JP2014194659A - Image processing apparatus and computer program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To reduce image processing time for determination on an object in an image, in executing multiple kinds of analyses.SOLUTION: An image processing apparatus includes: a processing time determination unit which determines processing time for first analysis and processing time for second analysis of multiple analyses to be executed on an object in an image; an analysis processing unit which executes the first analysis prior to the second analysis when the processing time for the first analysis is determined to be less than the processing time for the second analysis, and executes the second analysis prior to the first analysis when the processing time for the first analysis is determined to be equal to or more than the processing time for the second analysis; and an attribute determination unit which determines an attribute of the object on the basis of a result of the processing executed.

Description

  The present invention relates to an image processing technique, and more particularly to an image processing technique for executing a determination regarding an object in an image to be processed.

  Japanese Patent Application Laid-Open No. 2005-228561 discloses a technique for executing a plurality of types of analysis processing using a plurality of types of algorithms on image data and extracting a contour line in the image. In this technique, the image processing apparatus determines a contour line to be finally extracted using processing results of a plurality of types of analysis processing.

JP-A-7-92651

  However, when a plurality of types of analysis processes are used as in the above technique, the processing time for extracting the contour line may be excessive. Such a problem is not limited to the extraction of the contour line, but is a problem common to the determination regarding the object in the image by the analysis processing on the image data.

  An object of the present invention is to reduce the processing time of image processing for performing determination related to an object in an image when a plurality of types of analysis processing are executed.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following application examples.

Application Example 1 An image processing apparatus, which is a first analysis process among a plurality of analysis processes to be performed on a specifying unit that specifies an object in an image represented by image data and the object A processing time determination unit that determines the processing time of the second analysis process, and the processing time of the first analysis process is determined to be less than the processing time of the second analysis process. In the first case, the first analysis process is executed prior to the second analysis process, and the processing time of the first analysis process is equal to or longer than the processing time of the second analysis process. An analysis processing unit that executes the second analysis process prior to the first analysis process in the second case, wherein the first case is the first case, When the processing result of the analysis process is the first result, the second analysis process is performed. When the processing result of the first analysis process is different from the first result, the second analysis process is not executed, and the second analysis process is performed. If the processing result is the second result, the first analysis processing is executed. If the processing result of the second analysis processing is different from the second result, the first analysis processing is performed. An image processing apparatus comprising: the analysis processing unit that does not execute the process; and an attribute determination unit that determines an attribute of the object based on a processing result of the executed process among the plurality of analysis processes.

  According to the above configuration, the process determined to have a short processing time is executed in advance among the first analysis process and the second analysis process. Then, when the first analysis process is executed in advance and the processing result of the first analysis process is different from the first result, the second analysis process is not executed. In addition, when the second analysis process is executed in advance and the processing result of the second analysis process is different from the second result, the first analysis process is not executed. As a result, there is a high possibility that the attribute of the object can be determined without executing a process having a long processing time out of the first analysis process and the second analysis process. Therefore, it is possible to reduce the processing time of the analysis process for determining the attribute of the object.

Application Example 2 In the image processing apparatus according to Application Example 1, the processing time determination unit uses a first value corresponding to the number of pixels that are the processing target of the first analysis process, The processing time of the first analysis processing is determined, and the processing time of the second analysis processing is determined using a second value corresponding to the number of pixels that are the processing target of the second analysis processing. , Image processing device.

  According to this configuration, the processing time of the first analysis processing and the processing time of the second analysis processing can be appropriately determined according to the number of pixels to be processed.

Application Example 3 In the image processing apparatus according to Application Example 2, the first analysis process includes a process for a plurality of pixels in a specific region including the object, and the first value is It is a value according to the number of pixels in the specific area, and the second analysis process includes a process for a plurality of pixels constituting the object, and the second value is a pixel constituting the object. An image processing apparatus having a value corresponding to the number of the images.

  According to this configuration, the processing time of the first analysis processing and the processing time of the second analysis processing can be appropriately determined according to the processing contents of the first analysis processing and the second analysis processing.

Application Example 4 In the image processing apparatus according to any one of Application Examples 1 to 3, the first analysis process is a first unit process that is repeated for each of one or more pixels to be processed. The second analysis process includes a second unit process that is repeated for each of one or more pixels to be processed, and the processing time determination unit responds to a processing load of the first unit process. Image processing that uses the value to determine the processing time of the first analysis processing, and that uses the value according to the processing load of the second unit processing to determine the processing time of the second analysis processing apparatus.

  According to this configuration, it is possible to appropriately determine the processing time of the first analysis process and the processing time of the second analysis process in accordance with the processing load of the unit process.

Application Example 5 In the image processing apparatus according to any one of Application Example 1 to Application Example 4, the attribute determination unit is configured based on a processing result of an executed process among the plurality of analysis processes. An image processing apparatus that determines whether or not the object is a surrounding line surrounding a partial area in an image.

  According to this configuration, it is possible to reduce the processing time of the analysis processing for determining whether or not the object is a surrounding line surrounding a partial area in the image.

Application Example 6 In the image processing device according to Application Example 5, the first analysis process is a process of analyzing whether or not the object is a line, and the first analysis process includes the first analysis process. The first result is that the first condition indicating that the object is a line is satisfied, and the attribute determination unit determines that the result of the first analysis process does not satisfy the first condition. Is an image processing apparatus that determines that the object is not the surrounding line regardless of the second analysis processing.

  According to this configuration, when the first analysis process is executed prior to the second analysis process, and the result of the first analysis process does not satisfy the first condition, the second analysis process is performed. It is possible to determine that the object is not a surrounding line without performing the analysis process. As a result, it is possible to reduce the processing time of the analysis processing for determining whether or not the object is a surrounding line.

Application Example 7 In the image processing apparatus according to Application Example 6, in the first analysis process, the pixels constituting the object are arranged on a plurality of straight lines extending in the first direction in the specific area. The image processing apparatus includes a process of analyzing a distribution, and the first condition includes that a number of pixels constituting the object that are continuous in the first direction is equal to or less than a reference.

  According to this configuration, whether or not the object is a line is analyzed based on the distribution of the pixels constituting the object on a plurality of straight lines extending in the first direction. As a result, it can be appropriately determined whether or not the object is a line.

Application Example 8 In the image processing apparatus according to Application Example 7, in the first analysis process, a plurality of straight lines extending in a second direction intersecting the first direction in the specific region is further included. The first condition is that the number of pixels constituting the object continuing in the first direction is not more than a reference, and An image processing apparatus comprising: a number of pixels constituting the object continuing in the second direction being equal to or less than a reference.

  According to this configuration, whether or not the object is a line based on the distribution of the pixels constituting the object on the plurality of straight lines extending in the first direction and on the plurality of straight lines extending in the second direction. Is analyzed. As a result, it can be appropriately determined whether or not the object is a line.

Application Example 9 In the image processing device according to any one of Application Examples 5 to 7, the second analysis process determines whether or not the object forms a shape surrounding a partial region in the image. And the second result of the second analysis process is to satisfy a second condition indicating that the object forms a shape surrounding a partial region in the image, The attribute determination unit determines that the object is not the surrounding line regardless of the first analysis process when the result of the second analysis process does not satisfy the second condition. Image processing device.

  According to this configuration, when the second analysis process is executed prior to the first analysis process, and the result of the second analysis process does not satisfy the second condition, the second analysis process is performed. It is possible to determine that the object is not a surrounding line without performing the analysis process. As a result, it is possible to reduce the processing time of the analysis processing for determining whether or not the object is a surrounding line.

Application Example 10 In the image processing apparatus according to Application Example 9, in the second analysis process, a plurality of frame areas corresponding to each of a plurality of specific pixels constituting the object are set. The frame region is a frame-shaped region in which the corresponding specific pixel is included in any position between the frame region and the inside of the frame region; 1 is a first determination process for determining the number of object partial areas included in the frame area for each of the frame areas, wherein the object partial area is continuous with one or more specific pixels constituting the object. A first determination process, and a second determination process for determining whether a ratio of the first specific pixel to a plurality of the specific pixels constituting the object is equal to or higher than a reference. The first specific pixel is the specific pixel in which the number of the object partial areas included in the corresponding frame area is two of the plurality of specific pixels constituting the object. And a second determination process, wherein the second condition includes a ratio of the first specific pixel being equal to or higher than a reference.

  According to this configuration, it is analyzed whether or not the object surrounds a part of the region in the image using the frame region set for each of the plurality of specific pixels constituting the object. As a result, it is possible to appropriately determine whether or not the object surrounds a partial area in the image.

Application Example 11 In the image processing device according to Application Example 9, the second analysis process further includes a third determination process for determining whether or not a condition based on a second specific pixel is satisfied. In the second specific pixel, of the plurality of specific pixels constituting the object, the number of the object partial regions included in the corresponding frame region is M (M is 1, 3). Image processing, including the third determination process that is the specific pixel that is an integer of any one of 4, 5 and 5, wherein the second condition includes a condition based on the second specific pixel apparatus.

  According to the above configuration, the condition based on the second specific pixel is used to analyze whether or not the object surrounds a partial region in the image. As a result, it is possible to appropriately determine whether or not the object surrounds a partial area in the image.

  The present invention can be realized in various forms, such as an image processing method, a computer program for realizing the functions or methods of these apparatuses, a recording medium on which the computer program is recorded, and the like. It can be realized in the form.

It is a block diagram which shows the structure of the image processing system in 1st Example. 5 is a flowchart showing the operation of the image processing system 1000. It is a figure which shows an example of a manuscript and an image. It is a figure explaining a contraction process and an expansion process. It is a flowchart of an object determination process. It is explanatory drawing of a line determination process. It is a flowchart of a shape determination process. It is the 1st explanatory view of shape judging processing. It is the 2nd explanatory view of shape judging processing. It is a figure explaining determination using specific field ES. It is a flowchart of a process order determination process. It is a figure which shows an example of the partial image PSI, and an example of the partial binary image PBI. It is a flowchart of an inclusion relation determination process.

A. First embodiment:
A-1: Configuration of Image Processing System 1000 FIG. 1 is a block diagram showing the configuration of the image processing system in the first embodiment. The image processing system 1000 includes a server 400 as an image processing apparatus and a multifunction device 200. The server 400 is connected to the Internet 70, and the multi-function device 200 is connected to the Internet 70 via a LAN (Local Area Network) 50. As a result, the server 400 and the multifunction device 200 can communicate with each other via the LAN 50 and the Internet 70. Further, the personal computer 500 of the user of the multifunction device 200 may be connected to the LAN 50.

