area including the outline of the image, and a region specifying means for specifying the partial area including the outline of the image. The above-mentioned problem has been achieved by providing a means for automatically cutting out a partial area that has been cut out.

[Effect]

In the present invention, if automatic cropping is attempted for the entire target image, the calculation area will be enormous, the processing time will be extremely long, and the cropping performance will be unstable. However, if the area is limited to a certain extent, This was done with the focus on the fact that automatic cutting is easy and accurate, and that it does not place much of a burden on the operator as long as the outline is only roughly specified. That is, the operator roughly specifies a partial area that includes the outline of the image, and automatic cropping is performed for the partial area specified by the operator. For example, when there is an image @2 as shown in FIG. 1, the operator first specifies the background part of the image in preparation for automatic cropping, and trains its density distribution, for example. Based on this,
The operator roughly specifies the vicinity of the outline of the target image using a pointing tool such as a brush, and then the device determines the density for the band-shaped area A specified by the operator, taking into account the density change axis, for example. Perform automatic cropping by classification,
When applying a mask, when the color of the background part changes, you can specify it again, or if the color element distribution of the substance part is smaller than that of the background part, conversely specify the substance part, You can also put a reverse mask on it. Therefore, it is possible to create a cutout mask more quickly and easily than when an operator manually specifies the contour line accurately as in the past, and moreover, it is possible to create a cutout mask more quickly and accurately than when fully automatic cropping is used. It becomes possible to extract lines. Here, automatic cropping can be performed by, for example, extracting a cropping line by specifying the color elements that make up the background in the image with a cursor, or It is possible to adopt a method in which the operator specifies the density change axis, finds the density change axis, and extracts the cutout line by density classification taking the density change axis into consideration.

