Texture discrimination method

Abstract: To reliably discriminate the characters only from the headlines having any background textures, the present invention analyzes the projection profile of a grey image in a headline area In order to automatically set a learning area A in the area of background texture only of the periphery in the headline area, and set a learning area B in the area that includes background texture of a central portion and characters; determines a discriminant function having, as variables, characteristics that use a plurality of pixel densities in the vicinity of a pixel which is processed so that output values of the discriminant function at each of the positions in the learning area A forms a profile with an average Va, that the output values at each of the positions in the learning area B forms a profile with an average Vb, and that the sum of dispersion values of the two profiles becomes smaller than a predetermined value, in order to discriminate in which area is included the pixel which is processed in the headline area; and determines in which area of the background pattern or the characters the pixel is included depending upon whether the output value of the discriminant function is close to the value Va or to the value Vb for each of the pixels in the headline area, in order to discriminate the headline area into areas.

Description: BACKGROUND OF THE INVENTION The present invention relates to texture discrimination for discriminating the whole area, in which two kinds of textures are present in a mixed manner, into the areas for each kind of textures, and particularly for automatically discriminating the characters from a headline area with a background texture of a printed matter such as a newspaper, a magazine or the like. Accompanying the widespread use of personal computers or word processors and the reduction in the cost of the recording media in recent years, documents in the form of data base and electronic filing of printed matter have been occupying important positions in office automation. In particular, it has been desired to construct an automatic document register/retrieval system capable of storing data contained in the printed documents such as newspaper, magazines and official documents, and capable of retrieving required information only therefrom. At the time of registering, this system automatically divides, first, the document into a headline area, a text area and a photo area through image processing. In the former two areas, the characters are encoded by character recognition and, then, character code sequences and photo image data are registered. With the file registered in such a form, the cost for storage and transfer is cheaper and the retrieval of contents is easier than when the whole document is stored as an image. In an attempt to develop such systems, therefore, study and research have been as described, for example, in the Trans. of IEICE (the Institute of Electronics, Information and Communication Engineers of Japan) "A Method of Document-Image Segmentation based on Projection Profiles, Stroke Densities and Circumscribed Rectangles", Vol. J69-D, No. 8, August, 1986. According to the above-mentioned system, a character sequence in the headline area includes key words of the content of the text, and the storage of the character sequence in an encoded form has an important meaning. The headline-character sequence added as key words to the text data makes it easy to retrieve the content. Although it is important to pick up the characters in the headline area, a difficult assignment is involved. The headline of an important article of a newspaper and the like has a texture as background of the characters to draw attention. Therefore, even if the headline area could be detected by the aforementioned conventional art, it is difficult to pick up the character sequences only from such an area with a texture through a simple processing. This is because, with the binary processing based on a simple grey level, background texture is picked up together with the characters. The texture discrimination method of this type has been disclosed in, for example, Trans. of IEICE (the Institute of Electronics, Information and Communication Engineers of Japan), "Texture Analysis by Self-Organization Method", Vol. J67-D, No. 1, January, 1984. With this method, however, the size of the element of the texture must have been known. Moreover, the background texture of the headline area is usually different depending upon the headlines, which makes it difficult to design optimum discrimination processing despite several kinds of textures being taken into consideration in advance. A further texture discrimination method and a computer system for processing such a method are described in PROC. OF THE 1st INT. CONFERENCE ON COMPUTER VISION, 8 June 1987, London GB, Pages 439-443; W.E. Blanz et al.: "Control-free low-level image segmentation: theory, architecture, and experimentation". This method includes a training phase, wherein a set of features is calculated