  The server 400 includes a CPU 410, a volatile storage device 420 such as a DRAM, a nonvolatile storage device 430 such as a hard disk drive or a flash memory, and a communication unit 480 including an interface for connecting to a network such as the Internet 70. I have. The volatile storage device 420 is provided with a buffer area 421 for temporarily storing various intermediate data generated when the CPU 410 performs processing. The nonvolatile storage device 430 stores a computer program 431 that causes the CPU 410 to perform image processing to be described later. The computer program 431 can be provided in a form recorded on a DVD-ROM or the like.

  The CPU 410 functions as the image processing unit 300 by executing the computer program 431. The image processing unit 300 includes an image data acquisition unit 310 that acquires image data to be processed (also referred to as target image data), an object specification unit 320 that specifies an object in the target image represented by the target image data, and a processing It includes a time determination unit 330, an analysis processing unit 350, an attribute determination unit 360, and a removal processing unit 370 that executes a process of removing an object surrounded by a surrounding line. The analysis processing unit 350 can execute a plurality of analysis processes on the object. Specifically, the analysis processing unit 350 includes a color determination unit 351 that determines whether or not the object is a single color, a line determination unit 352 that determines whether or not the object is a line, A shape determination unit 353 that determines whether or not the shape has an enclosing shape. The processing time determination unit 330 determines the processing time of a specific process among a plurality of analysis processes to be executed on the object. The attribute determination unit 360 determines the attribute of the object, specifically, whether or not the object is a surrounding line, based on the processing result of the executed process among the plurality of analysis processes. Here, as described later, the encircling line is a line enclosing a partial area in the target image, for example, an area indicating another object or a background area not including the object. For example, the surrounding line is formed on an original with a red pen or the like, and appears on an image represented by image data (scan data) obtained by reading the original. Specific image processing performed by each of these functional units will be described later.

  The MFP 200 includes a CPU 210, a volatile storage device 220 such as a DRAM, a nonvolatile storage device 230 such as a flash memory and a hard disk drive, a printer unit 240, a scanner unit 250, and an operation unit 260 such as a touch panel and buttons. And a display unit 270 such as a liquid crystal display, and a communication unit 280 including an interface for connecting to a network such as the LAN 50.

  The volatile storage device 220 is provided with a buffer area 221 for temporarily storing various data generated when the CPU 210 performs processing. A control program 231 is stored in the non-volatile storage device 230.

  The printer unit 240 executes printing using a printing method such as an inkjet method or a laser method. The scanner unit 250 obtains scan data by reading a document using a photoelectric conversion element (for example, CCD, CMOS). The scan data is bitmap data (RGB image data) composed of RGB pixel data. The RGB pixel data is pixel data including color component values of three color components of red (R), green (G), and blue (B) (each color component value in this embodiment is a gradation value of 256 gradations). It is. Hereinafter, among the color components, the red component is also referred to as R component, the green component is also referred to as G component, and the blue component is also referred to as B component.

  The CPU 210 functions as the communication control unit 110 and the device control unit 120 by executing the control program 231. For example, the apparatus control unit 120 controls the printer unit 240 and the scanner unit 250 to execute copy processing, printing processing, scanning processing, and the like. The communication control unit 110 is a functional unit that controls communication with an external device. Specifically, the communication control unit 110 includes an image data transmission / reception unit 115 that transmits / receives image data to / from the server 400 in image processing to be described later. ing.

A-1: Operation of Image Processing System 1000 The image processing unit 300 of the server 400 performs image processing on image data to be processed (also referred to as target image data), and generates processed image data. The operation of the image processing system 1000 including this image processing will be described.

  FIG. 2 is a flowchart showing the operation of the image processing system 1000. The processing of this flowchart is executed when the multifunction device 200 receives a specific operation mode designation and an instruction to read the document SC from the user. This specific operation mode is a mode for generating image data representing an image obtained by removing a specific object surrounded by a surrounding line from the document SC, and is also referred to as a specific object removal mode.

  When the processing is started, in step S105, the apparatus control unit 120 (FIG. 1) of the multifunction device 200 reads the document SC using the scanner unit 250, thereby generating scan data.

  FIG. 3 is a diagram illustrating an example of a document and an image. FIG. 3A shows an example of a scan image 60 represented by scan data. Since the scan image 60 is an image representing the document SC, FIG. 3A can also be referred to as a diagram illustrating an example of the document SC.

  The scanned image 60 (original SC) includes four characters 61 to 64, two drawings 65 and 67, one photograph 68, and a surrounding line 66 as objects. Drawing is, for example, an object representing an illustration, a table, a graph, a diagram, vector graphics, a pattern, or the like. The surrounding line 66 is an object handwritten on the document SC by the user using a pen of a color (in this embodiment, red) designated in advance as the color of the surrounding line to be used in the object removal mode. The user manually draws a surrounding line surrounding the object to be removed on the document SC. In the example of FIG. 3A, the surrounding line 66 surrounds the drawing 65. The three characters 61 to 63 are red characters, and the one character 64 is a character that is not red. The drawing 65 does not include a red portion, and the drawing 67 and the photograph 68 include a red portion.

  Subsequently, in step S <b> 110, the image data transmission / reception unit 115 of the server 400 transmits scan data to the server 400. For example, the scan data is JPEG compressed and transmitted to the server 400. As a result, the image data acquisition unit 310 of the image processing unit 300 of the server 400 acquires scan data from the multifunction device 200 as target image data.

  When the scan data is acquired, the image processing unit 300 executes a series of processes for specifying a surrounding line in the scan image 60 (steps S120 to S145).

  First, in step S120, the object specifying unit 320 executes binarization processing on the scan data to generate binary image data in order to specify an object that is a candidate for a surrounding line. Specifically, the scan data is divided into a pixel having a color value representing red, which is the color of the scan surrounding line (red object pixel), and a pixel having a color value representing a color other than red (non-red pixel). Binarized. For example, a pixel whose R component value is equal to or greater than the reference value Rth, G component value is equal to or less than the reference value Gth, and B component value is equal to or less than the reference value Bth is classified as a red object pixel. A pixel having an R component value greater than the reference value Rth, a G component value less than the reference value Gth, or a B component value less than the reference value Bth is classified as a non-red pixel. In the present embodiment, since red is assumed as the color of the surrounding line, a red object is specified in this step. However, if another color is assumed, in this step An object having a range of colors including other colors is identified.

  FIG. 3B is a diagram illustrating an example of a binary image 60A represented by binary image data. In the binary image 60A, as a red object composed of red object pixels, three characters 61A to 63A, a surrounding line 66A, a drawing 67 and a partial object which is a part of a photograph 68 (FIG. 3A). 67A and 68A. Since the character 64 and the drawing 65 (FIG. 3A) are not red, they do not appear as red objects in the binary image 60A.

  In step S125, the object specifying unit 320 performs a contraction process for contracting the red object and an expansion process for expanding the red object on the binary image data. In this embodiment, the contraction process is executed first, and the expansion process is executed on the binary image data that has been subjected to the contraction process (contracted binary image data).

  FIG. 4 is a diagram illustrating the contraction process and the expansion process. FIG. 4A shows a partial image PI1 of the binary image 60A (FIG. 3B). The partial image PI1 includes a part of an encircling line 66A as an example of a red object before the contraction process, and a noise pixel DT. The noise pixel DT is an isolated pixel. The isolated noise pixel DT is a red object pixel that appears in the binary image 60A due to noise in the scan image 60, and does not constitute a red object to be specified. Further, the surrounding line 66A may include a disconnected portion (disconnected portion) NT. For example, when the surrounding line 66 in the scan image 60 has a relatively light color part or a part having a relatively narrow line width, the broken line part is shown in the surrounding line 66A in the binary image 60A. NT may appear.

  The contraction process is executed using, for example, a filter having a predetermined size, and in the example of FIG. 4A, a filter FI1 having a size of 3 vertical pixels × 3 horizontal pixels. Specifically, the object specifying unit 320 applies the filter FI1 to the binary image data representing the binary image 60A to generate contracted binary image data. That is, the object specifying unit 320 arranges the filter FI1 on the binary image 60A so that the center position CC1 (see FIG. 4A) of the filter FI1 overlaps the target pixel. If at least one non-red pixel exists within the range of the filter FI1, the object specifying unit 320 sets a pixel in the contracted binary image corresponding to the target pixel as a non-red pixel. Then, the object specifying unit 320 corresponds to the target pixel when there is no non-red pixel within the range of the filter FI1, that is, when all the nine pixels within the range of the filter FI1 are red object pixels. A pixel in the contracted binary image is set as a red object pixel. The object specifying unit 320 sets all the pixels of the binary image 60A as the target pixel, and sets the corresponding pixel in the contracted binary image as one of the non-red pixel and the red object pixel, whereby the contracted binary image Shrinkage binary image data representing is generated. As can be understood from this description, the contraction process for contracting a red object can be said to be a process for expanding a region (mainly a background region) constituted by non-red objects.

  FIG. 4B shows a contracted partial image PI2 corresponding to the partial image PI1 in FIG. 4A among the contracted binary images. As understood from the fact that the noise pixel DT does not appear in the contracted partial image PI2, an isolated red object pixel such as the noise pixel DT can be erased by the contraction process. In the contracted partial image PI2, the red object, for example, the surrounding line 66A is thinner (contracted) than the surrounding line 66A of the partial image PI1. In the contracted partial image PI2, the disconnection portion NT of the surrounding line 66A is larger than the disconnection portion NT of the surrounding line 66A of the partial image PI1.

  The subsequent expansion process is executed using, for example, a filter having a predetermined size, and in the example of FIG. 4B, a filter FI2 having a size of 5 pixels vertically × 5 pixels horizontally. Specifically, the object specifying unit 320 applies the filter FI2 to the contracted binary image data representing the contracted binary image to generate expanded binary image data. That is, the object specifying unit 320 arranges the filter FI2 on the contracted binary image so that the center position CC2 (see FIG. 4B) of the filter FI2 overlaps the target pixel. When at least one red object pixel exists within the range of the filter FI2, the object specifying unit 320 sets a pixel in the expanded binary image corresponding to the target pixel as a red object pixel. Then, when there is no red object pixel in the range of the filter FI2, that is, the 25 pixels in the range of the filter FI2 are all non-red pixels, the object specifying unit 320 corresponds to the target pixel. Pixels in the expanded binary image are set to non-red pixels. The object specifying unit 320 sets all the pixels of the contracted binary image as the target pixel, and sets the corresponding pixel in the expanded binary image to one of the non-red pixel and the red object pixel, so that the expanded binary image Inflated binary image data is generated.

  FIG. 4C shows an expanded partial image PI3 corresponding to the partial images PI1 and PI2 in FIGS. 4A and 4B of the expanded binary image. As can be understood from the fact that the disconnection portion NT does not appear in the surrounding line 66A of the inflated partial image PI3, the disconnection portion NT that can appear in the surrounding line 66A can be connected by the expansion process. In the expanded partial image PI3, a red object, for example, a surrounding line 66A is thicker (expanded) than the surrounding line 66A of the partial image PI1.