【Example】

Embodiments of the present invention will be described in detail below with reference to the drawings. This embodiment is constructed as shown in FIG. In FIG. 2, reference numeral 1 denotes a layout table, on which a color original 2 (with a uniform background) such as a color film, for example, is placed. A color scanner 3 optically reads the image of the color original 2, converts the RGB signals into Y, M, C, and Bk signals and stores them in the image memory 4. Reference numeral 5 denotes an image processing controller section composed of a computer or the like, which includes a principal component calculation means 5a, a density average/covariance calculation means 5b, ! It is composed of a separate function calculation means 5c, a foreground part extraction means 5d, a smoothing processing means 5e, a vector conversion means 5f, a vector data correction means 5g, a noise removal means 51, etc., and the clipping processing program stored in the program memory 10 Start based on. The principal component calculation means 5a calculates a training area 11a within a uniform background portion of a cutout area designated by the operator;
For example, by sampling the image data (C, M-Y) at the upper left corner of the cutout area 11 (see FIG. 4) and calculating its principal components, as shown in FIG. shaft)
seek. With this principal component calculating means 5a, changes in density distribution can be predicted even when the background part of the image data has a gradation or when a shadow of a real part is captured. Of course, it is also possible to set the training area at another location within the background. The density average/covariance calculation means 5b calculates the density average value and covariance value of the training area lla from the second and third principal component values of each pixel obtained by the principal component analysis in order to perform density clustering by the maximum likelihood method. Find the variance value. The discrimination interval calculation means 5C substitutes the density average value and covariance value calculated by the density average/covariance calculation means 5b into a normal distribution function, and divides the entire target image into a background part and a substance part (a part that is not a background part). ) is determined, and then the color image data stored in the image memory 4 is read out and substituted into the discrimination function to calculate density distance data with respect to the training area. here,
The reason why a normal distribution function is used is because it is assumed that if an image is uniform, it follows a normal distribution. The foreground portion extracting means 5d refers to the density distance data calculated by the discriminant function calculating means 5C and pre-stored determination distance data (for example, Mahalanobis distance data), and determines whether the density distance data is within a predetermined value. is determined to be the background part, and the background part image data is extracted from the color image data stored in the image memory 4. The smoothing processing means 5e includes the foreground portion extraction means 5.
The background image data extracted in step d is smoothed as described below. The vector conversion means 5f is the smoothing processing means 5.
The background part image data (binary data) smoothed by e is converted into background part vector data. This is because the processing up to this point is raster processing, and the positional relationship seen from the entire image cannot be grasped. This vectorization makes it possible to eliminate cutout lines and the like that occur in non-background areas. The vector data correction means 59 includes the vector conversion means 5
The background vector data vectorized by f is thinned out and corrected to remove jagging that occurs due to quantization on diagonal lines at the time of vectorization. The noise removal means 5h is the vector data correction means 5g.
Regarding the background part vector data thinned out and corrected by , the background part elements extracted into the real part, that is, the noise, are removed according to knowledge information including the complexity of the shape of the vector loose and the inclusion relationship, as described later. In FIG. 2, 6 is a display for the operator to designate the automatic cutting area, check and modify the automatically extracted cutting lines, etc., and 7 is an input section for moving the keyboard 7a and the cursor on the display 6. The pointing device 7b inputs commands, position data, etc. necessary for the clipping process to the image processing controller section 5. 8 is a layout scanner serving as a film output means;
While referring to the background vector data outputted from the noise removing means 5h, the color image data other than the background is read out from the color image data stored in the image memory 4 to the plate color side, and is applied to the printing film plate 9. Cut out and output. FIG. 4 is a state transition diagram explaining the automatic clipping process, and the same parts as in FIG. 2 are given the same reference numerals. In FIG. 4, reference numeral 11 indicates a clipping area, lla indicates the training area, and 12 indicates background extracted image data, which corresponds to the background extracted image data extracted by the background extracting means 5d shown in FIG. In the corresponding 0 diagram, Nl
, N2 are noise components, which correspond to data extracted as non-uniform density regions generated by image processing calculations. 13 is smoothing data, smoothing processing means 5
e corresponds to noise components Nl and N2, for example, obtained by removing adjacent pixel data using predetermined matrix data. As a result, background contour data 13a is generated. Reference numeral 14 denotes background vector data, which corresponds to the vector data of the background contour data 13a. Reference numeral 15 indicates thinning-corrected background vector data, which is obtained by thinning-correcting the background vector data 14 by the vector data correction means 5g. Reference numerals 15a and 15b are noise vector data, which are automatically erased by the noise removing means 5h using knowledge information. 16 is background vector data, and this background vector data 16 is input to the layout scanner 8. 17 is an automatically cut out image which is exposed to the printing film plate 9 shown in FIG. When inputting the color original 2 is instructed by the pointing device 7b, the color scanner 3 starts reading the color original 2, and stores the read color image data in the image memory for each plate color (yellow, magenta, cyan, black). When this writing is completed, the color image data read from the image memory 4 is displayed on the display 6. The training area lla is specified by the operator using the pointing device 7b near the outline of the background portion of the cutout area 11. Please note that this training area
a can be changed using the pointing device 7b. The principal component calculation means 5a operates in this training area lla.
Sample the image data (C, M, Y) of the part,
As shown in Fig. 3, this image data is subjected to principal component analysis to determine the direction of change in density.
Changes in density distribution can be predicted even when the shadow of the real body part is captured. When extracting the background part, the second and third principal component values of each pixel obtained by this principal component analysis are passed to the maximum likelihood method. In the maximum likelihood method, first, the concentration average/covariance calculation means 5b
is the second of each pixel in the training area lla,
The concentration average value and covariance value are calculated from the third principal component value. Next, the discriminant number calculation means 5C substitutes the density average value and covariance value of the training area lla into a normal distribution function, and determines a discriminant function for clustering the automatically cut out region into a background part and a real part. Furthermore, the discrimination interval calculation means 5C substitutes each pixel into this discrimination function and calculates density distance data with respect to the training area. Next, the background extracting means 5d extracts background extracted image data 12 by determining whether the calculated density distance data is smaller than pre-stored determination distance data. Since the maximum likelihood method statistically processes the --like parts of the image in this way, the background part can be extracted stably. The background contour portion of the extracted background image data 12 is smoothed by the smoothing processing means 5e, and smoothed data 13 is generated. Next, the background contour data 13a, which is background image data, is converted into background vector data 14 by the vector conversion means 5f. Next, the vector data correction means 5Q performs a thinning process to reduce the number of change points in the background vector data 14 to correct the vector shape. Then, the noise removing means 5h performs noise vector data 15a.
, 15b using knowledge information. Next, the operator confirms and corrects the cutout line automatically extracted in this process on the display 6, and the cutout line is determined. The determined background vector data 16 (cutting line) is output to the layout scanner 8, and the layout scanner 8, while referring to the background vector data 16, identifies the background in the color image data stored in the image memory 4. Color image data corresponding to outside world / cutout picture @
