JP6472226B2 - Image processing apparatus, image processing method, and computer program - Google Patents

Description

  The present invention relates to a technique for expressing an image as a vector.

  Conventionally, a technique for expressing an image as a vector is widely known. The vector-represented image has advantages such as little image quality degradation at the time of enlargement / reduction, easy image editing, and high compression ratio.

  In the case of vector representation of illustrations and characters, a method of approximating the contour of an object with a Bezier, spline function or the like is used. The object area can be painted with a single color, linear gradation, radial gradation, etc., but it is difficult to express complex gradation.

  In the case of expressing an object including a complex gradation as a vector, an Adobe Illustrator (registered trademark) gradient mesh tool is generally used. In a gradient mesh, an object including a complex gradation can be drawn by generating a cubic function by giving a color and a gradient to the mesh (Patent Document 1).

  A gradient mesh is a curved mesh composed of four vertices, and a technique for expressing a complex gradation vector by a curved mesh composed of three vertices is known (Non-Patent Document 1). Edges in the image are extracted as line segments (hereinafter referred to as edge lines), and a vector expression is performed by configuring a 3-vertex curve mesh group that reproduces the edge lines. Here, the three-vertex curve mesh is assumed that the number of vertices of the mesh is 3, and the sides of the mesh connecting the vertices are straight lines or curves.

  In addition, a method of providing divided vertices with colors added inside the mesh so that the level of detail of color change can be changed in each mesh is also disclosed (Non-Patent Document 2). Note that the above-described three-vertex curve mesh has an advantage that an object can be approximated with a small number of meshes because mesh control can be performed locally. On the other hand, the conventional three-vertex curve mesh is encoded in mesh units, and does not take into account vertices, edges, and colors shared between meshes, so the encoding efficiency is low and the amount of data increases. It was. Therefore, the present applicant uses the characteristics of a three-vertex curve mesh that shares edges, shares sampling points on the edges, and sampling points on the edge line have two colors, and improves mesh representation efficiency. (Patent Document 3).

Japanese Patent Laid-Open No. 10-320585 JP 2002-260005 A JP 2012-221319 A

Tian Xia, Binbin Liao, and Yizhou Yu, “Patch-Based Image Collection with Automatic Curvilinear Feature Alignment”, ACM SIGGRAPH Asia Via.9. 28, no. 5. Chem YukselJohn KeyserDonald H. House, “Mesh Colors”, Technical Report, Department of Computer Science, Texas A & M University, 2008

  In a natural image vectorization technique for approximating a natural image in a mesh format, each mesh constituting a mesh group is divided into sub-mesh, and mesh data is also generated using color information at the vertices of the sub-mesh. At this time, regarding the vertexes of the sub-mesh (hereinafter also referred to as color points), when the number of color points is increased, the image quality is improved, but the data amount is increased. Therefore, in view of the trade-off between image quality and data volume, instead of using a uniform number of color points per mesh, it is simple and fast to generate an adaptive mesh group in which meshes with different numbers of color points are mixed. Is desired.

  The present invention has been made in view of such a problem, and provides a technique for adaptively controlling the number of vertices of a sub-mesh for each mesh.

  According to one aspect of the present invention, the input image is input to the mesh so that a detection line detected from the input image is composed of three vertices and a side connecting the three vertices is one side of a mesh that is a straight line or a curve. An image processing apparatus that divides into units and determines each vertex of the sub-mesh to divide the mesh into a plurality of sub-mesh, and is a candidate set as a candidate for a vertex of the sub-mesh that divides the mesh Calculation means for obtaining a difference between the color information of the partial vertex group which is a part of the vertex group and the color information of the partial vertex group obtained from the color information of the other vertex group excluding the partial vertex group in the candidate vertex group When the difference is smaller than a specified value, the other vertex group is set as the candidate vertex group and then the calculation unit is operated. When the difference is equal to or larger than the specified value, the candidate vertex group The message Characterized in that it comprises a determination means for determining as vertices of each sub-meshes for dividing the Interview.

  According to the configuration of the present invention, it is possible to provide a technique for adaptively controlling the number of vertices of sub-mesh for each mesh.

FIG. 3 is a block diagram illustrating an example of a functional configuration of the image processing apparatus. The figure explaining the case where an undesirable result is obtained as an edge line. The figure explaining operation | movement of the feature point extraction part 1022. FIG. The figure explaining operation | movement of the feature point extraction part 1022. FIG. The flowchart of the process which the curved mesh production | generation part 1023 performs. The figure which shows the result of having performed the 3 vertex curve mesh group production | generation process with respect to the characteristic line of Fig.3 (a). The figure explaining shape information and color information. The figure explaining the division | segmentation of a Bezier patch. The flowchart of the process after the maximum color point number determination part 103. FIG. The figure explaining operation | movement of the color point candidate reduction part 1071. FIG. The figure which shows a response | compatibility of a division | segmentation index | exponent, a division | segmentation number, and a color point number. FIG. 2 is a block diagram illustrating a hardware configuration example of a computer applicable to an image processing apparatus.

  Preferred embodiments of the present invention will be described below with reference to the accompanying drawings. The embodiment described below shows an example when the present invention is specifically implemented, and is one of the specific examples of the configurations described in the claims.

[First Embodiment]
In this embodiment, the input image is divided in units of the mesh so that the detection line detected from the input image is composed of three vertices and the side connecting the three vertices is one side of a mesh that is a straight line or a curve. An example of an image processing apparatus that determines each vertex of the sub-mesh in order to divide the mesh into a plurality of sub-mesh will be described. That is, the color information of the partial vertex group that is a part of the candidate vertex group set as the candidate for the vertex of the sub-mesh that divides the mesh, and the color information of the other vertex group excluding the partial vertex group in the candidate vertex group If the difference is smaller than the specified value, the difference is calculated again after setting another vertex group as a candidate vertex group. An example of a configuration in which the candidate vertex group is determined as the vertex of each sub-mesh that divides the mesh when the value is equal to or greater than the predetermined value will be described.

  First, the three-vertex curve mesh used in this embodiment will be described. As described above, each side of the three-vertex curve mesh is a curve or a line segment, but this curve may be constituted by a Bezier curve or a B-spline. That is, the type of curve is not limited to a specific type. In the following description, a three-vertex Bezier patch that is one of parametric curved surfaces is used for the three-vertex curved mesh. Among the Bezier patches, in particular, the cubic Bezier patch has an advantage that each side of the mesh becomes a cubic Bezier curve, so that the user can easily edit after generating the three-vertex curve mesh. The points in the cubic Bezier patch having the vertices at P1, P2, and P3 are expressed by the following equations.