for each pixel in the training area of an image and on the basis of these sets of features a polynomial classifier is adapted to discriminate between pixels belonging to the object of interest or to the background, and a run-phase, wherein the whole image is discriminated into the object of interest and the background on the basis of adapted polynomial classifier. SUMMARY OF THE INVENTION The object of the present invention is to provide a texture discrimination method which is suitable for automatically discriminating an image having two kinds of textures into areas for each kind of texture in a flexible and fast manner. The object is met by a method according to claim 1 . Preferred embodiments are disclosed in the depending claims. The following principles are used in the present invention. The projection profile of a grey image in a headline area is analyzed in order to automatically set a learning area A in the area of background texture only of the periphery in the headline area, and set a learning area B in the area that includes background texture and characters. A discriminant function is determined having, as variables, characteristics that use a plurality of pixel densities in the vicinity of a pixel which is processed so that output values of the discriminant function at each of the positions in the learning area A forms a profile with an average Va, that the output values at each of the positions in the learning area B forms a profile with an average Vb, and that the sum of dispersion values of the two profiles becomes smaller than a predetermined value, in order to discriminate in which area is included the pixel which is processed in the headline area. Finally, the area of the background textures or the characters is determined depending upon whether the output value of the discriminant function is close to the value Va or to the value Vb for each pixel in the headline area, in order to discriminate the headline area into areas. The aforementioned steps make it possible to automatically set a discriminant function for texture discrimination for each headline area, and characters can be discriminated from the headlines having any background textures. BRIEF DESCRIPTION OF THE DRAWINGS Further objects, features and advantages of the present invention will become more clear upon the following detailed description of preferred embodiments as shown in the drawing, wherein: Fig. 1(a) is a diagram explaining a method of picking up characters according to the present invention; Fig. 1(b) is a diagram explaining how to set a learning area A and a learning area B in the method of picking up characters according to the present invention; Fig. 1(c) is a diagram showing the result of picking up the characters by the character pick-up method of the present invention; Fig. 2 is a diagram showing the whole constitution of an automatic document register/retrieval system to which the present invention is adapted; Figs. 3(a) and (b) are diagrams showing the output conditions of discriminant functions; Fig. 4 is a flowchart explaining the method of setting the learning areas A and B; Fig. 5 is a diagram which shows a projection profile; Fig. 6 is a flowchart explaining a method of finding a discriminant function; and Figs. 7(a), (b), and (c) are a flowchart and diagrams which explain the method of finding an n-th order discriminant function in Fig. 6. DETAILED DESCRIPTION An embodiment of the present invention will now be described in detail in conjunction with the drawings. Fig. 2 illustrates the whole constitution of an automatic document register/retrieval system which employs the texture discrimination of the present invention. First, the image information of a printed matter 1 such as a Japanese newspaper or the like is converted into digital signals is through a scanner 6 and is stored in an image memory 2 as a two-dimensional image. Next, using a computer 3, the image in the image memory 2 is divided into a headline area, a text area, a photo area, and like areas. This can be done by the aforementioned conventional technique which is based on projection profiles, stroke densities and circumscribed rectangles. After the characters are discriminated from the headline area by the texture discrimination of the present invention, the character sequence is encoded by known character recognition. The encoded data groups are filed for every article and are stored in a memory 4. The CRT terminal 5 is used by a user to instruct the content of work to be performed to the computer 3 at the time of registering and retrieval. A method of discriminating characters from the headline area to which the present invention is directly concerned will now be described in conjunction with Fig. 1(a). The final purpose is to discriminate the character portion C from the headline area M that has a background texture T as shown in Fig. 1(b). Step 1 in Fig. 1(a) sets a learning area A of only the background texture without characters in a peripheral portion only of the headline area, and sets a learning area B that has