17) is read out, cut out onto the plate side of the printing film plate 9, and outputted by exposure. Next, with reference to FIGS. 5 to 8, smoothing processing based on expansion and contraction by the smoothing processing means 5e shown in FIG. 3 will be described. FIG. 5 is a schematic diagram illustrating the swelling of contour pixels by the smoothing processing means 5e shown in FIG.
8 neighboring pixels adjacent to are searched and r of the target pixel 21a is
62 to determine “l” (black pixel) and “O” (white pixel)
2 is an expansion processing pattern, in which when the 8 neighboring pixels of the judgment pixel pattern 21 are one or "1", the target pixel 21a is "1".
1'' (indicated by diagonal lines), which corresponds to the expanded state. FIG. 6 is a schematic diagram illustrating the contraction of contour pixels by the smoothing processing means 5e shown in FIG.
8 neighboring pixels adjacent to the target pixel 31a are searched, and the target pixel 31a is
1” Determine “0”. 32 is a contraction processing pattern,
If even one of the eight neighboring pixels of the determination pixel pattern 31 is "01", the target pixel 31a is formed as "0", corresponding to a contracted state. For example, when line drawing image data 41a in which the outline of the line drawing includes a convex notch as shown in FIG. 7 is retrieved, the judgment pixel pattern 31 shown in FIG. To perform a contraction scan to generate contraction image data 41b, and to perform expansion processing on this contraction image data 41b, based on the smoothing processing program, after the smoothing processing means 5e is set to the determination pixel shown in FIG. Expansion scanning is performed using pattern 21 to generate notch-removed image data 41C. On the other hand, when line drawing image data 42a in which the outline of the line drawing includes a concave notch as shown in FIG. 8 is retrieved, the judgment pixel pattern 21 shown in FIG. In order to perform an expansion scan to generate expanded image data 42b, and to apply a contraction process to this expanded image data 42b, based on the smoothing processing program, the smoothing processing means rear 5e is set to the judgment pixel shown in FIG. Contraction scanning is performed using the pattern 31 to generate notch removed image data 42C. In addition,
In this embodiment, this processing is performed on the entire image in the order of dilation and then rci compression. FIG. 9 is a schematic diagram illustrating the vector data correction process performed by the vector data correction means 5g shown in FIG. corresponds to 4
Reference numeral 6 indicates corrected vector data, which corresponds to data in which the number of vector points has been reduced so that the level difference between the respective original vector data 45 does not occur. As can be seen from FIG. 9, the ring vector data 45 is automatically corrected to the correction vector data 46 by searching for key points of change on the binary thin line using the direction code. This reduces the roughness of the contour edges. Next, the noise correction vector data is removed loop by loop from the correction vector data 46 by the noise removal means 5h. The following knowledge information ■ to ■ for noise removal is stored in an internal memory (not shown), and noise can be removed by searching vector data according to the noise removal rules (1) to (4) below. . ■Loop length ■Loop inclusion relationship ■Loop complexity (number of critical points/vector loop length) (1) A vector loop whose vector loop length is less than a certain value and whose complexity is more than a certain value is Remove. (2) A vector loop whose vector loop length is a certain value or more, whose inclusion relationship is the outermost one, and whose complexity is less than a certain certain value remains. (3) Vector loops that are located inside due to inclusion relationships and whose complexity exceeds a certain value are removed. (4) A vector loop that is located inside due to the inclusion relationship, has a vector loop length of a certain value or more, and has a complexity of a certain certain value or less is left as a cutout line. Using such knowledge information and noise removal rules, highly accurate background vector data for cropped images is generated.
The layout scanner 8 accesses the image memory 4 while referring to this background part vector data, extracts only the color image data outside the background part, and extracts the color image data from the printing film plate 9.
A print version of the cutout image is automatically exposed and output. Next, with reference to FIG. 10, the procedure for the cutting process according to the present invention will be explained. 7. In the cutting process according to the present invention, the pointing device 7b first instructs image reading for the color original 2, and in step 110, the first color side waits for the color scanner 3 to finish reading the image for the color original 2. When the reading is completed, the process proceeds to step 112, and the read color image data is registered in the image memory 4 on the plate color side. Then, in step 114, the pointing device 7
Waiting for input of a drawing command for the registered color image from b, and when the drawing command is input, step 116 is followed by step 116 of drawing the color image data read from the image memory 4 on the display 6. At 117, the operator specifies the background portion of the image as a training area. Next, in step 118, the principal component calculation means 5a samples the training area 11a designated by the operator, and performs principal component analysis on the image data in the training area in order to find out the direction of density change. Principal component analysis is performed, for example, by creating a covariance matrix, calculating eigenvalues of the covariance matrix, and calculating each principal component value based on the eigenvalues. Next, in step 119, while looking at the display 6,
An operator roughly specifies an automatic cropping area, which is a partial area that includes the outline of the image, using a brush or the like. Next, the process proceeds to step 120, where the concentration average/covariance calculation means 5b calculates the concentration average value and covariance value of the data of the training area lla on which the principal component calculation has been performed, and the discriminant function calculation means 5C: The color image data of each pixel read from the image memory 4 and the density average value and covariance value are substituted into a normal distribution function serving as a discrimination function to calculate density distance data. Next, the process proceeds to step 122, where the background portion extracting means 5d refers to the pre-stored judgment distance data, and performs step 1.
In step 24, if the density distance data becomes a background part, the process proceeds to step 126, and the color image data which becomes a background part is registered; on the other hand, if the density distance data does not become a background part, step 1
Proceed to step 28. In step 128, it is determined whether or not all pixel determination processing has been completed, and if the determination result is negative, step 120
Returning to step 130, if the determination result is positive, the process proceeds to step 130. In steps 130 and 132, the smoothing processing means 5
e performs expansion/contraction processing on the contour shape of the color image data to perform smoothing correction. Next, in step 134, the vector conversion means 5f converts the smoothed background image data (raster data) into vector data. Next, in step 136, the vector data correction means 5g performs thinning correction of the vector data, and in step 138
Then, the noise removal means 5h removes noise from the vector data. Next, in step 140, the operator confirms and corrects the automatic cutting line displayed on the display 6, and finally determines the cutting line. Next, in step 142, the l/iaud scanner 8 waits for the color image data for each plate color stored in the image memory 4 to be read out while referring to the corrected vector data, and in step 144, the color image data for each plate color stored in the image memory 4 is read out. Print the color image (cutout image) outside the background area on film plate 9
Exposure output is performed for each plate color. Next, in step 146, it is determined whether all the films have been outputted. If the determination result is negative, the process returns to step 142, and if the determination result is positive, the process is terminated. In the above embodiment, the case where the color original 2 is inputted from the color scanner 3 was explained, but even if the inputted color document 2 is inputted from the input drum (not shown) of the layout scanner 8,
It is clear that similar processing can be performed. Furthermore, in the above embodiment, automatic clipping was performed by density classification taking into account the density change axis, so even if the brightness or hue changes, it is possible to effectively extract background areas with gradations and shadows. However, the automatic clipping method is not limited to this.