  Here, s, t, and u are patch parameters (center-of-gravity coordinates in the patch). Further, p1, p2, and p3 are coordinate values of the vertices P1, P2, and P3, respectively. c1, c2, c3, c4, c5, c6, and c7 are parameters that determine the shape of the curved patch. In particular, c1 to c6 are control points for the vertices P1, P2, and P3 (three parameters that determine the contour of the Bezier patch). This represents the coordinate value of the control point of the Bezier curve. c7 is a parameter that does not relate to the contour of the Bezier patch. In this embodiment, the parameter is determined from the coordinates of the vertex of the Bezier patch and the parameter corresponding to the control point of the Bezier curve forming the contour by the following equation.

  Next, a functional configuration example of the image processing apparatus according to the present embodiment will be described with reference to the block diagram of FIG. The configuration shown in FIG. 1 is merely an example of a configuration that can realize the processing described below, and any configuration that can realize processing equal to or higher than that is not limited to the configuration shown in FIG. A configuration may be adopted.

  A raster representation image (input image) is input to the image input unit 101. The input image may be a color image, a gray image, or a black and white image. Further, the input image may be a rectangular image, and when it is desired to vectorize a part of the image, a clipped region such as an image in which the background portion is set to a transparent color may be given. Then, the image input unit 101 transfers this input image to the subsequent mesh generation unit 102.

  The mesh generation unit 102 performs a process of dividing the input image into the three-vertex curve mesh group by generating a three-vertex curve mesh group that reproduces a feature line having a curved shape representing the feature in the input image. A feature line detection unit 1021, a feature point extraction unit 1022, and a curved mesh generation unit 1023.

  The feature line detection unit 1021 configures a curve-shaped feature line representing the feature in the input image. In the present embodiment, edge lines are used as feature lines. The process for configuring the feature line is roughly divided into an edge detection process and an edge line configuration process.

  First, the edge line detection process will be described. In the edge line detection process, first, pixels (edge pixels) constituting an edge are detected from the input image. In the present embodiment, the edge pixel is detected using a known method called the Canny method, but the edge pixel may be detected using another method. In addition, preprocessing such as brightness adjustment and edge enhancement may be performed on the input image so that the Canny method functions effectively.

  Then, an edge line configuration process is performed. In the edge line configuration process, first, a line segment that reproduces an edge line is configured by configuring a line segment that connects adjacent edge pixels. When a line segment group is formed by connecting adjacent edge pixels, there is a four-connection connection in which four edge pixels located above, below, left, and right of the target edge pixel are considered to be adjacent. Further, in addition to these four directions, there are eight connected connections in which four edge pixels on the upper right, lower right, upper left, and lower left are considered to be adjacent. In the present embodiment, eight linked connections are used, but this is only an example.

  Here, when edge pixels that can be considered to be adjacent to each other by 8-connection are connected to edge pixels detected using the Canny method, an undesirable result may be obtained as an edge line. This case will be described with reference to FIG.

  FIGS. 2A to 2D show a pixel group constituting a certain area in the image, and one square represents one pixel. In addition, a rectangle indicated by hatching represents an edge pixel, and a rectangle without hatching represents a pixel other than the edge pixel (non-edge pixel).

  As shown in FIG. 2B, when the edge pixel group shown in FIG. 2A is connected to adjacent edge pixels by 8-connection, a loop that connects the edge pixels 201, 202, and 203 is generated. End up. When an edge pixel as shown in FIG. 2A is detected, it is necessary to suppress the occurrence of such a loop. Therefore, in the present embodiment, this problem is solved by using a known technique called the Hiditch thinning method before performing 8-connection. When the thinning is performed on the edge pixel group shown in FIG. 2A, the edge pixel 201 shown in FIG. 2B becomes a non-edge pixel. As a result, as shown in FIG. 2C, the edge pixels (204 to 207) are connected by one edge line having no loop.

  At this time, as shown by the edge pixel 208 in FIG. 2D, a pattern in which a plurality of edge lines meet at one place may occur. In such a case, the meeting edge lines are divided and treated as independent edge lines. In the example of FIG. 2D, there are three edge lines.

  In addition to the edge line, for example, the contour of an object region in the input image can be considered as the feature line. Here, the object refers to a character, an object image, or the like. As already mentioned, for the purpose of vectorizing a part of the input image, if a part of the input image is set to transparent color, the pixel at the boundary between the transparent color and the non-transparent color is the outline of the object. It becomes a pixel. The process of combining the contour pixels of the object into one line segment group can be realized by connecting adjacent pixels according to the same process as the edge line configuration process. The same applies to pixels on the boundary line between regions having different attributes in the input image.

  The feature line detection unit 1021 may divide the input image into regions and use the boundary lines as feature lines. Many methods are known for area division, such as those using the nearest neighbor method and those using an EM algorithm. In the present embodiment, a threshold value is set as a reference for density change, an edge pixel is detected by applying the Canny method, and a feature line is configured using the edge pixel.

  Returning to FIG. 1, next, the feature point extraction unit 1022 determines a sample point to be finally used among the sample points on the feature line as a feature point. The operation of the feature point extraction unit 1022 will be described with reference to FIGS.

  FIG. 3A shows a characteristic line configured by a certain area in the input image. The feature point extraction unit 1022 sets a plurality of sample points on the feature line. This sample point may be the edge pixel described above.

  A sample point group on a characteristic line in a certain section is shown in FIG. In FIG. 4A, reference numerals 401 to 405 indicate sample points. Starting from the end point of the feature line, adjacent sample points are sequentially deleted to generate an approximation function.

  First, as shown in FIG. 4B, starting from the sample point 401 that is the end point of the feature line, the sample point 402 is first deleted, and the sample point 401 and the sample point 403 are used as end points of the approximate curve. An approximate curve 406 is generated so as to approximate. Here, the approximation function is a cubic Bezier function generated by least square approximation, but other functions such as a B-spline function may be used. An error is calculated from the distance between the generated approximate curve 406 and the deleted sample point 402. If the error is within the threshold, the next sample point is deleted. In the present embodiment, the error threshold is set to one pixel. In FIG. 4B, the next process will be described on the assumption that the error is within the threshold.

  As shown in FIG. 4C, the next adjacent sample point 403 is deleted and the approximate curve 407 is approximated so that the sample point 402 and the sample point 403 are approximated by using the sample point 401 and the sample point 404 as end points of the approximate curve. Is generated. The error between the approximate curve 407 and the sample points 402 and 403 is calculated. In FIG. 4C, the next process will be described on the assumption that the error is within the threshold.

  As shown in FIG. 4D, next, the adjacent sample point 404 is deleted, and the approximate curve 408 is approximated so that the sample points 402, 403, 404 are approximated with the sample point 401 and the sample point 405 as the end points of the approximate curve. Generate. The error between the approximate curve 408 and the sample points 402, 403, 404 is calculated. Here, it is assumed that the error between the approximate curve 408 and the sample points 402 and 404 exceeds the threshold value. At this time, the approximate curve 408 is not employed, and the previously generated approximate curve 407 is employed as an effective approximate function.