background texture and characters, as shown in Fig. 1(b). The headline area with background texture of a printed matter usually has areas A of textures only in the periphery thereof. These areas A can be automatically set by a method that will be described later. Step 2 determines a discriminant function f having, as variables, character quantities that use a plurality of pixel densities in the vicinity of a pixel which is processed, so that output values of the discriminant function f at each of the positions of the area A form a profile with an average value Va, that output values of the discriminant function at each of the positions of the area B form a profile with an average value Vb, and that the sum of dispersion values of the two profiles becomes smaller than a predetermined value S. Therefore, despite the average value Vb of all of the outputs of the area B, the background texture portion only of the area B forms a profile with an average value Va and the remaining character portion forms a profile of a different value. Therefore, the two areas can be discriminated. Figs. 3(a) and (b) illustrates relationships of these profiles showing background texture BT and character portion CP. Step 3 determines in which profile the output value of the discriminant function is included for each of the positions in the whole headline area. The step 3 produces 1 when the output value is included in the profile of the character portion and produces 0 when the output value is included in the profile of only the texture portion. Thus, the character portion C is found as shown in Fig. 1(b). Step 4 cuts out a character and recognizes a character from the image data of character portion C by the analysis of projection profiles, and converts the character portion C into a character code sequence by optical character recognition, OCR. Fig. 4 illustrates a method of setting the learning areas A and B in the step 1 of Fig. 1(a). In the first step, pixel density projection profiles are found in the X- and Y-directions of the headline region, and a suitable smoothing processing is effected for each of the profiles. Fig. 5 shows the results thereof. In the headline with background texture, in general, there exists a region of background texture only along the periphery of the headline. Therefore, the pixel density projection profile of either the X-direction or the Y-direction has a small degree of ruggedness, and the character area has a conspicuously rugged profile. Therefore, a second step finds a range La having a pixel density projection profile of either the X-direction or the Y-direction with little ruggedness in the periphery of the headline area based on the threshold processing of the projection profiles. A third step sets the learning area A shown in Fig. 1(b) based on the range La found in the second step. Here, the learning area B may be a range Lb having conspicuous ruggedness on the profile. Fig. 6 illustrates a method of determining a discriminant function in the step 2 of Fig. 1(a). A first step sets the generation number n to 0, and renders the images Pna and Pnb that are to be discriminated to be the images in the areas A and B. A second step determines, based on a method that will be described later, an n-th order discriminant function f n (x, y), such that the output values of the function at each of the positions (x, y) of the image Pna form a profile with an average value Va, the output values of the function at each of the positions (x, y) of the image Pnb form a profile with an average value Vb, and that the sum Sn of dispersion values of the profiles becomes a minimum. A third step advances to the next step in the case of Sm > Sn > S (where m = n-1, and S is a predetermined value, e.g. a constant) and advances to the final step in other cases. A fourth step renews the generation (n = n+1), and uses the images Pna and Pnb to be discriminated as an output value in the area A and as an output value in the area B of the m-th order discriminant function f m (x, y). The program then returns to the second step. Using a group of discriminant functions f 0 ,-----, f n thus obtained, the final step sets a final discriminant function f = f n (f m (---(f 0 )---)). Fig. 7(a) is a flowchart of how to find the n-th order discriminant function f n (x, y) in the step 2 of Fig. 6. Step (1) finds character quantities c 1 ,-----, c k of a predetermined number from the vicinity area of a predetermined size with a pixel which is processed as a center. They may be average values or densities in the subareas r 1 , -----, r k included in the vicinity area as shown in, for example, Fig. 7(b) or Fig. 7(c). The step (2) finds, by Lagrange's method of indeterminate coefficient, unknown coefficients a 0 , a 1 , a 2 and a 3 of a 0-th order basic discriminant function f′ 0ij (x, y) expressed by the following equation (3) such that Sij of the following equation (1) becomes a minimum for each combination (c i , c j ) of the character quantities and that the following equation (2) holds true, f′ oij (x,y)= a 0 + a 1 