Image processing method

Abstract: An image processing method for calculating the distance between nearest centroids comprising steps of; Generating a table of x- and y-coordinates of centroid of groups in a image in a order of x- or y-coordinate; Evaluating distances between a couple of centroids from x- or y-coordinates thereof so as to find couples impossible to be the nearest; and Calculating distances between centroids of other couples than the couples found in the evaluation.

Description: FIELD OF THE INVENTION The present invention relates to an image processing method for calculating the minimum distance between centroids of groups in an image, that is, the distance of the nearest centroids. Centroids and their calculations an discussed in PROCEEDINGS OF THE IEEE 1987 CUSTOM INTEGRATED CIRCUITS CONFERENCE 4 May 1987, pages 299-302, The Portland Hilton, Portland, Oregon, US; D.D. SHUGARD et al.: "A CMOS VLSI Chip for Real-Time Centroid Calculation". BACKGROUND OF THE INVENTION For example, as for images of a metal, it often happens that the minimum distances between centroids of groups in various organizations differ from one another. Therefore it is important to calculate the minimum distance so as to find the boundary between different organizations. It does not seem that a method to calculate the minimum distance effectively is published. In the case that the minimum distance is calculated by a self-obvious method, it is necessary to calculate all of the centroid distances of each couple of groups, and moreover to find the minimum value among them. The number of times of distance calculation and numerical calculation to find the minimum, it is: NxN(N-1)/2N³ (N = number of groups) and the more the number of groups increases, the more calculation time grows. A general method for minimum distance calculation in images is described in US-A- 4 223 387. SUMMARY OF THE INVENTION The present invention has an object to provide an image processing method for efficiently calculating the minimum distance between centroids. The image processing method according to the present invention as set out in claim 1 is based on the rule of experience that the couple of centroids which has the minimum distance has (a) closest value(s) of x-coordinate and/or y-coordinate with each other. An index for indicating the centroid of each group in an image is registered at the place of each centroid in a frame memory, x-coordinate and y-coordinate of the centroid are registered in a table in the raster order, centroid distances are calculated from couples adjacent in this table to more remote couples, and neglecting distance calculations for couples which do not have small differences between x-coordinates or y-coordinates. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 shows an images to be processed; Figs 2 to 4 show an embodiment of image processing method according to the present invention, that is; Fig. 2 shows centroids in an image; Fig. 3 shows a table of x- and y-coordinates of the centroids; Fig. 4 shows more detailedly the table in Fig. 3. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT Exemplary preferred embodiments of the image processing method according to the present invention will be described in detail hereinafter. When centroids of groups are calculated (there are 6 groups from No.1 to No.6 in Fig. 1: the X marks are centroids of each group), an image including indexes for indicating centroids is generated. Their index may be the labeling numbers of each group. Fig. 2 shows extracted centroids in the image of Fig. 1. The image is stored in a frame memory and data in the frame memory are read out in the raster order in high speed. Simultaneously with the read-out, x-coordinates and y-coordinates of indexed pixels, are written in a table in the order of indexes found, that is, in the raster order. The table is generated as shown in Fig. 3 in which pairs of y-coordinate and x-coordinate are registered in serial. As shown in Fig. 1 and Fig. 2, when the origin of the coordinate axes is on the corner of upper left, downward direction is y-direction and rightward direction is x-direction, the y-coordinate train is arranged so as to increase monotonously, and x-coordinate train is arranged so as to increase monotonously in each group of the same y-coordinate. Since the distances between the nearest centroids can be defined for each group, taking each group as a reference group one by one from which centroid distances to other groups are calculated. The centroid distances are calculated sequentially between the reference group and the other groups from the nearest to the most remote one. "Nearest" means the order on the table is the nearest, therefore, as for the coordinate system above, centroid distance is calculated sequentially from the one whose value of y-coordinate is the same as or the nearest to the centroid of the reference group. As shown in Fig. 4, when the group whose centroid coordinate is (x k , y k ) is the reference group, the centroid distances to other groups are evaluated in increasing order or decreasing order. Here the increasing order is applied. First the centroid distance d k,k+1 between the reference group and the next group is calculated as follows; d k,k+1 =(x k -x k+1 )²+(y k -y k+1 )² Next the centroid coordinate (x k+2 , y k+2 ) of next group is evaluated and the value of the coordinates of x k , x k+1 , x k+2 are compared. Then, if the values are x k >=x k+1 >=x k+2 or x k <=x k+1 <=x k+2 the values of d k,k+1 and d k,k+2 are always d k,k+1 <=d k,k+2 Therefore the calculation of d k,k+2 is omitted and the value of next group is evaluated. In this way, by omitting complex calculations as many times as possible, the process becomes efficient. If the condition of (2), (3) or both of them are not satisfied, d k,k+2 is calculated and the smaller one between d k,k+1 and d k,k+2 is registered. Thereafter (x k+3 , y k+3 ), (x k+4 , y k+4 ), ..., are evaluated in the same manner as the above, that is, evaluation by the conditions (2) and (3) and minimal times distance calculations are performed; finally the minimal value of distance is registered. After the evaluation in increasing order, the evaluation in decreasing order is performed. As for the evaluation in decreasing order, the condition formulas (4) and (5) are applied instead of the above conditions (2) and (3). x k >=x k-1 >=x k-2 x k <=x k-1 <=x k-2 When the evaluation in decreasing order is started, the minimum distance (hereinafter it is called d u, min ) obtained by the evaluation in increasing order is used as a reference distance. When a distance d k,k-1 is d k,k-1 >=d u,min , the groups which do not satisfy the conditions (4) or (5) are evaluated by comparing their distances with d u,min , then, the minimal distance is finally registered and the following evaluation is performed. In this case, the evaluations are performed from increasing order to decreasing order, however the same result will be obtained by the evaluation in reverse sequences. In the process above, when the number of groups is N, the number of distance calculations is much fewer than (N-1), and only the evaluations by the condition formulas are (N-1) times. Therefore the number of calculation times decreases than the one of conventional method, and the more the number of groups becomes big, the more the efficiency of the present invention becomes conspicuous. Incidentally, x-coordinate and y-coordinate can be settled voluntarily. The table may be generated according to any data orders as far as the order is based on the raster order. Although only a few exemplary embodiments of this invention have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the exemplary embodiments without departing from the novel teachings and advantages of this invention. Accordingly, all such modifications are intended to be included within the scope of this invention as defined in the following claims.