  In this case, as shown in FIG. 4E, the sample point 401 that is the end point of the feature line is used as the starting point, the sample points 402 and 403 are deleted, and the sample point 404 that is the end point of the approximated curve 407 is used as the feature point. Sampling. That is, the sample point 401 and the sample point 405 which are the end points of the feature line, and the sample point 404 sampled as the feature point are employed as the first feature point. Here, when there are more sample points, the same process is repeated starting from the sample point 404 to determine the feature points. With the above processing, feature points are extracted in addition to the intersections of feature lines such as edge pixels 204 and 207 in FIG. 2C and feature lines such as edge pixel 208 in FIG. The feature points finally sampled with respect to the feature line in FIG. 3A are as indicated by white circles 301 and asterisks 302 in FIG. Of course, as long as the features of the image can be reproduced, other methods may be used such as sampling evenly between the end points of the feature lines to obtain the feature points.

  Returning to FIG. 1, the curved mesh generation unit 1023 divides the input image into a three-vertex curved mesh group so that the feature line can be reproduced. Processing performed by the curved mesh generation unit 1023 will be described with reference to FIG. 5 showing a flowchart of the processing. Of course, the main subject of the processing in each step shown in the flowchart of FIG. 5 is the curved mesh generation unit 1023.

  First, in step S501, a side to be curved is set. Here, in the feature point determined by the feature point extraction unit 1022, a line segment connecting the feature points adjacent on the feature line is set as an edge to be curved.

  Next, in step S502, by using the Delaunay triangulation with constraints using the side set in step S501 as a constraint condition, a three-vertex line mesh group in which the set side becomes a side of the mesh is generated. The constrained Delaunay triangulation is an algorithm that, when given a point group and a constraint condition, generates a straight line mesh with the given point group as a vertex and satisfying the constraint condition. Here, the constraint condition is a line segment connecting a part of the point group. Of course, other methods may be used as long as a three-vertex straight line mesh that reproduces the set side can be generated.

  In step S503, it is determined whether or not all sides to be curved have been curved. As a result of the determination, if all the sides to be curved have been curved, the processing according to the flowchart of FIG. 5 is completed, and the sides that have not yet been curved remain among the sides to be curved. If yes, the process proceeds to step S504.

  In step S504, one side that has not yet been curved is selected from the sides to be curved. In step S505, the side selected in step S504 is curved. In this curving, the sides of the mesh are curved so as to approximate the feature line. In the present embodiment, the cubic Bezier function generated by the feature point extraction unit 1022 is used as it is as a mesh side, so there is no need to recalculate the curve. FIG. 6 shows the result of such a three-vertex curve mesh group generation process performed on the feature line of FIG.

  Through the operation (processing) so far, the input image can be divided into a three-vertex curve mesh group representing a feature line. If the same purpose can be achieved, “the input image may be divided into three vertex curve mesh groups representing feature lines” by other methods.

  Returning to FIG. 1, the operation of the maximum color point number determination unit 103 will be described next. Here, in order to describe the operation of the maximum color point number determination unit 103, the shape information and color information in the three-vertex curve mesh will be described first. In the present embodiment, each three-vertex curve mesh is further divided into fine three-vertex meshes (sub-mesh), and the color of the pixel at the vertex of the divided sub-mesh is determined and assigned to the vertex. When rendering, the colors in each sub-mesh can be supplemented with bilinear interpolation to paint each mesh color.

  Here, shape information and color information necessary for configuring each mesh will be described. FIG. 7A shows shape information relating to a mesh (three-vertex curve mesh) composed of three vertices and connected between three vertices by a curve (all sides are expressed by a curve). Reference numerals 701, 702, and 703 denote vertices of the three-vertex curve mesh, and reference numerals 704 to 709 denote control points of the three-vertex curve mesh. This shape information is generated when the curved mesh generation unit 1023 generates a three-vertex curved mesh.

  FIG. 7B shows color information relating to the three-vertex curve mesh shown in FIG. Reference numerals 710 to 724 denote vertices (color points) of the sub-mesh in the three-vertex curve mesh, and each color point has color information. Here, the color point is described as having RGB values as color information. However, the color point may have color information in another color space such as CMYK. In addition, when the input image is a gray image, the color point holds the luminance information of the corresponding pixel as color information. The color point is derived by regularly dividing the Bezier patch. Here, 15 points are used as color points. However, the number of Bezier patches may be changed to change the number of color points. For example, in FIG. 8A, the number of divisions is 1, and the number of color points is 3. In FIGS. 8B, 8C, and 8D, the number of divisions is 2, 4, and 8, and the number of color points is 6, 15, and 45, respectively.

The number of divisions is a power of 2. If this number is called a division index, the division index increases as the number of divisions increases to 1, 2, 4, 8, 16, 32, 64. Corresponding to 0, 1, 2, 3, 4, 5, 6 respectively. FIG. 11 shows the correspondence between the division index, the number of divisions, and the number of color points. In FIG. 11, when the division index is a non-negative integer k, the division number n is n = 2 ^k and the color point number N is N = (n + 1) (n + 2) / 2.

  Here, it is desirable that the number of color points in one three-vertex curve mesh is changed according to the complexity of color change in the partial image included in the three-vertex curve mesh in the input image. That is, for a three-vertex curve mesh that has a more complex color change, set more color points, and for a three-vertex curve mesh that corresponds to a partial image whose color change is relatively complex (simple). It is desirable to set a minimum number of color points. Such adaptive control of the number of color points for each three-vertex curve mesh is desirable in terms of image quality and data capacity. Hereinafter, a configuration and a method for setting color points while adaptively determining the number of color points for each three-vertex curve mesh will be described.

  The maximum color point number determination unit 103 sets, for each three-vertex curve mesh, a maximum color point number N (initial value), which is the maximum number of color points that represent colors in the three-vertex curve mesh. Various methods can be considered as a method of setting the maximum number N of color points. In the present embodiment, a predetermined number is set as the maximum number N of color points common to the three vertex curve meshes. In the following, N = 45 for specific description, but the following description can be similarly applied even when N is another number. When the maximum number of color points N = 45, as shown in FIG. 8D, the division number n = 8 and the division index k = 3 (see FIG. 11). FIG. 7C shows the color information of the three-vertex curve mesh when the number of color points is 45. Similarly to FIG. 7B, the color points 730 to 774 in the figure each have color information. However, if numbers are assigned to all 45 points in FIG. 7 (c), the figure becomes complicated, so it is expressed as a larger figure than FIG. 7 (b), and reference numbers are assigned only to 27 of them. It is.