[Brief explanation of the drawing]

FIG. 1 is a diagram showing an example of an image to be processed by the cutout mask creation system according to the present invention, FIG. 2 is a block diagram showing the configuration of an embodiment of the cutout mask creation system according to the present invention, and FIG. FIG. 4 is a diagram showing a two-dimensional histogram of the training area and principal component axes to explain the automatic clipping method; FIG. 4 is a state transition diagram explaining the automatic clipping process in the embodiment; FIG. , FIG. 6 is a schematic diagram illustrating expansion of contour pixels by the smoothing processing means of the embodiment shown in FIG. 2, and FIG. 6 is a schematic diagram illustrating contraction of contour pixels. FIG. 9 is a schematic diagram illustrating the state of smoothing processing; FIG. 9 is a schematic diagram illustrating vector data correction processing by the vector data correction means used in the embodiment; FIG. 10 is a schematic diagram illustrating the clipping processing procedure in the embodiment. FIG. 2... Color document, A... Automatic clipping area, 3... Color scanner, 4... Image memory, 5... Image processing controller section, 5a... Principal component calculation means, 5b...・Concentration average/covariance calculation means, 5C: Discrimination function calculation means, 5d: Background portion extraction means, 6: Display, 9: Printing film plate, 10: Program memory, 11. ...Cutout area, 11a...Training area 1. 12...Background part extracted image data.

Claims (1)

[Claims] (1) In a cutting mask creation system for extracting a predetermined portion from an input image, a region specifying means for an operator to roughly specify a partial region including the outline of the image, and a region specifying means for roughly specifying a partial region including the outline of the image; A cutout mask creation system comprising: a means for automatically cutting out a partial area; and a cutout mask creation system.