  Note that the sizes of the filters FI1 and FI2 described above, that is, the degree of contraction by the contraction process and the degree of expansion by the expansion process are examples. However, when the contraction process is performed first and the expansion process is performed later, the degree of expansion by the expansion process (corresponding to the size of the filter FI2) is the degree of contraction by the contraction process (corresponding to the size of the filter FI1). Larger). If it carries out like this, the connection of the disconnection part NT can be implement | achieved appropriately. If the contraction process is executed first and the expansion process is executed later, if the degree of expansion by the expansion process is equal to or less than the degree of contraction by the contraction process, the disconnection portion NT expanded by the contraction process is caused by the expansion process. It may not be connected.

  In contrast to the above embodiment, when the expansion process is executed first and the contraction process is executed later, the degree of expansion by the expansion process is preferably smaller than the degree of contraction by the contraction process. In this way, the noise pixel DT can be appropriately erased. If the expansion process is executed first and the contraction process is executed later, if the degree of expansion by the expansion process is equal to or greater than the degree of contraction by the contraction process, the noise pixel DT enlarged by the expansion process is It may not be erased.

  The above-described contraction / expansion process (step S125 in FIG. 2) can realize the elimination of the noise pixel DT and the connection of the disconnection portion NT, so that the accuracy of the surrounding line determination can be improved in the object determination process to be executed later. . In addition, the processing load can be reduced by reducing unnecessary processing (for example, processing in which the noise pixel DT is an object to be determined).

  In the subsequent step S130 (FIG. 2), the object specifying unit 320 uses the binary image data after the contraction / expansion processing to specify a red object, and executes labeling for attaching an identifier to the specified red object. . As a result of the labeling, for example, label data in which a red object is associated with an identifier is generated.

  Specifically, the object specifying unit 320 specifies one area composed of one or more continuous (adjacent) red object pixels as one red object. In the example of FIG. 3B, three characters 61A to 63A, a surrounding line 66A, a drawing 67 and partial objects 67A and 68A that are part of the photograph 68 (FIG. 3A) are specified. Each of these red objects is given a different identifier.

  When the red object is specified, in step S145, the image processing unit 300 executes an object determination process. FIG. 5 is a flowchart of the object determination process. The object determination process is a process for determining, for each red object, whether or not each of the plurality of identified red objects 61A to 63A and 66A to 68A is a surrounding line. The determination of one red object is a process of at least one of the vertical line determination process (step S235), the horizontal line determination process (step S240), and the shape determination process (step S245). It is executed based on the result.

  In step S205, the analysis processing unit 350 selects a red object to be processed. In the example of FIG. 3B, one red object is selected as a processing target from among the three characters 61A to 63A, the surrounding line 66A, and the partial objects 67A and 68A that are parts of the drawing 67 and the photograph 68. Selected.

  In step S210, the color determination unit 351 of the analysis processing unit 350 executes a single color determination process for determining whether or not the red object to be processed is a single color. Specifically, the color determination unit 351 calculates an average component value (Rave1, Gave1, Bave1) of the red object. The average component values (Rave1, Gave1, Bave1) are obtained by calculating the average value of all red object pixels constituting the red object to be processed for each color component. Next, the color determination unit 351 determines the ratio (RT_R, RT_G, RT_B) of red object pixels having component values close to the average component value for each color component using the following equations (1) to (3). calculate.

RT_R = (PN_Rave1 / PNtotal) ... (1)
RT_G = (PN_Gave1 / PNtotal) ... (2)
RT_B = (PN_Bave1 / PNtotal) (3)

  Here, PNtotal is the total number of red object pixels constituting the red object to be processed. PN_Rave1, PN_Gave1, and PN_Bave1 are the number of red object pixels having component values close to the average component values (Rave1, Gave1, Bave1), and are calculated for each color component. In this embodiment, when the R component value is within the range of (Rave1−ΔV1) ≦ R ≦ (Rave1 + ΔV1), the pixel is determined to be a pixel having an R component value close to the average component value. Is done. Similarly, when the G component value is within the range of (Gave1−ΔV1) ≦ G ≦ (Gave1 + ΔV1), the pixel is determined to be a pixel having a G component value close to the average component value. When the B component value is within the range of (Bave1−ΔV1) ≦ B ≦ (Bave1 + ΔV1), the pixel is determined to be a pixel having a G component value close to the average component value. For example, ΔV1 is set to 70, for example, for a component value of 256 gradations.

  Then, the color determination unit 351, when the ratio (RT_R, RT_G, RT_B) calculated for each component is greater than or equal to the reference value TH1, that is, RT_R ≧ TH1, RT_G ≧ TH1, and RT_B ≧ TH1. In some cases, it is determined that the red object to be processed is a single color. Further, the color determination unit 351, when at least one of the ratios (RT_R, RT_G, RT_B) is less than the reference value TH1, that is, when RT_R <TH1, or RT_G <TH1, or RT_B <TH1. It is determined that the red object to be processed is not a single color. The reference value TH1 is a design value set experimentally, and is set to 0.6, for example. The encircling line may be written with a light red pen or a dark red pen, so in step S120 (FIG. 2), a relatively wide range including light red, dark red, etc. A pixel having a color inside is extracted as a red object pixel. For this reason, in this step, it is determined relatively strictly whether or not the red object is a single color.

  If it is determined by the single color determination process that the red object to be processed is not a single color (step S215: NO), the attribute determination unit 360 determines that the red object to be processed is not a surrounding line (step S265). . Since one encircling line is generally entered with a single stroke without changing the pen in the middle of entry, the encircling line is considered to be monochromatic. For example, an encircled line drawn using a dark red pen is considered to be a dark red monochromatic color, and an encircling line entered using a light red pen is considered to be a pale red monochromatic color. For this reason, in this embodiment, the single color is adopted as a necessary condition for determining that the red object is a surrounding line. As a result, it is possible to appropriately determine whether or not the red object is a surrounding line. For example, the surrounding line 66A in FIG. 3B is determined to be a single color. Further, for example, when the partial object 67A of B3 (B) includes a large number of colors including light red and dark red, it is determined that the partial object 67A is not a single color.

  When it is determined that the red object to be processed is a single color (step S215: YES), the processing time determination unit 330 executes a processing order determination process (step S220). The processing order determination process determines processing times of a line determination process (steps S235 and S240 described later) as the first analysis process and a shape determination process (step S245 described later) as the second analysis process. In this process, the processing order of the line determination process and the shape determination process is determined based on the determination result. Specifically, when it is determined that the processing time of the line determination process is less than the processing time of the shape determination process, the processing time determination unit 330 executes the line determination process prior to the shape determination process. The processing order is determined as follows. In addition, when it is determined that the processing time of the line determination process is equal to or longer than the processing time of the shape determination process, the processing time determination unit 330 executes the shape determination process prior to the line determination process. Determine the processing order. Details of the processing order determination processing will be described after the entire object determination processing (FIG. 5) including the line determination processing and the shape determination processing is described.

  In step S230, the analysis processing unit 350 determines a determination process to be executed in accordance with the processing order determined in the processing order determination process described above. For example, in the process order determination process, when it is determined to execute the line determination process prior to the shape determination process, the vertical direction line determination process (step S235) and the horizontal direction line determination process (step S240). The determination process to be executed is determined in the order of the shape determination process (step S245). Conversely, in the processing order determination process, when it is determined to execute the shape determination process prior to the line determination process, the shape determination process (step S245), the vertical line determination process (step S235), The determination process to be executed is determined in the order of the horizontal direction line determination process (step S240). In any case, in this embodiment, it is predetermined that the vertical direction line determination process is executed prior to the horizontal direction line determination process. Instead of this, the horizontal direction line determination process may be executed prior to the vertical direction line determination process.

  First, the line determination process will be described. FIG. 6 is an explanatory diagram of the line determination process. The line determination process is a process for determining whether or not the red object to be processed is a line. The line determination process is executed by the line determination unit 352, and includes a vertical line determination process (step S235) and a horizontal line determination process (step S240).

  The vertical direction line determination processing is executed using processing for analyzing the distribution of red object pixels on a plurality of straight lines extending in the vertical direction (Y direction) in a processing region including a red object. Specifically, as shown in FIG. 6A, an example will be described in which a surrounding line 66A (FIG. 3B) in the binary image 60A is a red object to be processed. The line determination unit 352 identifies the rectangular area SI1 circumscribing the surrounding line 66A as a processing area. The line determination unit 352 calculates the number of consecutive red object pixels (the number of continuous pixels) along the line L1 (p) along the Y direction (the vertical direction in FIG. 6) in the rectangular area SI1. p is a value indicating the position of the line in the X direction, for example, a value within a range of 1 ≦ p ≦ W (W is the length (number of pixels) in the X direction of the rectangular region SI1). In the example of FIG. 6A, two consecutive pixel numbers Lh1 and Lh2 are calculated. In this case, the average value ((Lh1 + Lh2) / 2) of the two continuous pixel numbers Lh1 and Lh2 is calculated as the continuous pixel number LN1 (p) corresponding to the line L1 (p). The object specifying unit 320 calculates the number of continuous pixels LN1 (p) for all W lines L1 (p) within the range of 1 ≦ p ≦ W.

  The line determination unit 352 calculates an average value LN1ave of the W consecutive pixel numbers LN1 (p) calculated for the W lines L1 (p). The line determination unit 352 determines that the red object is a line when the average continuous pixel number LN1ave is equal to or less than the reference value TH2, and the red object is determined when the average continuous pixel number LNave is less than the reference value TH2. Judge that it is not a line. That is, the determination condition of the vertical line determination process is that the average continuous pixel number LN1ave is equal to or less than the reference value TH2.

  The horizontal direction line determination process is a process for executing the same process as the vertical direction line determination process in the horizontal direction (X direction). Specifically, the line determination unit 352 calculates the number of continuous pixels of the red object pixels along the line L2 (q) along the X direction (lateral direction in FIG. 6) in the rectangular area SI1. q is a value indicating the position (Y coordinate) of the line in the Y direction of the line. For example, q is a value within the range of 1 ≦ q ≦ H (H is the length of the rectangular region SI1 in the Y direction (pixel number)). In the example of FIG. 6A, two consecutive pixel numbers Lw1 and Lw2 are calculated. In this case, the average value of the two continuous pixel numbers LW1 and LW2 is calculated as the continuous pixel number LN2 (q) corresponding to the line L2 (q). The object specifying unit 320 calculates the continuous pixel number LN2 (q) for all of the H lines L2 (q) within the range of 1 ≦ q ≦ H.

  The line determination unit 352 calculates an average value LN1ave of the H continuous pixel numbers LN1 (q) calculated for the H lines L1 (q). The line determination unit 352 determines that the red object is a line when the average continuous pixel number LN2ave is equal to or less than the reference value TH2, and the red object is determined when the average continuous pixel number LNave is less than the reference value TH2. Judge that it is not a line. That is, the determination condition of the vertical line determination process is that the average continuous pixel number LN2ave is equal to or less than the reference value TH2.