  Next, the color point candidate position setting unit 104 obtains the position (coordinates) of each color point (45 pieces) in the three-vertex curve mesh for each three-vertex curve mesh. For this processing, the above-described formulas 1 and 2 are formulas for defining the positions of the points in the three-vertex curve mesh (third-order Bezier patch) having vertices P1, P2 and P3 as described above. Is used. Taking FIG. 7A as an example, the vertices 701, 702, and 703 of the three-vertex curve mesh are P1, P2, and P3, respectively, and their coordinates are p1, p2, and p3, respectively. In addition, the coordinates of the control points 704 to 709 of the three-vertex curve mesh are c1, c2, c3, c4, c5, and c6, respectively. B (s, t, u) determined according to the respective combinations of the parameters s, t, u of the patch in Equation 1 that are changed within the range limiting the range of values of these parameters. The coordinates of the point position.

  For example, in the case of FIG. 7B, that is, when the number of divisions is 4, the values of s, t, and u are values obtained by equally dividing the possible range 0 to 1 into four. B (s, t, u) is obtained according to a combination that changes by ¼ and meets the limit of s + t + u = 1.

  B (1, 0, 0) is the position (coordinate) of the color point 710, and B (3/4, 1/4, 0) and B (3/4, 0, 1/4) are the color point 711 and It becomes the position (coordinates) 712. Also, B (1/2, 1/2, 0), B (1/2, 1/4, 1/4), and B (1/2, 0, 1/2) are color points 713, 714, The position (coordinates) of 715 is obtained. Similarly, the positions (coordinates) of the color points 716, 717, 718, 719 are B (1/4, 3/4, 0), B (1/4, 1/2, 1/4), B (1/4, 1/4, 1/2) and B (1/4, 0, 3/4). The positions (coordinates) of the color points 720, 721, 722, 723, and 724 are B (0, 1, 0), B (0, 3/4, 1/4), B (0, 1/2, 1/2), B (0, 1/4, 3/4), and B (0, 0, 1).

  Incidentally, when the maximum number of color points N = 45 corresponds to FIG. 7C and the number of divisions n = 8, the values of s, t, and u are changed by 1/8. The respective positions (coordinates) of the color points 730, 731, 733, 736, 740, 745, 751, 758, 766 are u = 0, s = 1- (i / 8), t = i / 8, It is obtained as B (s, t, 0) at i = 0, 1, 2,. If the positions (coordinates) of the color points 730, 732, 735, 739, 744, 750, 757, 765, 774 are t = 0, s = 1- (i / 8), u = i / 8, B (s, 0, u) at i = 0, 1, 2,. For the positions (coordinates) of the color points 766, 767, 768, 769, 770, 771, 772, 773, 774, s = 0, t = 1- (i / 8), u = i / 8, where i = B (0, t, u) at 0, 1, 2,. In addition, if there are three color points 733, 734, and 735, all the positions (coordinates) are s = 3/4, and B (3/4, 1/4, 0), B ( 3/4, 1/8, 1/8) and B (3/4, 0, 1/4). If it is the position (coordinate) of the color point 753, the position (coordinate) of the color point 751 is B (1/4, 3/4, 0), and the position (coordinate) of the color point 757 is B (1/4, 0). , 3/4) but B (1/4, 1/2, 1/4). If it is the position (coordinate) of the color point 763, the position (coordinate) of the color point 758 is B (1/8, 7/8, 0), and the position (coordinate) of the color point 765 is B (1/8, 0). , 7/8) and B (1/8, 1/4, 5/8).

  Thus, in general, when the number of divisions is n, the values of s, t, and u are each changed by 1 / n from 0 to 1, and the limit of s + t + u = 1 is satisfied. N = (n + 1) (n + 2) / By obtaining B (s, t, u) according to two combinations, the positions (coordinates) of (n + 1) (n + 2) / 2 color points can be obtained. s = 1− (j / n), where for j = 0, 1, 2,..., n, the combination of t and u satisfying t + u = j / n is 1+ (n -J) There are streets. That is, there are 1+ (n−j) B (1− (j / n), t, u). After all, when the number of divisions is n, the combination of s, t, u satisfying s + t + u = 1 is (n + 1) + n + (n−1) + (n−2) +... + 2 + 1 = (n + 1) (n + 2) / 2 = N ways exist. These (n + 1) (n + 2) / 2 = N B (s, t, u) give the coordinates of the maximum number of color points N = (n + 1) (n + 2) / 2.

It should be noted that the three-vertex curve mesh does not necessarily have a curve between all vertices, and generally there exists a vertex that is a straight line. For example, if the vertex P1 and the vertex P2 are a straight line, the side connecting the vertex 701 (P1) and the vertex 702 (P2) in FIG. 7A is a straight line. In this case, the control point 704 and the control point 705 are on this straight line, and their coordinates c1 and c2 are respectively c1 = (2p1 + p2) / 3 (Formula 3)
c2 = (p1 + 2p2) / 3 (Formula 4)
As described above, the above-described (Expression 1) and (Expression 2) may be used.

In addition, when the vertex P3 and the vertex P1 are also straight lines, that is, when only the vertex P2 and the vertex P3 are curved, the control point 708 and the control point 709 are further connected to the vertex P3 and the vertex P1. The coordinates c5 and c6 are respectively c5 = (2p3 + p1) / 3 (Formula 5)
c6 = (p3 + 2p1) / 3 (Formula 6)
As described above, the above-described (Expression 1) and (Expression 2) may be used.

Further, when the vertex P2 and the vertex P3 are also straight lines, that is, when all the vertices are straight lines, the control point 706 and the control point 707 are on the straight line between the vertex P2 and the vertex P3. And their coordinates c3 and c4 are respectively c3 = (2p2 + p3) / 3 (formula 7)
c4 = (p2 + 2p3) / 3 (Formula 8)
As described above, the above-described (Expression 1) and (Expression 2) may be used. However, (Formula 1) and (Formula 2) are added based on the conditions of (Formula 3) to (Formula 8) to the above (Formula 1) and (Formula 2) and s + t + u = 1. Simplify
B (s, t, u) = s.p1 + t.p2 + u.p3 (Formula 9)
Therefore, when all the vertices of the three-vertex curve mesh are straight lines, the position (coordinate) of each color point may be obtained based on the above (Equation 9). In this case, the position (coordinates) of each color point can be obtained with a much smaller calculation amount than when using the above (Expression 1) and (Expression 2).