c i + a 2 c j + a 3 c i c j where Sa and Sb denote areas of the areas A and B. Advance is to the final step (6) when every minimum value Sij is smaller than or equal to S, and advance to the next step (3) in other cases. Step (3) selects k 0-th order basic discriminant functions f′ 0ij (x, y) based on the combinations ((i 0 . j 0 ), (i 1 , j 1 ), ---) of character quantities having small values Sij, and sends the outputs as input images to the next step (4). Here, i and j together are denoted by q (but (i 0 , j 0 ) when q = 1, (i 1 , j 1 ) when q = 2,1 ≦ q ≦ k) and the input image is denoted by f' nq (x, y)(where O ≤ n). Step (4) finds, by Lagrange's method of indeterminate coefficient, unknown a m 0, a m1 , a m2 and a m3 of the m-th order basic discriminant function f' mij (where m = n+1) expressed by the following equation (6) for each combination (f′ ni (x, y), f′ nj (x,y)) of input images, such that Smij of the following equation (4) becomes a minimum and that are following equation (5) holds true, f′ mij (x,y)= a m0 + a m1 f' ni (x,y)+ a m2 f' nj (x,y) + a m3 f' ni (x,y)f' nj (x,y) Advance is to the final step (6) when the minimum value Smij is smaller than or equal to S or when the minimum value Smij is greater than or equal to the minimum value Snij, and advance is to the next step (5) in other cases. Step (5) selects m-th order basic discriminant functions f′ mij (x, y) having small values Smij in a number of k′, and sends the outputs as input images f′ mq (x, y) (where q = k′) to the step (4). Step (4) is repeated as described above to gradually decrease the dispersion value Smij and the final step (6) is reached when the dispersion value Smij is converged. When the minimum value Smij or Sij is smaller than S in the final step (6), a master basic discriminant function is sought reversely from the m-th order basic discriminant function f′ mij (x, y) having the minimum value in order to reconstitute a discriminant function that is to be found. When the minimum value Smij is greater than the minimum value Snij, a master basic discriminant function is sought reversely from the n-th order basic discriminant function f′ nij (x, y) having the minimum value in order to reconstitute a discriminant function that is to be found. The routine of Fig. 7(a) is thus finished. An important feature is that the discriminant function that is found is in the form of a polydimensional polynominal equation having a predetermined number of characteristics c 1 , ---, c k in the vicinity area of a given size with a pixel which is processed as a center. In summary, the steps shown in Fig. 6 and Fig. 7(a) are used to determine a discrimination function having, as variables, a plurality of densities from the vicinity area of a pixel which is processed. The output values of the discriminant function at each of the positions of the area A form a profile with an average value Va. The output values of the discriminant function at each of the positions of the area B form a profile with an average value Vb. The sum of the dispersion values of the two profiles is smaller than a predetermined value S. The steps of Fig. 7(a) find a discriminant function f expressed in a multidimensional polynominal form having a predetermined number of character quantities c 1 , -----, c k in the vicinity area of a predetermined size with a pixel which is processed as a center, such that the sum of dispersion values of the two profiles becomes a minimum. The steps of Fig. 6 apply the second step again to the discriminant function f n in order to minimize the sum of dispersion values of the two profiles. That is, the steps of Fig. 6 repeat the second step to gradually expand the vicinity area, and to find a plurality of character quantities by which the texture can be discriminated (dispersion value becomes smaller than S) and a discriminant function which consists of the character quantities. Then, as shown in Fig. 3(b), the background textured area in the area A forms a profile of small dispersion values with average value Va. Since the background textured area in the area B forms a profile with average value Va, the remaining character portion forms a profile having different values, and the two areas can be discriminated. The result of picking up the characters is shown in Fig. 1(c). The above embodiment has explained the present invention by picking up the characters from the headline area of a printed document. However, it is also possible to pick up particular tissues from the image of living body tissues and to pick up particular areas from the image of aerial photograph by setting the learning areas A and B, and by obtaining a discriminant function of the present invention. According to the present invention as described above, the learning areas A and B are automatically set for two textured areas, and an optimum discriminant function for texture discrimination is obtained by using the learning areas. Therefore, the texture can be discriminated flexibly even in case a pattern consisting of an unkno« , n texture is encountered.