  Thus, the object width (line width LW) is specified by calculating the number of consecutive red object pixels on each of a plurality of straight lines extending in the X direction and the Y direction. Can be specified. Since one encircling line is assumed to be a single line drawn with a red pen, it is assumed that it is a line as a necessary condition for determining that a red object is an encircling line. doing. As a result, it is possible to appropriately determine whether or not the red object is a surrounding line. For example, the surrounding line 66A in FIG. 3B is determined to be a line.

  FIG. 6B illustrates an example in which the partial object 68A (FIG. 3B) in the binary image 60A is a red object to be processed. When such a red object is not a line, as shown in FIG. 6B, the continuous pixel number Lh3 for the line L1 (p) in the Y direction and the continuous pixel for the line L2 (q) in the X direction. The number Lw3 is relatively large. As a result, the partial object 68A is determined not to be a line in the line determination process.

  Next, the shape determination process will be described. The shape determination process is a process that is executed by the shape determination unit 353 and determines whether or not the red object to be processed has a surrounding shape surrounding a part of the area in the scan image 60.

  FIG. 7 is a flowchart of the shape determination process. FIG. 8 is a first explanatory diagram of the shape determination process. FIG. 9 is a second explanatory diagram of the shape determination process. In step S305, the shape determination unit 353 selects one red object pixel constituting the red object as a target pixel. More specifically, the shape determining unit 353 sequentially advances a plurality of lines Lx in the X direction from the negative direction side in the Y direction (from the upper side in FIG. 8) in the rectangular area SI1 (FIG. 8A). Scan and select red object pixels one by one in the order they are detected.

  In step S310, the shape determination unit 353 sets a frame region FL corresponding to the selected target pixel. FIG. 8B shows an example of the frame region FL set for the target pixel NP. As shown in FIG. 8B, the frame area FL is a square frame-shaped area, and the thickness of the frame is one pixel. The width (number of pixels) FW in the X direction and the Y direction of the frame region FL is expressed by the equation shown in FIG. As can be seen from this equation, the width FW of the frame region FL includes the reading resolution RS of the scanned image 60 (unit is dpi (dot per inch)), the line width LW of the red object (unit is pixel), and the red object. The size is determined in accordance with the size OBS (unit: pixels). For the line width LW, for example, the average continuous pixel number LNave calculated in the line determination process (step S220 in FIG. 5) is used. For the object size OBS, for example, an average value ((H + W) / 2) of the horizontal width W and the vertical width H of the rectangular area SI1 is used. In this embodiment, the frame area FL is set so that the target pixel NP is located at the center. As can be understood from the above description, the number M of pixels included in the frame region FL can be expressed as {(FW × 4) −4}.

  In subsequent step S315, the shape determining unit 353 calculates the number Mv of the object partial areas in the frame area by examining the M pixels in the frame area FL one by one. The object partial area is an area in which red object pixels are continuously arranged in the frame area FL. In the example of FIG. 8B, since there are two object partial areas OA1 and OA2, the value of the number Mv is “2”. Here, the reason why the number Mv is calculated is to recognize the number of intersections between the red object and the frame area FL. As shown in FIG. 8B, it can be seen that the number Mv indicates the number of red objects to be processed that intersect the frame region FL. For this reason, the number Mv is also referred to as the intersection number Mv. When the intersection number Mv is 2, the red object passes through the position of the target pixel NP (in this embodiment, the center of the frame arrangement rectangle) within the rectangle (frame arrangement rectangle) in which the frame area FL is arranged. It can be estimated that a single line crossing the frame arrangement rectangle is formed. In addition, when it says that it is located in the frame area | region FL, it means that it is located inside the frame of the thickness of 1 pixel. In other words, the pixels located in the frame region FL can be rephrased as pixels constituting the frame region FL. For example, in FIG. 8B, the object partial areas OA1 and OB2 are located in the frame area FL, but the target pixel NP and a plurality of red colors between the object partial area OA1 and the object partial area OA2 are used. The object pixel is not located in the frame area FL. Further, when it is located within the frame arrangement rectangle, it means that it is located within the frame area FL or inside the frame area FL. For example, in FIG. 8B, the object partial areas OA1, OA2, the target pixel NP, and the plurality of red object pixels between the object partial area OA1 and the object partial area OA2 are all located within the frame arrangement rectangle. Yes.

  For example, the frame area FL2 shown in FIG. 8A shows an example in which the number of intersections Mv is 2, but the red object passes through the center of the frame arrangement rectangle in the frame arrangement rectangle and passes through the frame arrangement rectangle. It can be seen that it forms a single line that crosses. On the other hand, the frame area FL1 shown in FIG. 8A shows an example in which the number of intersections Mv is 1. However, since the red object forms the end in the frame arrangement rectangle, one line passing through the center is shown. This line does not cross the frame layout rectangle.

  In step S320 of FIG. 7, the shape determination unit 353 determines whether all red object pixels have been selected as the target pixel. When there is an unselected red object pixel (step S320: NO), the shape determination unit 353 returns to step S305, selects an unselected red object pixel, and performs the processing from step S305 to S320 described above. repeat. When all the red object pixels are selected (step S320: YES), the shape determining unit 353 advances the process to step S325. At the time of shifting to step S325, the number of intersections Mv is calculated for the frame region FL corresponding to all red object pixels constituting the red object to be processed. Hereinafter, the red object pixel corresponding to the frame area FL whose intersection number Mv is “A (A is a natural number)” is simply referred to as a red object pixel whose intersection number Mv is “A”. Further, the fact that the number of intersections Mv between the frame area FL corresponding to the red object pixel and the red object is “A” simply means that the number of intersections Mv of the red object pixel is “A”.

  In step S325, the ratio RT2 of red object pixels whose intersection number Mv is 2 with respect to all red object pixels is calculated. A red object pixel having an intersection number Mv of 2 is also referred to as a first specific pixel. In step S330, the shape determining unit 353 determines whether the ratio RT2 is equal to or greater than the reference value THa. When the ratio RT2 is equal to or greater than the reference value THa (step S330: YES), the shape determining unit 353 determines that the red object has a surrounding shape (step S370). When the red object has a surrounding shape, it is considered that the intersection number Mv of most red object pixels (target pixels) is two as in a frame region FL2 shown in FIG. Exceptionally, like the frame region FL1 shown in FIG. 8A, the number Mv of intersections of red object pixels in the vicinity of the end of the drawn stroke line may not be two. In addition, the number of intersections Mv of some red object pixels may not be 2 due to noise or the like. In consideration of such circumstances, it is possible to appropriately determine whether or not the red object has a surrounding shape by appropriately defining the reference value THa. In this embodiment, THa is set to 0.85.

  Further description will be given with reference to FIG. FIG. 9A shows an example in which the red object is a character 63A (FIG. 3B). A frame region FL4 shown in FIG. 9A shows an example in which the number of intersections Mv is 3, but it is understood that the red object has a shape branched into three in the frame arrangement rectangle. . Further, as in the frame region FL5 shown in FIG. 9A, the red object pixel near the end of the character is likely to have the intersection number Mv of 1. In this way, when the red object is a character, it often has a portion branched into three or more and three or more ends, so the proportion of red object pixels whose intersection number Mv is not 2 is This is higher than when the red object has a surrounding shape. The red character is determined to be a single color in the single color determination process (FIG. 5: step S210), and in the vertical direction determination process (FIG. 5: step S235) or the horizontal direction determination process (FIG. 5: step S240) There is a relatively high possibility of being determined. However, in the shape determination process, it is determined that the red character is not a surrounding shape. Therefore, in the shape determination process, it is determined that a red object (for example, a character) that is not a surrounding line is not a surrounding shape by making a determination using the ratio RT2, and a red object that is a surrounding line is appropriately Can be judged.

  Here, as shown in the equation of FIG. 8C, the width FW of the frame region FL increases as the resolution RS increases, increases as the line width LW of the red object increases, and increases as the object size OBS increases. Has been. The reason for this will be explained. When the width FW of the frame area FL is excessively small with respect to the line width LW, for example, in an extreme example, the entire frame arrangement rectangle of the frame area FL is filled with red object pixels, and the number of branches is reduced. There is a possibility that the indicated intersection number Mv cannot be calculated appropriately. In addition, when the width FW of the frame area FL is excessively larger than the object size OBS and the line width LW, a plurality of separated parts of one red object are included in the frame arrangement rectangle of the frame area FL. The possibility of being placed increases. For example, the possibility that a plurality of lines are arranged within the frame arrangement rectangle of the frame region FL is relatively high. As a result, there is a possibility that the number of intersections Mv indicating the number of branches of one part passing through the target pixel NP cannot be calculated appropriately. Note that the higher the resolution RS, the smaller the dimension on the document corresponding to one pixel (for example, (1/300) inch in the case of 300 dpi), so the width FW of the frame area FL indicated by the number of pixels. The value of is increased as the resolution RS is higher. As a result, it is possible to determine an appropriate width FW (number of pixels) of the frame area FL according to the size of the encircled line assumed on the document.

  When the ratio RT2 of the red object pixels whose intersection number Mv is 2 is less than the reference value THa (step S330: NO), the shape determination unit 353 advances the process to step S340. Here, when the ratio RT2 of the red object pixels whose intersection number Mv is 2 is less than the reference value THa, it is not immediately determined that the red object is not a surrounding shape. This is because even if the ratio RT2 of the red object pixels having the intersection number Mv of 2 is relatively small, the red object may be a surrounding line if the predetermined condition is satisfied. .

  For example, in the encircling line 66A shown in FIG. 8A, as described above, the red object pixel having the intersection number Mv of 1 exists in the vicinity of the start end and the end end of the encircling line 66A of one stroke. Yes. Specifically, when both ends (start end and end end) of the stroke line drawn with one stroke do not completely match, for example, when both ends are separated (FIG. 8A), one end of both ends When the part is in contact with a position different from the other end of the enclosing line (FIG. 9B), when the encircling line intersects in the vicinity of both ends (FIG. 9C), There may be a red object pixel with an intersection number Mv = 1.

  Further, as shown in FIG. 9B, a red object pixel whose intersection number Mv is 3 in a surrounding line 66B in which one end of both ends is in contact with a position different from the other end of the surrounding line. (See frame region FL7 in FIG. 9B). Further, as shown in FIG. 9C, in a surrounding line 66C intersecting in the vicinity of both ends, there is a red object pixel having an intersection number Mv of 4 (see frame region FL9 in FIG. 9B). obtain. Further, except when there is an influence of noise, when the red object is a one-stroke outline, there is no red object pixel having an intersection number Mv of 5 or more. Here, the red object pixel whose intersection number Mv is any one of “1, 3, 4” is also referred to as a second specific pixel. A red object pixel whose intersection number Mv is “5” or more is also referred to as a third specific pixel. As can be understood from the above description, even if the ratio RT2 of the first specific pixel is less than the reference value THa, the distribution state of the second specific pixel and the third specific pixel has the characteristics of the surrounding line. If so, it can be said that the possibility that the red object is a surrounding line is relatively high. In consideration of this, the processing after step S340 is executed.