  Returning to FIG. 1, the color information extraction unit 105 next acquires color information of each color point in the three-vertex curve mesh for each three-vertex curve mesh. Since the coordinates of the 45 color points in the three-vertex curve mesh are obtained by the color point candidate position setting unit 104, the color information extraction unit 105 converts the pixel values at the coordinates of the color points in the input image into the color. Acquired as point color information. Note that the coordinates obtained by the color point candidate position setting unit 104 are not necessarily integer values, but generally deviate from the center position of the pixel in the input image. For the deviation from the center position of this pixel, an interpolation method called a so-called nearest neighbor method, that is, rounding off the decimal part of the coordinates to an integer, the pixel of the input image at the corresponding position is converted into an integer. A pixel value is used as color information of the color point. Note that the interpolation method is not limited to the nearest neighbor method. Using the pixel values of the surrounding four pixels surrounding the position on the input image indicated by the decimal part of the coordinates, the color information of the color point may be obtained by using an interpolation value obtained by a known bi-linear interpolation method. Of course, an interpolation value obtained by a known bi-cubic interpolation method using 16 neighboring pixels may be used. Bilinear interpolation and bi-cubic interpolation are considered to have higher image quality than bilateral interpolation, but the amount of computation increases in this order. There is a trade-off between the calculation amount and the calculation amount.

  Next, the operation of the allowable value setting unit 106 will be described. As described above, if the number of color points in the three-vertex curve mesh is small, the data capacity is small. On the other hand, the color change complexity of the image included in the three-vertex curve mesh is as faithful as possible. In order to be able to reproduce, it is necessary to express with a large number of color points. For this reason, if the number of color points is reduced more than necessary, the reproduced image has a greater image quality degradation than the original image. The allowable value setting unit 106 determines an allowable error value as a reference value for suppressing an unnecessary reduction in reducing the number of color points. In the present embodiment, the allowable value setting unit 106 sets the allowable threshold value of the error value per color point in the maximum number of color points as the fixed value Th. For example, Th = 1.0. This value is used by the determination unit 1074 in the subsequent allowable color point information generation unit 107.

  The permissible color point information generation unit 107 determines a color point to be set for the three-vertex curve mesh for each three-vertex curve mesh, and includes a color point candidate reduction unit 1071, a color information interpolation unit 1072, and an error generation unit. 1073, a determination unit 1074, and a color point information determination unit 1075. Hereinafter, the operation of the allowable color point information generation unit 107 for one three-vertex curve mesh will be described. However, in practice, the allowable color point information generation unit 107 performs the processing described below on each of the three vertex curve meshes.

  The color point candidate reducing unit 1071 is a three-vertex curve mesh of the number of color points corresponding to the division index (k−1), where k is the division index corresponding to the maximum number of color points N set by the maximum color point number determination unit 103. Is obtained in the same manner as the color point candidate position setting unit 104. For example, when N = 45, k = 3 as described above. Therefore, the positions of 15 color points (color points 710 to 724 shown in FIG. 7B) corresponding to k = 2 are obtained. The color point candidate reduction unit 1071 then differs from the number of color points corresponding to the division index (k−1) out of the number of color points corresponding to the division index k (maximum number of color points) to the color points whose positions are different. Are determined as reduction candidate color points. Note that “the position is the same between the color points” is not limited to “exactly the same”, but may be “the same” as long as the position difference is within an allowable range. The operation of the color point candidate reduction unit 1071 will be described using FIG. 10 as an example. In FIG. 10, the sides between the vertices are straight lines for simplicity of explanation, but the following explanation can be applied in the same manner even if the sides between the vertices are curves.

  FIG. 10A shows color points when the number of divisions = 1, that is, when the division index is 0. In this case, the number of color points is three. FIG. 10B shows color points when the number of divisions = 2, that is, when the division index is 1. In this case, the number of color points is six. FIG. 10C shows color points when the number of divisions = 4, that is, when the division index is 2. In this case, the number of color points is 15. FIG. 10D shows the color points when the number of divisions is 8, that is, when the division index is 3, and in this case, the number of color points is 45. In addition, FIG.10 (d) is expressed large compared with Fig.10 (a)-(c). As in the case of FIG. 7C, when 45 color points are shown in the figure, the figure is more complicated than the expression of the color points in FIGS. 10A to 10C. This is to prevent it from becoming difficult to see.

  Here, as shown in FIG. 10A, the three color points when the number of divisions = 1 are indicated by white circles. FIG. 10B shows six color points in the case where the number of divisions = 2, but the same color points as those shown in FIG. 10A are indicated by white circles, and FIG. Color points different from the color points shown in FIG. FIG. 10 (c) shows 15 color points when the number of divisions = 4. The same color points as those shown in FIG. 10 (a) are shown as white circles, and FIG. 10 (b). The same color point as the color point is indicated by a double circle, and the color point different from the color point shown in FIGS. 10A and 10B is indicated by a black circle. FIG. 10D shows 45 color points when the number of divisions is 8, but the same color points as those shown in FIG. 10A are indicated by white circles and FIG. 10B. The same color point as that shown in FIG. 10C is indicated by a double circle, and the same color point as that shown in FIG. 10C is indicated by a black circle. The colors shown in FIG. 10A, FIG. 10B, and FIG. Color points that are different from the dots are shown as stars.

  As described above, the color point candidate reducing unit 1071 has the number of color points corresponding to the division index k (maximum number of color points), where k is the division index corresponding to the maximum color point number N set by the maximum color point number determination unit 103. Among them, the number of color points corresponding to the division index (k−1) and the color points having different positions are determined as reduction candidate color points. In the case of the maximum number of color points N = 45, the arrangement of 45 color points in the 3-vertex curve mesh is as shown in FIG. 10 (d), and within the 15-vertex curve mesh of 15 color points corresponding to the division index = 2. The arrangement in FIG. 10C is as shown in FIG. 10C. Among the color points shown in FIG. 10D, the color points whose positions are different from those shown in FIG. 10C are the color points 1005 and 1006. In this case, the color point candidate reduction unit 1071 determines a star-shaped color point as a reduction candidate color point.

  Although details will be described later, in this embodiment, if the determination unit 1074 determines that the image quality of the three-vertex curve mesh is higher than the reference even if the color point determined as the reduction candidate color point is deleted, The point candidate reduction unit 1071 further determines reduction candidate color points from color points that have not been determined as reduction candidate color points. For example, as shown in FIG. 10 (d), the color point candidate reducing unit 1071 starts processing with 45 color points set in the three-vertex curve mesh, and as a result, the star-shaped color points are deleted. If the determination unit 1074 determines that the image quality of the three-vertex curve mesh is equal to or higher than the reference, the star-shaped color point is deleted, and as a result, the color points on the three-vertex curve mesh are shown in FIG. As shown in FIG. Then, the color point candidate reduction unit 1071 performs the same processing, so that among the 15 color points, the color points (black circle color points) whose positions are different from the 6 color points in FIG. It is determined as a reduction candidate color point. If the determination unit 1074 determines that the image quality of the three-vertex curve mesh is higher than the standard even if the black circle color point is deleted, the black circle color point is deleted, and as a result, the three-vertex curve mesh As shown in FIG. 10B, there are six color points.