Claims: A method of discriminating an image containing two kinds of textures into areas containing only one kind of texture, comprising the steps of automatically setting a first learning area (A) to an area containing only one kind of texture and a second learning area (B) containing the other kind of texture by: a first step of producing pixel density projection profiles of said image in X- and Y-directions, a second step of determining a first range (La) in the pixel density projection profiles having a small degree of ruggedness and a second range (Lb) in said pixel density projection profiles having a conspicuous degree of ruggedness, and a third step of setting the first learning area (A) to the area corresponding to the first range (La) and the second learning area (Lb) to the area corresponding to the second range (Lb); determining a discriminant function, having as variables, the pixel densities in the vicinity of a pixel being processed, such that the output values of said discriminant function for the pixels of said learning areas (A, B) form profiles having average values (Va, Vb) for each learning area and so that the sum of the dispersion values of the two profiles for the learning areas (A, B) is smaller than a predetermined value (S); and discriminating with the determined discriminant function the areas containing the one kind of texture from the areas containing the other kind of texture by comparing the output values of said discriminant function for the pixels of the image with the profiles of the learning areas (A, B). The method according to claim 1, wherein said discriminant function is a polynominal discriminant function determined by Lagrange's method of indeterminate coefficient. The method according to claim 2, wherein said step of determining said one discriminant function f' comprises a first step for finding an average density in the subareas r 1 , ..., r k included in the vicinity area as characteristics c 1 , ..., c k from a predetermined vicinity area with the pixel processing as a center; a second step which finds, by Lagrange's method of indeterminate coefficient, unknown coefficients a0, a1, a2 and a3 of a 0-th order basic discriminant function f '0jj (x, y) expressed by the following equation (3) such that Sij of the following equation (1) becomes a minimum for each combination (ci, cj) of the character quantities and that the following equation (2) holds true, f '0ij (x,y)= a 0 + a 1 c i + a 2 c j + a 3 c i c j where Sa and Sb denote areas of the learning areas A and B; a third step which- advances to the final step when the minimum value Sij is smaller than S and advances to the next step in other cases; a fourth step which selects k 0-th order basic discriminant functions f' 0ij (x, y) based on combinations ((io, io), (i 1 , j 1 ), ...) of character quantities of small Sij values, and which sends the outputs as input images to the next step; a fifth step for finding, by Lagrange's method of indeterminate coefficient, unknown coefficients am0, am1, am2 and am3 of an m-th order basic discriminant function f'mij(x, y) expressed by the following equation (6) for each combination (f'ni(x, y), f'nj(x, y)) of the input images such that Smij of the following equation (4) becomes a minimum and that the following equation (5) holds true, f' mij (x,y)= a m0 + a m1 f' ni (x,y)+ a m2 f' nj (x,y) + a m3 f' ni (x,y)f' nj (x,y) where i and j together are denoted by q (but (io,jo) when q=1, (i1, j1) when q=2, ---, 1 ≦ q ≦ k), the input image is denoted by f'nq(x, y)(where 0 ≦ n), and Sa and Sb denote areas of the learning areas A and B; a sixth step which advances to the final step when the minimum value S mij is smaller than or equal to S or when the minimum value S mij is greater than or equal to the minimum value S nij, and advances to the next step in other cases; a seventh step which selects m-th order basic discriminant functions f' mij (x, y) having k' small values Smij, and which sends the outputs as input images f'mq(x,y)(where q = k') to the fifth step; and a final step which, when the minimum value Smij or Sij is smaller than S, reconstitutes a discriminant function f that is to be found by reversely seeking a master basic discriminant function from the m-th order basic discriminant function f' mij (x,y) having the minimum value, and which, when the minimum value Smij is greater than the minimum value Snij, reconstitutes a discriminant function f' that is to be found by reversely seeking a master basic discriminant function from the n-th order basic discriminant function f'nij(x, y) having the minimum value. The method according to any of claims 1 to 3, wherein the profiles of said learning areas (A, B) determined in the determining step are at least substantially non-overlapping. The method according to any of claims 1 to 4, wherein said first learning area (A) contains textured background without characters and said second learning area (B) contains characters. The method according to claim 5, further comprising the steps of encoding the areas containing characters into character codes by character recognition, and retrieving other information using the character codes as keywords.

Inventors: MATSUSHIMA HITOSHI, SAKOU HIROSHI, MATSUSHIMA, HITOSHI, SAKOU, HIROSHI
Applicants: HITACHI LTD, HITACHI, LTD.
Application year: 1990
Main classification: G06K9
IPC codes: G06K9, G06T7