  In step S340, the shape determination unit 353 calculates a ratio RT5 of red object pixels (third specific pixels) having an intersection number Mv of 5 or more with respect to all red object pixels. In step S350, the shape determining unit 353 determines whether or not the third specific pixel ratio RT5 is equal to or less than the reference value THb. When the ratio RT5 of the third specific pixel is larger than the reference value THb (step S350: NO), the shape determining unit 353 determines that the red object is not a surrounding shape (step S375). As described above, when the red object is a one-stroke outline, the third specific pixel is determined to be the third specific pixel due to an exceptional factor such as noise. Because basically it does not exist. For example, when the red object is a surrounding line, the reference value THb is set to a value slightly larger than the upper limit value of the number of third specific pixels that can be generated by noise. Specifically, in this embodiment, the reference value THb is set to 0.1. In the shape determination process, it is possible to appropriately exclude a red object that is not a surrounding line from the determination target by making a determination using the ratio RT5.

  When the ratio RT5 of the third specific pixel is equal to or less than the reference value THb (step S350: YES), the shape determination unit 353 sets a specific region ES used for determination described later (step S355). FIG. 10 is a diagram illustrating determination using the specific area ES. Based on the position (coordinates) of the plurality of red object pixels (second specific pixels) whose intersection number Mv is any one of “1, 3, 4”, the shape determination unit 353 The center of gravity GC of the second specific pixel is calculated. As described above, when the red object is an encircling line, the second specific pixels are concentrated in the vicinity of both ends of the encircling line drawn with one stroke (FIGS. 8A and 9B). (C)). Therefore, as shown in FIG. 10A, when the red object is an encircling line, the center of gravity GC is located near both ends of the encircling line drawn with one stroke.

  The shape determination unit 353 sets a square specific region ES with the center of gravity GC as the center. The width (number of pixels) EW in the X direction and the Y direction of the specific area ES is expressed by the formula shown in FIG. As can be seen from this equation, the width EW of the specific area ES is the same as the width FW of the frame area FL (FIG. 8C), the reading resolution RS of the scanned image 60, the line width LW of the red object, and the pixels ) And the size OBS of the red object.

  In subsequent step S360, the shape determining unit 353 calculates the ratio RTC of the second specific pixels located in the specific region ES with respect to all the second specific pixels. In step S365, the shape determination unit 353 determines whether or not the ratio RTC of the second specific pixel is equal to or greater than the reference value THc. When the ratio RTC of the second specific pixel is smaller than the reference value THc (step S365: NO), the shape determining unit 353 determines that the red object is not a surrounding shape (step S375). As described above, when the red object is a one-stroke outline, the second specific pixels are concentrated on a part of the red object, that is, near both ends of the one-stroke outline (that is, the specific area). Concentrate on ES). On the other hand, the ends of the lines constituting the character are distributed at a plurality of locations in the character. For example, there is one end of the character “Y” (see FIG. 9A) in the central portion below the character “Y”, and one in each of the upper right portion and the upper left portion of the character. Further, the lines constituting the character can be branched into various numbers within the character. For example, the letter “Y” (see FIG. 9A) branches into three at the central portion. Therefore, when the red object is a character, the second specific pixel is highly likely to be dispersed without being concentrated on a part of the red object. In the shape determination processing, it is determined that a red object (for example, a character) that is not a surrounding line is not a surrounding shape by making a determination using the ratio RTC, and it is appropriately determined that a red object indicating the surrounding line is a surrounding shape. can do. The reference value THc is, for example, a design value set experimentally, and is set to 0.5 in this embodiment.

  When the ratio RTc of the second specific pixel is equal to or greater than the reference value THc (step S365: YES), the shape determination unit 353 determines that the red object has a surrounding shape (step S370).

  Here, as shown in the equation of FIG. 10B, the width EW of the specific area ES is larger as the resolution RS is larger, as is the width FW of the frame area FL (FIG. 8C). The larger the line width LW is, the larger the object size OBS is. When the width EW of the specific area ES is excessively large with respect to the object size OBS, even if the second specific pixels are spread and distributed over a relatively wide range, the ratio RTC is the reference value THc. There is a possibility that Further, when the width EW of the specific area ES is excessively small with respect to the object size OBS, the ratio RTC is equal to the reference value THc even when the second specific pixels are concentrated in a relatively narrow range. Could be less. For this reason, it is preferable that the specific area ES is set to an appropriate size with respect to the object size OBS. The line width LW of the red object has a correlation with the size of the object, and when the red object is a surrounding line, it is considered that the larger the size of the object, the higher the possibility that the line width LW becomes thicker. Further, the higher the resolution RS, the smaller the dimension on the document corresponding to one pixel (for example, (1/300) inch in the case of 300 dpi), so the width EW of the specific area ES indicated by the number of pixels. The value of is increased as the resolution RS is higher. As a result, it is possible to determine an appropriate width FW (number of pixels) of the specific area ES according to the size of the encircled line assumed on the document.

If it is determined in step S370 or step S375 whether the red object to be processed has a surrounding shape, the shape determination process ends. In the shape determination process described above, there are the following three conditions for determining that the red object to be processed is a surrounding shape.
(1) The ratio RT2 of the first specific pixel is greater than or equal to the reference value THa (step S330).
(2) The ratio RT5 of the third specific pixel is less than the reference value THb (step S350).
(3) The ratio RTC of the second specific pixel is greater than or equal to the reference value THc (step S365).

  In the above embodiment, when the condition (1) is satisfied, it is determined that the red object has a surrounding shape regardless of other conditions. Further, only when both the condition (2) and the condition (3) are satisfied, it is determined that the red object has a surrounding shape even when the condition (1) is not satisfied. Such usage of the conditions (1) to (3) is an example, and is not limited thereto. For example, depending on whether the reference values THa, THb, and THc are set larger or smaller, the usage of these conditions (1) to (3) can change. For example, it is determined that the red object has a surrounding shape in the two cases of the case where the condition (1) and the condition (2) are satisfied and the case where the condition (1) and the condition (3) are satisfied. In other cases, it may be determined that the red object is not a surrounding shape. Moreover, it may be determined that the red object has a surrounding shape only when all of the conditions (1) to (3) are satisfied. Moreover, one or both of the conditions (2) and (3) may not be determined. For example, when the condition (1) is satisfied, when at least one of the condition (2) and the condition (3) is satisfied, the red object is determined to have a surrounding shape, and otherwise, the red object is It may be determined that the shape is not a surrounding shape.

  Returning to FIG. In step S250, the attribute determination unit 360 determines whether the determination condition of the determination process executed immediately before is satisfied. That is, if the determination process executed immediately before is the vertical line determination process (step S235) or the horizontal line determination process (step S240), it is determined whether or not it is determined that the line is determined in the determination process. Is done. Further, when the determination process executed immediately before is the shape determination process (step S245), it is determined whether or not it is determined that the surrounding shape is determined in the determination process.

  When the determination condition of the determination process executed immediately before is satisfied (step S250: YES), the attribute determination unit 360 determines whether all the determination processes have been executed, that is, the vertical line determination process (step It is determined whether all of S235), horizontal line determination processing (step S240), and shape determination processing (step S245) have been executed (step S255). If there is a determination process that has not been executed (step S255: NO), the attribute determination unit 360 returns to step S230. As a result, among the determination processes that are not executed, the determination process having the next execution order is executed. Note that which determination process is executed next is determined by a process order determination process described later. If all the determination processes have been executed (step S255: YES), the attribute determination unit 360 determines that the red object to be processed is a surrounding line (step S260).

  On the other hand, when the determination condition of the determination process executed immediately before is not satisfied (step S250: NO), the attribute determination unit 360 determines that the red object to be processed is not a surrounding line (step S265).

  In step S270, the image processing unit 300 determines whether all red objects have been selected. If there is an unselected red object (step S270: NO), the image processing unit 300 returns to step S205, selects the unselected red object, and repeats the processing from step S205 to S265 described above. . If all red objects have been selected (step S270: YES), the image processing unit 300 ends the object determination process.

  As understood from the above description, when it is determined that the red object to be processed is a single color (step S215: NO), the following three determination conditions (A) to (C) are respectively determined. When all the three determination conditions are satisfied, the red object is determined to be a surrounding line. If at least one of the following three conditions (A) to (C) is not satisfied, the red object is determined not to be a surrounding line.

(A) It is determined as a line in the vertical direction line determination process (step S235) (B) It is determined as a line in the horizontal direction line determination process (step S240) (C) Shape determination process (step It is determined that it is a surrounding line in S245)

  As will be understood from this description, in this embodiment, when it is determined that the red object is an encircling line, three determination processes (analysis processes) are performed, but it is determined that the red object is not an encircling line. In some cases, all three determination processes may not be executed. That is, when the determination condition is not satisfied in the determination process executed in advance among the three determination processes (for example, the line determination process as the first analysis process), the analysis processing unit 350 determines the remaining determinations. Processing (for example, shape determination processing as the second analysis processing) is not executed, and the attribute determination unit 360 determines that the red object is not a surrounding line based on the processing result of the determination processing executed in advance. .

  In this embodiment, the processing time of the object determination process is reduced by appropriately determining the processing order of the three determination processes in the process order determination process of step S220. Details of the processing order determination process will be described below.

  FIG. 11 is a flowchart of processing order determination processing. As shown in FIGS. 6A and 8A, an example will be described in which a surrounding line 66A (FIG. 3B) in the binary image 60A is a red object to be processed. In step S221, the processing time determination unit 330 calculates the number of pixels (H × W) included in the rectangular area SI1 circumscribing the red object to be processed. H represents the number of pixels in the vertical direction (Y direction) of the rectangular area SI1. W represents the number of pixels in the horizontal direction (X direction) of the rectangular area SI1.

  In step S222, the processing time determination unit 330 calculates the number S of red object pixels constituting the red object to be processed. In the examples of FIGS. 6A and 8A, the number of pixels (black part pixels) constituting the surrounding line 66A is calculated as the number S of red object pixels.

  In step S223, the processing time determination unit 330 calculates the number M of pixels in the frame region FL (FIG. 8B) used in the shape determination process. As described with reference to FIG. 8B, the number M of pixels included in the frame region FL is based on the width (number of pixels) FW in the X direction and the Y direction of the frame region FL. FW × 4) −4}.