  Returning to FIG. 1, the color information interpolation unit 1072 uses the color point color information determined by the color point candidate reduction unit 1071 as the reduction candidate color point in the three-vertex curve mesh as the reduction candidate color point in the three-vertex curve mesh. It is obtained by interpolation from the color information of the color points that have not been determined. For example, when the star-shaped color point is determined as the reduction candidate color point in FIG. 10D, the color information of the star-shaped color point includes the white circle color point, the double circle color point, the black circle color point, and the like. (For example, for each star color point, the color information of the two closest color points among the white circle color point, double circle color point, and black circle color point) Is the color information of the star-shaped color point). More specifically, in the case of FIG. 10D, the color information of the color point 1005 is an average value of the color information of the color point 1001 and the color information of the color point 1004, and the color information of the color point 1006 is the color information. The average value of the color information of the point 1004 and the color information of the color point 1003 is set. This is because the color point 1005 is in the middle of the side between the color point 1001 and the color point 1004, and the color point 1006 is in the middle of the side between the color point 1004 and the color point 1003. Easy to understand.

  In addition to the star-shaped color point, when the black circle color point is also determined as the reduction candidate color point, the color information of the black circle color point is used for the white circle color point that is not the reduction candidate color point. It is obtained by interpolation from the color information and the color information of the double-circle color point (for example, for each black-circle color point, the two round-point color points of the white-circle and double-circle color points The average value of the color information is set as the color information of the color point of the black circle). Focusing on FIG. 10C, for example, the color information of the color point 1004 is an average value of the color information of the color point 1001 and the color information of the color point 1003. This can be easily understood because the color point 1004 is in the middle of the side between the color point 1001 and the color point 1003. After obtaining the color information of the black circle color point, in addition to the white circle color point and the double circle color point, the black circle color point for which the color information was obtained by interpolation is also used. The color information of the color point of the shape is obtained by interpolation.

  Similarly, in the case where the double-circle color point is also a reduction candidate color point in addition to the star-shaped color point and the black circle color point, the color information of the double-circle color point is first displayed as the reduction candidate. It is obtained by interpolation from the color information of the white circle color point that is not the color point. Focusing on FIG. 10B, for example, the color information of the color point 1003 is an average value of the color information of the color point 1001 and the color information of the color point 1002. This can be easily understood because the color point 1003 is in the middle of the side between the color point 1001 and the color point 1002.

  Here, the number of color points in a certain division index kw (0 <kw <k, where k is a division index corresponding to the maximum number of color points, hereinafter also referred to as the maximum division index) is determined as a reduction candidate color point. Focus on color points. The color information at the position B (l1, m1, q1) of the reduction candidate color point is the position in the color information (B (l0, m0, q0) of two color points that are not the reduction candidate color points in the nearest vicinity. Color information, color information of position in B (l2, m2, q2)). However, in l1, m1, and q1, only one of l0 = l1 = l2, m0 = m1 = m2, and q0 = q1 = q2 is established. Further, including the two not established, l1 = (l0 + l2) / 2, m1 = (m0 + m2) / 2, and q1 = (q0 + q2) / 2 are established.

Next, for each color point determined by the color point candidate reduction unit 1071 as the reduction candidate color point, the error generation unit 1073 extracts the color information obtained by interpolation by the color information interpolation unit 1072 and the color information extraction. The unit 105 obtains a difference (error) between the color point and the color information acquired from the input image. Various methods can be considered as a method for obtaining the difference. Here, as an example, for each color point determined as a reduction candidate color point, the color information interpolated by the color information interpolation unit 1072 for the color point, The information extraction unit 105 obtains the square of the difference between the color point and the color information acquired from the input image as the “difference”. Then, the error generation unit 1073 divides the sum of the differences obtained for the respective color points determined as the reduction candidate color points by the maximum number of color points, and an error value θ _k−1 per color point based on the maximum number of color points ( Find the squared difference. Since the difference is 0 for color points that are not reduction candidate points, the error value θ _k−1 per color point based on the aforementioned maximum number of color points is not affected.

Next, the determination unit 1074 calculates the difference between the “error value θ _k−1 per color point” obtained by the error generation unit 1073 and the error allowable value (fixed value Th) set by the allowable value setting unit 106. A comparison is made to determine whether or not the error value θ _k−1 is smaller than the allowable error value. As a result of this determination, if the error value θ _k−1 is smaller than the allowable error value, it is determined that the number of reduction candidate color points can be further increased, and further reduction candidate color points are selected from the color points of the maximum number N of color points. In order to determine, the operation is performed by the color point candidate reduction unit 1071 and subsequent operations.

Accordingly, the color point candidate reduction unit 1071 sets 3 of the number of color points corresponding to the division index (k−2), where k is the division index corresponding to the maximum number N of color points set by the maximum color point number determination unit 103. The position in the vertex curve mesh is obtained in the same manner as the color point candidate position setting unit 104. The color point candidate reduction unit 1071 then selects the non-reduction candidate color points (that is, the number of color points corresponding to the division index (k−1)) among the number of color points corresponding to the division index k (the maximum number of color points). Among the target non-reduced candidate color points, the number of color points corresponding to the division index (k−2) and the color points whose positions are different are determined as reduction candidate color points. Then, the color information interpolation unit 1072 has not determined the color information of the color point determined as the reduction candidate color point by the color point candidate reduction unit 1071 in the three-vertex curve mesh as the reduction candidate color point in the three-vertex curve mesh. Obtained by interpolation from color point color information. Then, the error generation unit 1073 obtains an error value θ _k−2 (difference square value) per color point determined by the color point candidate reduction unit 1071 as the reduction candidate color point. The determination unit 1074 performs a size comparison between the error value θ _k−2 and the allowable error value (fixed value Th). If the error value θ _k−2 is smaller than the allowable error value, the number of reduction candidate color points is further increased. The color point candidate reduction unit 1071 is caused to perform an operation after the color point candidate reduction unit 1071 to determine further reduction candidate color points from the color points having the maximum number N of color points.

In this way, the process of “determining the error value θ _k−m (m is an integer equal to or greater than 1)” by the color point candidate reduction unit 1071, the color information interpolation unit 1072, and the error generation unit 1073 is performed as m = 1, 2,. When the error value θ _k−1 (1 ≦ l ≦ k) becomes equal to or greater than the allowable error value, the determination unit 1074 determines that it is not appropriate to further increase the number of candidate color points to be reduced. To the color point information confirmation unit 1075.

  The color point information determination unit 1075 has been moved here because it has been found that an appropriate condition cannot be maintained with the division index (in the above example, (km)) examined immediately before. Consider. That is, a division index larger by 1 than the division index examined immediately before is handled as an appropriate division number (hereinafter referred to as the final division number) in the three-vertex curve mesh. The position of the color point at the number of color points corresponding to the final division number and the color information at those positions are used as the color point information in the three-vertex curve mesh. If the generation of the color point information has been completed for all the three vertex curve meshes generated by the mesh generation unit 102, the color point information determination unit 1075 notifies the mesh information output unit 108 to that effect. On the other hand, when there remains a three-vertex curve mesh for which color point information has not yet been generated, the color point information determination unit 1075 determines the maximum number of color points for the three-vertex curve mesh whose color point information is not yet determined. The operation after the unit 103 is performed.

