JPH06111016A - Area dividing method for color image - Google Patents

Abstract

PURPOSE:To execute a better area division by modeling the feature of a distribution on a color space estimated from the reflection model of an object as a curve for passing through 'object color' from 'black' and reaching 'white', and executing clustering on a parameter space for distinguishing this curve. CONSTITUTION:The feature of a distribution on a color space estimated from the reflection model of an object is modeled as a curve for passing through 'object color' from 'black' and reaching 'white'. By mapping each picture element onto a parameter space for distinguishing its curve, such a distribution as each picture element in the same area is localized in a specific place is obtained, and clustering is executed on its space. Also, since cluster representative points 1-3 correspond to one piece of half ellipse for representing the area, after a division is executed in such a manner, for which half ellipse each picture element belongs is decided, labeling is executed and the area division is finished. Thus, the picture element in the same area comes to be localized easily in a specific place on the parameter space.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a color image area dividing method, and more particularly to extracting a specific area (so-called clipping) or area corresponding to each object in an image when processing image data of a printing plate. The present invention relates to a color image area dividing method suitable for selective color tone correction or for structurally describing an image and for use in, for example, structure extraction coding used for data compression or the like.

[0002]

2. Description of the Related Art A clustering method on a color space is one of typical methods for dividing a region of a digital color image.

As shown in FIG. 1, this is done by mapping pixels in an image plane into a color space (for example, RGB color space) and using a cluster boundary set according to the distance from the cluster centroid in that space. This is a method in which each pixel is classified and labeled with 1, 2, 3, etc. by clustering, and a region-divided image is obtained by inverse mapping on an image plane to perform region division.

[0004]

According to this conventional method, when the pixels in the same area are always localized in a specific place on the color space, the area can be properly divided, but it is not so in the actual image. In many cases. Therefore, even though they are the same area, they are divided into a plurality of areas, different areas are regarded as one area, and there are many problems in using them independently.

The present invention has been made to solve the above-mentioned conventional problems, and provides a new clustering method based on a model of distribution of an image in a color space, from a viewpoint completely different from the conventional method. The purpose is to do.

[0006]

According to the present invention, when a color image is divided into regions, the characteristics of the distribution in the color space estimated from the reflection model of the object are changed from "black" to "object color" to "white". By modeling as a curve leading to, and mapping each pixel on a parameter space that distinguishes the curve, a distribution in which pixels in the same region are localized at a specific location is obtained, and clustering is performed on that space. By performing the above, the above-mentioned object is achieved.

[0007]

In general, an image is photographed by illuminating an object having a three-dimensional spread with illumination light, passing the reflected light through an appropriate lens system, and mapping the reflected light on a two-dimensional plane.

One object has the same spectral reflectance, that is, the same color, and various gradations occur depending on the relationship between the angle of the illumination light and the surface of the object and the position of the viewpoint. However, this gradation changes along a certain curve in the color space. The reason is that in the area of the object surface where the line of sight to the object is close to the exit angle determined by the incident angle when the illumination light is incident on the surface of the object, which is shaded and the area to which the illumination light does not reach become black. In many cases, it is a specular reflection state, which is a so-called catch light place. In this part, the illumination light reflects light of all wavelengths with a certain reflectance, so it becomes the same “white” as the illumination light, and in the middle part, the object surface is diffusely reflected and the reflectance is different with respect to the line-of-sight angle. Since it has a distribution, pixels in the same area (same object) are
In the color space (for example, L * u * v * space), it is considered that there is a tendency to concentrate on the curve of black → object color → white as shown in FIG. You can confirm that you are doing. Here, L * represents lightness, the distance from the L * axis in the u * v * plane represents saturation, and the rotation angle represents hue.

Therefore, when viewed from the entire image, there are a plurality of banana-shaped distributions, such as a bent ellipsoid as shown in FIG. 2, corresponding to the number of regions (the number of objects), as shown in FIG. Become.

However, in the conventional method, since these matters are not taken into consideration at all, as shown by a broken line in FIG. 4, even though the same area (object) is divided into a plurality of areas, or in FIG. As shown by the broken line, different regions (objects) may be regarded as one region, and the conventional clustering method is incomplete as a region division method.