Claims: Image processing method for determining among a group of centroids the closest neighbor centroid to a predetermined reference-centroid out of the group, comprising steps of: determining coordinate pairs (x,y) of the centroids according to their positions in respect to a first coordinate direction x and a second coordinate direction y, storing said coordinate pairs in a table according to an increasing order with respect to the y-coordinate values and if equality exists with respect to the y-coordinate values further according to an increasing/decreasing order with respect to the x-coordinate values, calculating for said reference centroid its closest neighbor centroid by calculating the distances to other neighbor centroids of the table in either an increasing or a decreasing order from the reference centroids coordinate pair, wherein a distance to a further centroid of the table is only calculated if said further centroid has a x-coordinate which is within an absolute range inbetween the x-coordinate of the reference centroid and the x-coordinate of that neighbor centroid for which the distance has already been calculated and found to be currently the shortest neighbor distance and comparing the resultant detected shortest distance to the shortest distance found by a similar search in a respective reverse, i.e. decreasing/ increasing order of the table. An image processing method according to Claim 1, further comprising a step of registering an index at the place of each centroid in a frame memory for generating said table. An image processing method according to Claim 1, wherein data in said table is arranged in the raster order. An image processing method according to Claim 1, wherein said calculating is performed with respect to the reference coordinate pair in said table from the nearest to the most remote coordinate pair.

Inventors: KUMAGAI RYOHEI C O EZEL INC, KUMAGAI, RYOHEI C/O EZEL INC.
Applicants: EZEL INC, SHARP KK, EZEL INC., SHARP KABUSHIKI KAISHA
Application year: 1989
Main classification: G06F15
IPC codes: G06T7