  In the mesh information output unit 108, information on the number of the three-vertex curve meshes and the vertices of each of the three-vertex curve meshes obtained by the operation described above, the number of color points and the positions of the color points in each of the three-vertex curve meshes, and their positions The mesh information including the color information at is output. That is, the information of the three-vertex curve mesh obtained by the processing so far is output as vector information.

  Processing by each functional unit after the maximum color point number determination unit 103 in FIG. 1 will be described with reference to the flowchart in FIG. Since the processing according to the flowchart of FIG. 9 is processing for one three-vertex curve mesh, actually, the processing according to the flowchart of FIG. 9 is performed for each three-vertex curve generated by the mesh generation unit 102. Will be done on the mesh. Further, the processing according to the flowchart of FIG. 9 is different from the processing described above, but all are substantially the same processing.

<Step S9010>
The maximum color point number determination unit 103 sets the maximum division index of the three-vertex curve mesh. Here, the maximum division index = 3.

<Step S9020>
The maximum color point number determination unit 103 sets the maximum color point number N corresponding to the maximum division index set in step S9010. In the case of the maximum division index = 3, N = 45 from FIG. Next, the color point candidate position setting unit 104 obtains the positions (coordinates) of the 45 color points in the three-vertex curve mesh, and the color information extraction unit 105 determines the positions of the 45 color points. Color information is obtained from the input image.

<Step S9030>
The allowable value setting unit 106 determines the allowable error value Th as a reference value for suppressing an unnecessary reduction in reducing the number of color points. Here, Th = 1.0.

<Step S9040>
The color point candidate reduction unit 1071 sets the maximum division index = 3 set in step S9010 to the variable A indicating the current division number.

<Step S9050>
The color point candidate reduction unit 1071 sets the value of the variable A to the variable B indicating the previous division number.

<Step S9060>
The color point candidate reduction unit 1071 decrements the value of the variable A by one.

<Step S9070>
The color point candidate reduction unit 1071 determines whether or not the value of the variable A is 0 or more. As a result of the determination, if the value of variable A is 0 or more, the process proceeds to step S9080, and if the value of variable A is less than 0, the process proceeds to step S9120.

<Step S9080>
The color point candidate reduction unit 1071 obtains the positions of the number of color points corresponding to the division index A in the three-vertex curve mesh in the same manner as the color point candidate position setting unit 104. Then, the color point candidate reduction unit 1071 determines, among the number of color points corresponding to the division index B, the color points corresponding to the division index A and the color points whose positions are different as the reduction candidate color points. .

<Step S9090>
The color information interpolation unit 1072 uses the color information of the color point determined as the reduction candidate color point by the color point candidate reduction unit 1071 in the three-vertex curve mesh, and the color point not determined as the reduction candidate color point in the three-vertex curve mesh. It is obtained by interpolation from the color information.

<Step S9100>
For each color point determined by the color point candidate reduction unit 1071 as a reduction candidate color point, the error generation unit 1073 uses the color information obtained by the color information interpolation unit 1072 by interpolation, and the color information extraction unit 105 A difference between the color point and the color information acquired from the input image is obtained. Then, the error generation unit 1073 divides the sum of the differences obtained for the respective color points determined as the reduction candidate color points by the maximum number of color points, and an error value θ _k−1 per color point based on the maximum number of color points ( Find the squared difference.

<Step S9110>
The determination unit 1074 determines whether or not the error value θ _k−1 is smaller than the allowable error value. If it is determined that the error value θ _k−1 is smaller than the allowable error value, the process proceeds to step S9050. If the error value θ _k−1 is equal to or greater than the allowable error value, the process proceeds to step S9120. Proceed to

<Step S9120>
The color point information determination unit 1075 uses the color point position at the number of color points corresponding to the final division number B and the color information at those positions as color point information in the three-vertex curve mesh. Of course, the color point information may include other information related to the color point.

<Modification of First Embodiment>
Each unit shown in FIG. 1 may be configured by hardware, but may be implemented as software (computer program). In this case, the software is installed in a memory of a general computer such as a PC (personal computer). When the CPU of the computer executes the installed software, the computer realizes the functions of the above-described image processing apparatus (functions of each unit illustrated in FIG. 1). That is, this computer can be applied to the above-described image processing apparatus. A hardware configuration example of a computer applicable as the image processing apparatus according to the first embodiment will be described with reference to FIG.

  The CPU 1501 executes processes using computer programs and data stored in the RAM 1502 and the ROM 1503, thereby controlling the operation of the entire computer and executing the above-described processes described as being performed by the image processing apparatus. Control. In other words, each process described above as being performed by each unit shown in FIG. 1 is executed or controlled.

  The RAM 1502 has an area for temporarily storing computer programs and data loaded from the external storage device 1507 and the storage medium drive 1508, data received from the external device via the I / F (interface) 1509, and the like. Further, the RAM 1502 has a work area used when the CPU 1501 executes various processes. That is, the RAM 1502 can provide various areas as appropriate. The ROM 1503 stores computer setting data, a boot program, and the like.

  A keyboard 1504 and a mouse 1505 are examples of a user interface that can input various instructions to the CPU 1501 when operated by a computer operator. For example, various types of information described as being set, such as the above maximum division index, may be input by the user operating the keyboard 1504 or the mouse 1505.

  The display device 1506 is configured by a CRT, a liquid crystal screen, or the like, and can display a processing result by the CPU 1501 as an image, text, or the like. For example, the input image can be displayed, or a three-vertex curve mesh group can be displayed.

  The external storage device 1507 is a large-capacity information storage device represented by a hard disk drive device. The external storage device 1507 stores an OS (operating system), computer programs and data for causing the CPU 1501 to realize the functions of the respective units shown in FIG. 1, data of the input image, information described as known information, and the like. Has been. Computer programs and data stored in the external storage device 1507 are appropriately loaded into the RAM 1502 under the control of the CPU 1501 and are processed by the CPU 1501.

  The storage medium drive 1508 reads a computer program and data recorded on a storage medium such as a CD-ROM or DVD-ROM, and outputs the read computer program or data to the external storage device 1507 or the RAM 1502. Note that part or all of the information described as being stored in the external storage device 1507 may be recorded on this storage medium and read by this storage medium drive 1508.

  The I / F 1509 is for connecting an external device to a computer. For example, a device such as a digital camera for acquiring the input image is connected to the I / F 1509, and the input image is acquired from the device via the I / F 1509 to the RAM 1502 or the external storage device 1507. good. A bus 1510 connects the above-described units.