  Step S224 indicated by a broken line in FIG. 11 is not executed in the embodiment. Step SS224 will be described later as a modified example.

  In step S225, the processing time determination unit 330 determines whether or not the processing time of the line determination process is less than the processing time of the shape determination process. In the determination of this step, the number of pixels (H × W) in the rectangular area SI1 calculated in step S221 is used as the evaluation value for evaluating the processing time of the line determination process. As described above, the line determination process (the horizontal direction line determination process and the vertical direction line determination process) is a process that is executed on all the pixels in the rectangular area SI1. Therefore, the processing time of the line determination process is considered to be approximately proportional to the number of pixels (H × W) in the rectangular area SI1. The evaluation value for evaluating the processing time of the shape determination process is a product of the number S of red object pixels calculated in step S222 and the number M of pixels in the frame area FL calculated in step S223 ( S × M) is used. As described above, the shape determination process is a process in which a frame region FL is set for each of the S red object pixels, and each of the M pixels in the S frame region FL is a processing target. Therefore, it is considered that the processing time of the shape determination process is approximately proportional to the product (S × M) of the number S of red object pixels and the number M of pixels in the frame region FL.

  When the value of (H × W) is less than the value of (S × M), the processing time determination unit 330 determines that the processing time of the line determination process is less than the processing time of the shape determination process. . When the value of (H × W) is equal to or greater than the value of (S × M), the processing time determination unit 330 determines that the processing time of the line determination process is equal to or greater than the processing time of the shape determination process. .

  When it is determined that the processing time of the line determination process is less than the processing time of the shape determination process (step S225: YES), the processing time determination unit 330 determines the line determination process as a preceding process (step S226). ). That is, prior to the shape determination process, the processing order is determined so as to execute the line determination process. In addition, when it is determined that the processing time of the line determination processing is equal to or longer than the processing time of the shape determination processing (step S225: NO), the processing time determination unit 330 performs the shape processing. The preceding process is determined (step S227). That is, prior to the line determination process, the processing order is determined so as to execute the shape determination process.

  According to the processing order determination processing described above, processing that is determined to have a short processing time among the line determination processing and the shape determination processing is executed in advance according to the processing time of the determination processing (analysis processing). Is done. Therefore, when the determination process executed in advance does not satisfy the determination condition, it is determined that the red object is not a surrounding line without executing the determination process with a long processing time. For example, if it is determined that the red object is not a line as a result of the line determination process being executed in advance, the red object is determined not to be a surrounding line regardless of the shape determination process. Conversely, if the red object is determined not to be a surrounding shape as a result of the shape determination process being executed in advance, it is determined that the red object is not a surrounding line regardless of the line determination process. As a result, there is a high possibility that it is possible to determine whether or not the red object is a surrounding line without executing a process having a long processing time among the line determination process and the shape determination process. Therefore, it is possible to reduce the processing time of the analysis process for determining whether or not the red object is a surrounding line.

  Furthermore, the processing time determination unit 330 determines the processing time of the line determination process using the first value corresponding to the number of pixels that are the target of the line determination process, and the pixel that is the target of the shape determination process. The processing time of the shape determination process is determined using the second value corresponding to the number of the images. As a result, it is possible to appropriately determine the processing time of the line determination process and the processing time of the shape determination process.

  More specifically, since the line determination processing includes processing for a plurality of pixels in the rectangular area including the red object, the first value includes a value (H × W) is used. Since the shape determination process includes a process for a plurality of pixels constituting the red object, a value (S × M) corresponding to the number of pixels constituting the object is used as the second value. As a result, the first analysis process and the second processing time can be appropriately determined according to the contents of the line determination process and the shape determination process.

  For example, a linear object such as a surrounding line 66A shown in FIG. 6A is a red object, and a non-linear object such as a drawing partial object 68A shown in FIG. 6B is a red object. Compare with the case. As in the example of FIG. 6A, the linear object may have a significantly smaller number of pixels S in the object area than the number of pixels (H × W) in the circumscribed rectangular area ((H × W) >> S). In this case, since (H × W)> (S × M) is established, it is determined that the processing time of the shape determination process is shorter than the processing time of the line determination process. On the other hand, a non-linear object such as the drawing partial object 68A shown in FIG. 6B has a difference between the number of pixels in the circumscribed rectangular area (H × W) and the number of pixels S in the object area. May be small. In the example of FIG. 6B, since most of the circumscribed rectangular area SI1 is occupied by object pixels, the difference between the number of pixels in the rectangular area (H × W) and the number of pixels S in the object area is It can be seen that it is relatively small. In this case, since (H × W) <(S × M) is established, it is determined that the processing time of the shape determination process is shorter than the processing time of the line determination process.

  Returning to FIG. 2, the description will be continued. When the object determination processing is completed, in the subsequent step S150 (FIG. 2), one surrounding line is selected as a processing target from among the specified surrounding lines (red object determined to be the surrounding line). In the example of FIG. 3, since only one surrounding line 66A (FIG. 3B) is specified, the surrounding line 66A is selected.

  In step S155, the removal processing unit 370 of the image processing unit 300 generates partial image data including the object to be removed. Specifically, the removal processing unit 370 identifies a circumscribed rectangle OS1 (FIG. 3B) circumscribing the processing target enclosing line 66 in the binary image 60A. The removal processing unit 370 generates partial image data representing the partial image PSI by cutting out the partial image PSI (FIG. 3A) corresponding to the circumscribed rectangle OS1 from the scan image 60.

  FIG. 12 is a diagram illustrating an example of the partial image PSI (FIG. 12A) and an example of the partial binary image PBI (FIG. 12B). As shown in FIG. 12, the partial image PSI includes a surrounding line 66 and a drawing 65 as objects.

  In step S160, the removal processing unit 370 performs binarization processing on the partial image data in order to identify an object in the partial image PSI, and generates partial binary image data. Specifically, the partial image data is binarized into a background pixel constituting the background and an object pixel constituting the object. For example, the removal processing unit 370 includes, for example, a plurality of pixels located near the surrounding line 66A (FIG. 3B), for example, a plurality of pixels located outside the surrounding line 66A. An average value (Rave2, Gave2, Bave2) of a plurality of pixels arranged along 66A is calculated as a reference color for binarization. Alternatively, the removal processing unit 370 is an average value (Rave2, Gave2) of a plurality of pixels that are located outside the partial image PSI in the scan image 60 and are arranged along the outer edge of the partial image PSI. , Bave2) is calculated as a reference color for binarization. Then, the removal processing unit 370 satisfies (Rave 2 −ΔV 2) ≦ R ≦ (Rave 2 + ΔV 2), (Gave 2 −ΔV 2) ≦ G ≦ (Gave 2 + ΔV 2), and (Bave 2 −ΔV 2) ≦ B ≦ (Bave 2 + ΔV 2) Pixels having values (RGB values) are classified as background pixels, and pixels other than the pixels to be determined as background pixels are classified as object pixels. The removal processing unit 370 generates partial binary image data by classifying all pixels included in the partial image PSI as either background pixels or object pixels.

  In step S165, the removal processing unit 370 identifies an object using the generated partial binary image data, and performs labeling that adds an identifier to the identified object. As a result of labeling, for example, label data in which an object is associated with an identifier is generated.

  Specifically, the removal processing unit 370 identifies one area composed of one or more consecutive (adjacent) object pixels as one object. In the example of FIG. 12B, in the partial binary image PBI, a surrounding line 66D and a drawing 65D are specified as objects configured by object pixels, and different identifiers are assigned to these objects. Is done. Note that the object specified in the partial binary image PBI includes the processing target enclosure selected in step S150. For example, a special identifier different from other objects is attached to the object representing the surrounding line so that the object can be recognized as the surrounding line.

  When the object is specified, in subsequent step S170, the removal processing unit 370 performs inclusion relation determination processing for determining whether or not an object other than the enclosure line included in the partial binary image PBI is included in the enclosure line. Run.

  FIG. 13 is a flowchart of the inclusion relationship determination process. In step S405, the removal processing unit 370 selects an object other than the enclosing line as a processing target from among the objects specified in the partial binary image PBI. In the example of FIG. 12B, the drawing 65D is selected as the object to be processed.

  In step S410, the removal processing unit 370 sets four determination lines A1 to A4 as shown in FIG. The four determination lines A1 to A4 are based on the center of gravity CC of the object to be processed (specifically, the drawing 65D) as an origin, upward (−Y direction), downward (+ Y direction), and left direction (−X Direction) and rightward (+ X direction).

  In step S415, the removal processing unit 370 specifies the number of intersections (intersection number Mx) between each of the four determination lines A1 to A4 and the surrounding line 66D for each determination line. Specifically, the removal processing unit 370 sequentially scans pixels on the determination line to be processed from the base point (center of gravity CC) toward the outside in the partial binary image PBI. Then, the removal processing unit 370 counts the number of intersection points detected on the determination line. For example, in the example of FIG. 12B, it can be seen that each of the four determination lines A1 to A4 and the surrounding line 66D have one intersection point CP1 to CP4.

  In step S420, the removal processing unit 370 determines, for each determination line, whether or not the intersection number Mx is an odd number, and among the four determination lines A1 to A4, a determination line (hereinafter referred to as the intersection number Mx is an odd number). The number LIn). In the example of FIG. 12B, since the number Mx of intersections of the four determination lines A1 to A4 is all “1”, the number of odd number determination lines LIn is “4”.

  In step S425, the removal processing unit 370 determines whether or not the number of odd determination lines LIn is 3 or more. When the number LIn of the odd number determination lines is 3 or more (step S425: YES), the removal processing unit 370 determines that the object to be processed is included in the surrounding line 66D (step S430). When the number LIn of the odd number determination lines is less than 3 (step S425: NO), the removal processing unit 370 determines that the object to be processed is not included in the surrounding line 66D (step S435).

  It is assumed that the surrounding line 66D is a closed curve, and the end point of the determination line (the end of the image of the partial binary image PBI) is outside the closed curve. When scanning from the base point of the determination line toward the end point, the internal / external relationship of the point of interest (scanning position) on the determination line changes every time it passes through the intersection with the surrounding line 66D. The change in the internal / external relationship means that the position of the attention point changes from the inside to the outside of the surrounding line, or changes from the outside to the inside of the surrounding line. Accordingly, when the base point of the determination line is located inside the enclosing line, the number of intersections Mx between the base point of the determination line and the end point is an odd number considering that the end point is always located outside the enclosing line. I understand that

  Conversely, when the base point of the determination line is located outside the encircling line, it is understood that the number of intersection points Mx between the base point of the determination line and the end point is an even number. For example, let us consider four determination lines A5 to A8 respectively extending in the vertical and horizontal directions from the sample point SP1 (FIG. 12B) located outside the surrounding line 66D. The two determination lines A5 and A7 each have two intersections with the surrounding line 66D (CP5 to CP8 in FIG. 12B). On the other hand, neither of the other two determination lines A6 and A8 has an intersection with the surrounding line 66D (has 0 intersections). As described above, the number of intersection points Mx between the base point and the end point of the determination line with the sample point SP1 positioned outside the surrounding line 66D as the base point is an even number (in this example, 0 or 2).