In the present invention, the problem of the conventional method is solved by performing clustering so as to separate a plurality of bent ellipsoidal distributions corresponding to each region based on the above-described distribution model.

The clustering method of the present invention will be described in detail below.

Now, as shown in FIG. 6, the color space (for example, L * u
Consider a group of curves passing through a point A representing "black" and a point B representing "white" on the * v * space). Then any pixel becomes
Although represented as one point in the color space, it always belongs to one curve in the curve group. That is, one curve corresponds to one pixel.

Since two parameters for distinguishing each curve are sufficient, each pixel can be represented by mapping to one point on the two-dimensional parameter space. As an example of this curve group, consider a set of semi-ellipses passing through points A and B as shown in FIG. 7, and move the position of the origin on the lightness axis according to L = L * -50 ... (1) The range of L * is 0 ≦ L * ≦ 10
Normalize to 0.

Here, since the plane where one semi-ellipse intersects the L = 0 plane is uniquely determined, the semi-elliptic curve group has parameters (u *, v *) coordinate values (μ, ν) of this intersection. Can be converted.

Therefore, each pixel can be mapped on the L = 0 plane by a kind of mapping operation along the semi-ellipse.

When the distribution of the actual image on the color space is as shown in, for example, FIG. 8, such mapping results in the distribution on the (μ, ν) plane as shown in FIG. It is possible to obtain a localized distribution for each region. In this way, efficient mapping can be performed by mapping the feature space and then using a normal clustering method.

[0018]

Embodiments of the present invention will now be described in detail with reference to the drawings.

An apparatus for carrying out this embodiment is shown in FIG.
As shown in FIG. 3, the color space conversion unit 10, the feature space conversion unit 20, and the region division unit 30 are included.

The color space conversion section 10 receives an input image signal,
For example, an RGB signal is converted into an L * u * v * signal. RG
B to XYZ coordinate system tristimulus values XYZ, XYZ to L
Conversion to * u * v * has been established and RGB to XY
The conversion to Z is defined by the following equation (see Makoto Miyahara “Systematic Image Coding” IPC Co., Ltd., page 134).

[0021]

[Equation 1]

The conversion from XYZ to L * u * v * is
It is defined by the following formula (see "Image Analysis Handbook" edited by Mikio Takagi and Yoshihisa Shimoda, pages 107-108 of the University of Tokyo Press).

[0023]

[Equation 2]

Here, (X0, Y0, Z0) is (X, Y, Z) for a perfect diffuse white surface under a standard light source.
Of the standard light source C (color temperature 6774K), (X0, Y0, Z0) = (98.072, 100.0)
0, 118.225).

In this embodiment, L * u * v * is used as the color space.
Although the color system is used, the equivalent effect can be obtained by considering an appropriate curve group that passes through “black → object color → white” in other color spaces such as the L * a * b * color system. Can be expected.

The conversion unit 20 into the feature space is (L *,
u *, v *) is a point (L,
μ, ν). From FIG. 11, the conversion formula is obtained by algebraic geometry, and is as follows.

[0027]

[Equation 3]

The area dividing section 30 samples all images, creates training data from the values of (μ, ν) for each pixel, and ISODATA (Itera)
tiveSelf Organizing Data Analysis Techniques
) Method for two-dimensional clustering (see “Image Analysis Handbook” supervised by Mikio Takagi and Yohisa Shimoda, pages 648-651 of the University of Tokyo Press).

In the ISODATA method, an appropriate cluster is given as an initial state, and its members are recombined to gradually obtain a "better cluster", which is a representative method of non-hierarchical clustering (relocation method). , Is a method that has been known since ancient times.

FIG. 12 shows the procedure of this ISODATA method.

In this ISODATA method, first in step 100, parameters such as relocation convergence conditions, minute clusters, isolated data determination conditions, and split / fusion branch conditions are set.

Then, in step 102, the center of gravity of the initial cluster is determined. For example, the total average can be used with the number of initial clusters being 1.