  In the above configuration, when the computer is turned on, the CPU 1501 loads the OS from the external storage device 1507 to the RAM 1502 in accordance with the boot program stored in the ROM 1503. As a result, an information input operation can be performed via the keyboard 1504 and the mouse 1505, and a GUI can be displayed on the display device 1506. When the user operates the keyboard 1504 or the mouse 1505 and inputs an activation instruction for an image processing application program stored in the external storage device 1507, the CPU 1501 loads the program into the RAM 1502 and executes it. As a result, the computer functions as the image processing apparatus.

  The application program for image processing executed by the CPU 1501 basically includes functions corresponding to the respective components shown in the respective processing units in FIG. 1 and the respective steps in FIG.

[Second Embodiment]
As described in the first embodiment, in the first embodiment, the maximum number of color points is set to N = 45. However, this is for specific description, and in the first embodiment, N = 45. N is not a specialized embodiment, and N may be another value.

  In the first embodiment, the maximum number of color points N is the same for all three vertex curve meshes. However, for each three vertex curve mesh, an appropriate maximum number of color points N corresponding to the area of the three vertex curve mesh is set. You can set it. For example, a value that is smaller than the area of the three-vertex curve mesh (or a value that becomes larger or smaller depending on the size of the area) or the longest line among the three lines that connect the three vertices of the three-vertex curve mesh When a value smaller than the length is used as the division number, the corresponding number of color points may be used. The area of the three-vertex curve mesh is the number of pixels included in the three-vertex curve mesh, and is composed of the three vertices obtained from the coordinate values of the three vertices constituting the three-vertex curve mesh. A triangular area may be regarded as the area of the three-vertex curve mesh.

  For example, when the area of a certain three-vertex curve mesh (or a value that becomes larger or smaller depending on the size of the area) is 42, the maximum number of color points may be set to 15, for example. Further, when the longest line length among the three lines connecting the three vertices of the three-vertex curve mesh is 7, the number of color points corresponding to the division number 4 may be set to 15, for example. Focusing on the area, a larger maximum of 3 color curve meshes and a smaller maximum of 3 color curve meshes, and a smaller maximum color point of 3 vertex curve meshes. It can be set to an adaptive value corresponding to the number of pixels that must be expressed by. Focusing on the longest line length among the three lines connecting the three vertices of the three-vertex curve mesh, the lines are divided redundantly compared to the case where the number of color points corresponds to a fixed number of divisions. Instead, the maximum number of color points can be obtained by dividing by an adaptive number of divisions.

[Third Embodiment]
In the first embodiment, the error tolerance value Th has been described as 1.0, but this is for specific description, and the first embodiment specializes in Th = 1.0. Instead of the embodiment, Th may be another value.

  The value of Th is not limited to being common to all three vertex curve meshes, and may be determined adaptively for each three vertex curve mesh according to the area of the three vertex curve mesh. For example, a fixed value that does not depend on the three-vertex curve mesh may be determined as a value that is substantially inversely proportional to the area of the three-vertex curve mesh, such as a value obtained by dividing the fixed value by the area of the three-vertex curve mesh. In this case, it can be considered that the allowable value is adaptively determined as a value that also considers the viewpoint of how much area is used to represent the area per color point in the maximum number of color points. . Alternatively, even if the error allowable value itself is the same as that of the first embodiment, the determination unit 1074 (step S9110) performs a simple comparison between the error per color point and the error allowable value (fixed value Th). Instead, the determination may be made in consideration of the area of the three-vertex curve mesh. That is, the error value may be converted to a value substantially proportional to the area of the three-vertex curve mesh by multiplying the error value by the area, and the magnitude comparison between the converted error value and Th may be performed. Absent. Further, the error generation unit 1073 (step S9100) may be configured so that the error value itself is converted to a value substantially proportional to the area of the three-vertex curve mesh.

(Other examples)
The present invention supplies a program that realizes one or more functions of the above-described embodiments to a system or apparatus via a network or a storage medium, and one or more processors in a computer of the system or apparatus read and execute the program This process can be realized. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

  102: Mesh generation unit 104: Color point candidate position setting unit 105: Color information extraction unit 107: Allowable color point information generation unit

Claims (8)

The input image is divided into units of the mesh so that the detection line detected from the input image is composed of three vertices and the side connecting the three vertices is one side of a mesh that is a straight line or a curve. An image processing device for determining each vertex of the sub-mesh for dividing the sub-mesh into a plurality of sub-mesh,
From the color information of the partial vertex group that is a part of the candidate vertex group set as a candidate for the vertex of the sub-mesh that divides the mesh, and the color information of the other vertex group excluding the partial vertex group in the candidate vertex group A calculation means for obtaining a difference between the obtained color information of the partial vertex group,
When the difference is smaller than a specified value, the other vertex group is set as the candidate vertex group and then the calculation means is operated.When the difference is equal to or larger than a specified value, the candidate vertex group is An image processing apparatus comprising: determining means for determining the vertex of each sub-mesh that divides the mesh.   The calculation means includes color information of a partial vertex group that is a part of a candidate vertex group set as a candidate for a vertex of a sub-mesh that divides the mesh, and other vertices excluding the partial vertex group in the candidate vertex group The image processing apparatus according to claim 1, wherein a difference from the color information of the partial vertex group obtained by interpolation from the color information of the group is obtained. Furthermore,
The image processing apparatus according to claim 1, wherein the determination unit includes a unit that outputs information on the candidate vertex group determined as the vertexes of each sub-mesh that divides the mesh.   4. The image processing apparatus according to claim 1, wherein the initial value of the number of candidate vertices and the specified value are the same for each mesh. 5.   4. The image processing apparatus according to claim 1, wherein the initial value of the number of candidate vertices and the specified value differ according to the area of each mesh. 5.   4. The image processing apparatus according to claim 1, wherein an initial value of the number of candidate vertices differs according to a length of one longest side in each mesh. 5. The input image is divided into units of the mesh so that the detection line detected from the input image is composed of three vertices and the side connecting the three vertices is one side of a mesh that is a straight line or a curve. An image processing method performed by an image processing apparatus that determines each vertex of the sub-mesh to divide the sub-mesh into a plurality of sub-mesh,
The calculation means of the image processing apparatus includes color information of a partial vertex group that is a part of a candidate vertex group set as a candidate for a vertex of a sub-mesh that divides the mesh, and the partial vertex group in the candidate vertex group A calculation step for obtaining a difference between the color information of the partial vertex group obtained from the color information of the other vertex group excluding
When the determination unit of the image processing apparatus has the difference smaller than a predetermined value, the calculation step is operated after setting the other vertex group as the candidate vertex group, and the difference is equal to or larger than a predetermined value. Comprises determining a candidate vertex group as a vertex of each sub-mesh that divides the mesh.   A computer program for causing a computer to function as each unit of the image processing apparatus according to any one of claims 1 to 6.

Images