  In this way, if a determination line having a base point on the processing target object is set, the processing target object is included in the surrounding line based on the intersection number Mx of the determination line and the surrounding line (the surrounding line). Can be determined). Here, when four determination lines A1 to A4 are set and the number of intersection points Mx is an odd number is 3 or more, it is determined that the object to be processed is included in the enclosing line Will be explained. This is because, as an exception, even when the object to be processed is included in the enclosing line, the intersection number Mx may be an even number. For example, since the encircling line 66D is assumed to be written by handwriting, a gap BT may be generated in the vicinity of both ends of one-stroke writing as shown in FIG. If the determination line A2 in the downward direction in FIG. 12B passes through the gap BT, it can be seen that the intersection number Mx of the determination line A2 becomes 0 (even). In such a case, for example, if the determination is made based on one determination line, it is clear that an erroneous determination result can be obtained. In this embodiment, since the number LIn of the odd number judgment lines among the four judgment lines is based on whether or not it is 3 or more, the number of intersections Mx of one judgment line is exceptionally even. Even in such a case, the inclusion relationship can be appropriately determined.

  In step S440, the removal processing unit 370 determines whether all objects other than the enclosing line have been selected as processing targets. If there is an unselected object (step S440: NO), the removal processing unit 370 returns to step S405, selects the unselected object, and repeats the processing from step S405 to S440 described above. If all the objects have been selected (step S440: YES), the removal processing unit 370 ends the inclusion relationship determination process.

  When the inclusion relation determination process is completed, in the subsequent step S175 (FIG. 2), the removal processing unit 370 converts the surrounding line 66 and the object determined to be included in the surrounding line 66 into the scanned image 60 (FIG. 3). The object removal process to be removed from (A)) is executed. Based on the partial binary image PBI (FIG. 12B), the removal processing unit 370 configures a surrounding line 66 and an object (drawing 65) included in the surrounding line 66 on the scan image 60. A plurality of pixels to be identified are specified. The removal processing unit 370 changes the pixel values of the specified plurality of pixels to pixel values representing the background color of the scanned image 60. The pixel value representing the background color is, for example, a pixel value representing the color (white) of the original paper, and a pixel value representing the reference color for binarization calculated in step S160 (FIG. 2). (Rave2, Gave2, Bave2) are used. Thus, processed image data representing the processed image 60C (FIG. 3C) from which the surrounding line 66 and the drawing 65 are removed is generated from the scanned image 60.

  In step S180, the image processing unit 300 determines whether all the surrounding lines have been selected. If there is an unselected enclosing line (step S180: NO), the image processing unit 300 returns to step S150, selects an unselected enclosing line, and repeats the processes from steps S150 to S180 described above. . When all the encircling lines are selected (step S180: YES), the image processing unit 300 transmits the processed image data to the MFP 200 that is the transmission source of the scan data (target image data). (Step S185), and the process is terminated.

  When receiving the processed image data from the server 400, the image data transmission / reception unit 115 of the multifunction device 200 stores the image data in the nonvolatile storage device 230, for example, and ends the processing. This processed image data is used, for example, to print an image represented by the processed image data on a sheet using the printer unit 240.

  According to the image processing of the present embodiment described above, the user reads a document surrounded by a box line in which a desired object is filled with a red pen by causing the scanner unit 250 of the multifunction device 200 to read the document. Image data representing an image from which a desired object has been removed can be acquired. Note that the surrounding line can be used in addition to the designation of the object to be removed. For example, instead of the object removal process (step S175), the image processing unit 300 includes only image data representing an object surrounded by a surrounding line, and image data representing an image obtained by removing an object not surrounded by the surrounding line. You may perform the process to produce | generate. Generally speaking, an encircling line can be used to identify an object enclosed by an encircling line and perform predetermined image processing using partial image data representing a partial image including the identified object. .

  Furthermore, according to the object determination process of the present embodiment, the red object is based on the frame area FL (FIG. 8) set for each of a plurality of specific pixels (specifically, red object pixels) constituting the object. It is determined whether or not is an enclosing line. As a result, the shape of the red object can be appropriately analyzed based on the number of intersections Mv with the frame region FL as to the branching state of the object, and it can be appropriately determined whether or not the red object is a surrounding line. .

  More specifically, the ratio RT2 of the first specific pixels corresponding to the frame area FL (frame area FL including two object partial areas) having a crossing number Mv of two of the plurality of specific pixels is calculated. It is used to determine whether the red object is a surrounding line (FIG. 7: step S325). As a result, it is possible to appropriately determine whether or not the red object is a surrounding line.

  Furthermore, the shape determination unit 353 includes, among the plurality of second specific pixels corresponding to the frame region FL, where the number of intersections Mv is M (M is an integer of 1, 3, and 4). It is determined whether or not the red object is a surrounding line using the ratio RTC of the second specific pixel included in the specific area ES (FIG. 10) (FIG. 7: Step S365). As a result, whether or not the red object is a surrounding line can be determined with higher accuracy.

  Further, the shape determination unit 353 determines whether or not the red object is a surrounding line using the ratio RT5 of the third specific pixel corresponding to the frame region FL having the intersection number Mv of 5 or more (FIG. 7: Step S340). As a result, whether or not the red object is a surrounding line can be determined with higher accuracy.

  The line determination unit 352 also includes a line determination process including a process of analyzing the distribution of red object pixels on a plurality of straight lines extending in two directions (X direction and Y direction) of a rectangular area circumscribing the red object (see FIG. Step S235 of Step 5 and Step S240) are executed. As a result, it is possible to appropriately determine whether or not the object is a line based on the distribution of red object pixels in a plurality of directions.

  Note that the line determination processing may include only processing for analyzing the distribution of red object pixels in one direction. That is, only one of the vertical direction line determination process and the horizontal direction line determination process may be executed. In this case, it can be determined whether or not the object is a line in a relatively short time.

B. Modification (1) In the processing order determination process of the above embodiment, the processing times are compared by simply comparing the number of pixels to be processed. In addition to this, the processing order determination process is repeated for each pixel to be processed. The processing load of unit processing may be taken into consideration. Specifically, in step S224 indicated by a broken line in the flowchart of FIG. 11, the processing time determination unit 330 determines the coefficient C1 according to the processing load of the unit processing of the line determination processing and the processing load of the unit processing of the shape determination processing. And a coefficient C2 corresponding to. The coefficients C1 and C2 may be values that relatively indicate the processing load. For example, the coefficients C1 and C2 may be values determined in advance based on a value obtained by dividing an actual value of processing time measured using specific test image data by the number of pixels to be processed. . Alternatively, the coefficients C1 and C2 may be determined based on a clock count value required when unit processing is executed in a specific type of computer assumed as a computer that performs object determination processing. The coefficients C1 and C2 may be determined based on the number of unit processing steps in a machine language obtained by compiling for a specific type of computer.

  In this case, the product of the evaluation value (H × W) in the first embodiment and the coefficient C1 is used as the evaluation value for evaluating the processing time of the line determination process. Further, the product of the evaluation value (S × M) in the first embodiment and the coefficient C2 is used as the evaluation value for evaluating the processing time of the shape determination process. That is, in step S225 (FIG. 11), the processing time determination unit 330 determines that the line determination is performed when the value of {C1 × (H × W)} is less than the value of {C2 × (S × M)}. It is determined that the processing time of the process is less than the processing time of the shape determination process. When the value of {C1 × (H × W)} is equal to or greater than the value of C2 × (S × M)}, the processing time determination unit 330 sets the processing time of the line determination processing as the processing of the shape determination processing. Judge that it is over time. According to this modification, it is possible to accurately determine the processing time of the line determination process and the processing time of the second analysis process of the shape determination process according to the processing load of the unit process. As a result, it is possible to appropriately determine which one of the line determination process and the shape determination process should be executed in advance.

(2) In the above embodiment, the line determination process is given as an example of the first analysis process, and the shape determination process is given as an example of the second analysis process. However, the present invention is not limited to this. The first analysis process and the second analysis process are different processes, and any process for analyzing image data and obtaining an analysis result can be used. For example, the first analysis processing is processing for determining whether or not a specific object in the image is a character, for example, and the second analysis processing is whether or not the specific object in the image is a photograph. It may be a process for determining whether or not. In this case, for example, when it is determined by the first analysis process that the character is a character, the specific object is determined to be a character regardless of the second analysis process. When the second analysis process determines that the photograph is a photograph, the specific object is determined to be a photograph regardless of the first analysis process. If it is determined that the processing time of the first analysis process is less than the processing time of the second analysis process, the first analysis process is executed prior to the second analysis process. As a result, when it is determined by the first analysis process that the specific object is a character, the second analysis process is not executed. Further, when it is determined that the processing time of the first analysis process is equal to or longer than the processing time of the second analysis process, the second analysis process is executed prior to the first analysis process. As a result, when it is determined by the second analysis process that the specific object is a photograph, the first analysis process is not executed. As a result, among the first analysis process and the second analysis process, the execution of the analysis process having a long processing time can be suppressed, and the processing time of the entire analysis process can be reduced.

(3) In the above embodiment, the processing times of the two processes of the line determination process and the shape determination process are compared, but the processing times of three or more processes may be compared. For example, in addition to the process of determining whether or not the specific object illustrated in the modification example (2) is a character and the process of determining whether or not the specific object is a photograph, the specific object A case where the process of determining whether or not the drawing is performed will be described as an example. In this case, it is preferable to determine the processing order of the three determination processes by comparing the processing times of these three determination processes with each other. That is, the processing time determination unit 330 calculates the evaluation values of the processing times of these three determination processes, respectively, and determines the processing order of the three determination processes in ascending order of the evaluation values, that is, the processing time is short. It is preferable. In this way, if one of these three determination processes can determine the attribute (photograph, drawing, or character) of a specific object, it is not executed at that time. Regardless of the presence or absence of the determination process, the attribute of a specific object is determined.

(4) In the shape determination process of the above embodiment, the frame area FL is set for all red object pixels constituting the red object to be determined, but only for some of the red object pixels. A frame area FL may be set. Specifically, when the frame region FL is set for one red object pixel, the frame area FL is located within a predetermined distance (for example, within a distance of two pixels) from the one red object pixel. These red object pixels may be removed from the setting target of the frame area FL. Alternatively, a frame region FL is set for a pixel in which both the X coordinate and the Y coordinate are odd numbers among all the red object pixels constituting the red object to be determined, and at least one of the X coordinate and the Y coordinate is set. The frame area FL may not be set for pixels where one is an odd number. In this case, the ratios of the first, second, and third specific pixels with respect to the number of red object pixels in which the frame area FL is set are used as the ratios RT2, RTC, and RT5, respectively.