Then, in steps 104 to 108, the rearrangement method is performed. Specifically, first, step 104
Then, the distance between each piece of data and the center of gravity of the cluster is calculated, and each piece of data is arranged in the cluster having the smallest distance.
Then, in step 106, the center of gravity of each cluster is recalculated and corrected in the rearranged clusters. Then
In step 108, if the average of the distance between each data and the cluster centroid does not change as a result of the rearrangement / correction of the centroid, it is considered to have converged, and otherwise returns to step 104 to repeat.

After the rearrangement method is completed, the process proceeds to step 110,
Small clusters with a significantly small amount of data and isolated data that are significantly separated from other clusters are excluded from subsequent clustering.

Next, in step 112, when the minimum value of the distance between the cluster centroids is greater than or equal to a certain threshold value and the maximum value of the variance of the cluster is less than or equal to a certain threshold value, it is determined that the clustering has converged, and the clustering ends. Otherwise, go to steps 114 and / or 116.

If the minimum distance between the cluster centroids is less than or equal to a threshold, the process proceeds to step 114, the cluster pairs are integrated, and a new cluster centroid is calculated.

On the other hand, if the maximum value of the variance of the cluster is greater than a certain threshold value, the process proceeds to step 116, the cluster is divided until the maximum value of the cluster variance is equal to or less than the threshold value, and a new cluster centroid is calculated. .

Integration by step 114 or step 1
After the division by 16 is completed, the process returns to step 104 again to repeat the processing.

In this way, cluster division (cluster boundaries are indicated by broken lines) and cluster representative points 1, 2, 3 (indicated by circles) as shown in FIG. 13 are obtained.

As described in the section of the operation of the invention, since each of the representative points 1, 2, and 3 corresponds to one semi-ellipse representing the area as shown in FIG. 14, the division is performed in this way. After that, it is determined which half ellipse each pixel belongs to, labeling is performed, and the region division is completed.

[0041]

As described above, according to the present invention, the characteristic of the distribution in the color space estimated from the reflection model of an object is represented by a curve from "black" to "object color" to "white". Since modeling is performed and clustering is performed on the parameter space that distinguishes the curves, pixels in the same area tend to be localized at a specific location on the parameter space, and as a result, it is better than the conventional method. It is possible to perform area division.

[Brief description of drawings]

FIG. 1 is a diagram illustrating a conventional clustering method on an RGB color space.

FIG. 2 shows L * u * v * for explaining the principle of the present invention.
Diagram showing an example of distribution of object color of one object in space

FIG. 3 is a diagram similarly showing an example of distribution of object colors of a plurality of objects in the entire image.

FIG. 4 is a diagram showing an example of a division result by a conventional clustering method in an L * u * v * space.

FIG. 5 is a diagram showing another example in the L * u * v * space.

FIG. 6 is a diagram showing points representing “black” and “white” on the L * u * v * space for explaining the clustering method of the present invention.
Diagram showing a group of curves passing through points

FIG. 7 is a diagram showing a state in which the range of L * can be normalized.

FIG. 8 is a diagram showing an example of distribution of an actual image in a color space.

FIG. 9 is also obtained by mapping (μ,
ν) Diagram showing the distribution on the plane

FIG. 10 is a block diagram showing an overall configuration of an apparatus for carrying out an embodiment of the present invention.

FIG. 11 is a diagram for explaining a conversion formula used in a conversion unit for converting into a feature space in the embodiment.

FIG. 12 is a flowchart for explaining an example of a two-dimensional clustering method used in the area dividing unit of the above-described embodiment.

FIG. 13 is a diagram showing cluster division and cluster representative points obtained in the above embodiment.

14 includes L * u * v * including cluster representative points in FIG.
Diagram showing the area in space

[Explanation of symbols]

 10 ... Color space conversion unit 20 ... Feature space conversion unit 30 ... Region division unit

Claims (1)

[Claims] 1. A distribution characteristic in a color space estimated from a reflection model of an object has a characteristic of "white" passing from "black" to "object color".
By modeling each curve as a curve leading to, and mapping each pixel on the parameter space that distinguishes the curve, a distribution in which pixels in the same region are localized at a specific place is obtained, and clustering is performed on that space. A method of dividing a color image into areas.