  Similarly, in the line determination process of the above embodiment, the average continuous pixel numbers LN1ave and LN2ave are calculated for all the pixels in the rectangular area circumscribing the red object. Instead of this, for example, the average continuous pixel numbers LN1ave and LN2ave may be calculated with only some of the pixels in the rectangular area as the processing target. For example, in the vertical direction line determination processing, only pixels on a line having an odd X coordinate among all lines along the Y direction (vertical direction) in the rectangular area may be processed.

  In such a case, the processing time determination unit 330 may adjust the evaluation value of the processing time by, for example, multiplying by a coefficient. For example, the processing time determination unit 330 uses, as a coefficient, the ratio of the red object pixels in which the frame region FL is set to the number of all red object pixels in the evaluation value (S × M) of the processing time of the shape determination process. You may multiply. In addition, the processing time determination unit 330 uses the evaluation value (H × W) of the processing time of the line determination process as the number of lines for which the number of continuous pixels is calculated with respect to the number of all vertical lines in the rectangular area. May be multiplied as a coefficient. In addition, when the coefficient to be multiplied by the evaluation value (S × M) of the processing time of the shape determination process and the coefficient to be multiplied by the evaluation value (H × W) of the processing time of the line determination process are approximately the same It is not necessary to multiply the coefficient.

(5) Note that the object determination process (FIG. 5) of the above embodiment includes both the vertical direction determination process (step S235) and the horizontal direction determination process (step S240). Both the direction determination process determines that the line is a line and the horizontal direction determination process determines that the line is a line. It is a condition. Instead, the object determination process includes only one of the vertical direction determination process and the horizontal direction determination process, and it is determined that the object is determined to be a line by the one process. May be a necessary condition for determining that is a surrounding line.

(6) The evaluation value (H × W) of the processing time of the line determination process in the object determination process of the above embodiment is an evaluation value of each processing time of the vertical direction determination process and the horizontal direction determination process. Instead, as the evaluation value of the processing time of the line determination processing, the total evaluation value of the processing time of the vertical direction determination processing and the horizontal direction determination processing, that is, (H × W) × 2 is adopted. May be.

(7) Although the size (width FW) of the frame region FL and the size (width EW) of the specific region ES in the above-described embodiment are calculated using the equations of FIG. 8 (C) and FIG. 10 (B). Instead of this, a fixed value may be used. Further, the shape of the frame region FL and the shape of the specific region ES are not limited to a square, but may be a rectangle, a circle, or various polygons such as a triangle or a pentagon. .

(8) Note that the target image data of the present embodiment employs scan data read by a scanner, but may be, for example, photographed image data photographed by a digital camera or the like, or a document or drawing For example, the image data may be generated using an application program for creating the image data. In addition, the surrounding line is not limited to the surrounding line written using a pen, for example, and may be a surrounding line drawn in an image using the application program. In this case, for example, even with a surrounding line drawn freehand by using a pointing device such as a mouse, the surrounding line can be appropriately identified.

(9) In the above embodiment, the image processing executed by the image processing unit 300 of the server 400 may be executed by a device different from the server 400, for example, the personal computer 500 (FIG. 1) connected to the multifunction device 200. good. In this case, the image processing may be executed by a scanner driver canister installed in the personal computer 500 for controlling the scanner unit 250 of the multifunction device 200 or a single scanner (not shown), for example. good. Further, this image processing may be executed by the CPU 210 of the multifunction device 200 or the CPU of a single scanner. The server 400 is not limited to a single housing device, and may be configured by a computing system including a plurality of computers (for example, a distributed computing system that realizes so-called cloud computing).

(10) In the above embodiment, a part of the configuration realized by hardware may be replaced with software, and conversely, a part of the configuration realized by software may be replaced with hardware. Also good.

  110 ... communication control unit, 115 ... image data transmission / reception unit, 120 ... device control unit, 200 ... multifunction device, 210 ... CPU, 220 ... volatile storage device, 221 .. Buffer area 230 Non-volatile storage device 231 Control program 240 Printer unit 250 Scanner unit 260 Operation unit 270 Display unit 280 ..Communication unit, 300 ... Image processing unit, 310 ... Image data acquisition unit, 320 ... Object identification unit, 330 ... Processing time determination unit, 350 ... Analysis processing unit, 351 .. Color determination unit, 352... Line determination unit, 353... Shape determination unit, 360... Attribute determination unit, 370... Removal processing unit, 400. ... volatile storage device, 421 ... buffer area, 430 ... nonvolatile storage device, 431 ... computer program, 480 ... communication unit, 500 ... persona Computer, 70 ... Internet, 1000 ... image processing system

Claims (12)

An image processing apparatus,
A specifying unit for specifying an object in the image represented by the image data;
A processing time determination unit that determines a processing time of a first analysis process and a processing time of a second analysis process among a plurality of analysis processes to be performed on the object;
In the first case where the processing time of the first analysis processing is determined to be less than the processing time of the second analysis processing, the first analysis processing is performed prior to the second analysis processing. And in the second case where the processing time of the first analysis process is determined to be equal to or longer than the processing time of the second analysis process, the second analysis process is preceded by the second analysis process. An analysis processing unit for executing the analysis processing of
In the first case, when the processing result of the first analysis processing is the first result, the second analysis processing is executed, and the processing result of the first analysis processing is the first result. If the result is different from the result of 1, the second analysis process is not executed,
In the second case, when the processing result of the second analysis processing is the second result, the first analysis processing is executed, and the processing result of the second analysis processing is the first result. When the result is different from the result of 2, the first analysis process is not executed.
The analysis processing unit;
An attribute determination unit that determines an attribute of the object based on a processing result of an executed process among the plurality of analysis processes;
An image processing apparatus comprising: The image processing apparatus according to claim 1,
The processing time determination unit determines a processing time of the first analysis process by using a first value corresponding to the number of pixels to be processed by the first analysis process, and performs the second analysis. An image processing apparatus that determines a processing time of the second analysis process by using a second value corresponding to the number of pixels to be processed. The image processing apparatus according to claim 2,
The first analysis process includes a process for a plurality of pixels in a specific area including the object,
The first value is a value according to the number of pixels in the specific region,
The second analysis process includes a process for a plurality of pixels constituting the object,
The image processing apparatus, wherein the second value is a value corresponding to the number of pixels constituting the object. An image processing apparatus according to any one of claims 1 to 3,
The first analysis process includes a first unit process that is repeated for each of one or more pixels to be processed;
The second analysis process includes a second unit process that is repeated for each of one or more pixels to be processed,
The processing time determination unit determines a processing time of the first analysis process by using a value according to a processing load of the first unit process, and a value according to a processing load of the second unit process. An image processing apparatus that determines the processing time of the second analysis process using the above-described method. An image processing apparatus according to any one of claims 1 to 4,
The attribute determining unit determines whether the object is a surrounding line surrounding a partial area in the image based on a processing result of an executed process among the plurality of analysis processes. Processing equipment. The image processing apparatus according to claim 5,
The first analysis process is a process of analyzing whether or not the object is a line;
The first result of the first analysis process is to satisfy a first condition indicating that the object is a line;
The attribute determination unit determines that the object is not the surrounding line regardless of the second analysis process when the result of the first analysis process does not satisfy the first condition. Image processing device. The image processing apparatus according to claim 6,
The first analysis process includes a process of analyzing a distribution of pixels constituting the object on a plurality of straight lines extending in a first direction in the specific region,
The first condition is an image processing apparatus, wherein the number of pixels constituting the object that are continuous in the first direction is equal to or less than a reference. The image processing apparatus according to claim 7,
The first analysis processing further includes processing for analyzing distribution of pixels constituting the object on a plurality of straight lines extending in a second direction intersecting the first direction in the specific region. ,
The first condition is that the number of pixels constituting the object continuing in the first direction is less than a reference, and the number of pixels constituting the object continuing in the second direction is less than a reference. An image processing apparatus. An image processing apparatus according to any one of claims 5 to 7,
The second analysis process is a process of analyzing whether or not the object has a shape surrounding a partial area in the image;
The second result of the second analysis process is to satisfy a second condition indicating that the object has a shape surrounding a partial region in the image,
The attribute determination unit determines that the object is not the surrounding line regardless of the first analysis process when the result of the second analysis process does not satisfy the second condition. Image processing device. The image processing apparatus according to claim 9,
The second analysis process includes
A setting process for setting a plurality of frame regions corresponding to each of a plurality of specific pixels constituting the object, wherein the frame region includes a corresponding specific pixel, the frame region, and an inner side of the frame region. The setting process, which is a frame-like region included in any of the positions,
A first determination process for determining the number of object partial areas included in the frame area for each of the plurality of frame areas, wherein the object partial area is one or more of the specific parts constituting the object The first determination process, which is an area in which pixels are continuously arranged;
A second determination process for determining whether a ratio of the first specific pixel to a plurality of the specific pixels constituting the object is greater than or equal to a reference, wherein the first specific pixel constitutes the object; The second determination process, wherein the number of the object partial regions included in the corresponding frame region is the specific pixel among the plurality of the specific pixels.
Including
The second condition is an image processing device including a ratio of the first specific pixel being equal to or higher than a reference. The image processing apparatus according to claim 9,
The second analysis process further includes:
A third determination process for determining whether or not a condition based on a second specific pixel is satisfied, wherein the second specific pixel corresponds among a plurality of the specific pixels constituting the object; Including the third determination process, which is the specific pixel in which the number of the object partial areas included in the frame area is M (M is an integer of any one of 1, 3, 4, and 5). ,
The image processing apparatus, wherein the second condition includes a condition based on the second specific pixel. A computer program for executing image processing,
A specific function that identifies the object in the image represented by the image data;
A processing time determination function for determining a processing time of a first analysis process and a processing time of a second analysis process among a plurality of analysis processes to be performed on the object;
In the first case where the processing time of the first analysis processing is determined to be less than the processing time of the second analysis processing, the first analysis processing is performed prior to the second analysis processing. And in the second case where the processing time of the first analysis process is determined to be equal to or longer than the processing time of the second analysis process, the second analysis process is preceded by the second analysis process. An analysis processing function for executing the analysis processing of
In the first case, when the processing result of the first analysis processing is the first result, the second analysis processing is executed, and the processing result of the first analysis processing is the first result. If the result is different from the result of 1, the second analysis process is not executed,
In the second case, when the processing result of the second analysis processing is the second result, the first analysis processing is executed, and the processing result of the second analysis processing is the first result. When the result is different from the result of 2, the first analysis process is not executed.
The analysis processing function;
An attribute determination function for determining an attribute of the object based on a processing result of an executed process among the plurality of analysis processes;
A computer program that causes a computer to realize

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