Title of Invention

"AN APPARATUS FOR ANALYZING IMAGE TEXTURE"

Abstract The present invention relates to an apparatus for analyzing image information, and more particularly, to an apparatus for analyzing texture information of an image.
Full Text APPARATUS FOR ANALYZING IMAGE TEXTURE AND METHOD
THEREFOR
Technical Field
The present invention relates to an apparatus for analyzing image information, and more particularly, to an apparatus for analyzing texture information of an image and a method therefor.
Background Art
Texture information of a still image is important in classifying and detecting an image. The texture information is important in classifying objects after introducing MPEG-4 in which an object-based compression is applied. Referring to FIG. 1, an apparatus for detecting texture information from an image receives a still image and performs filtering with a Garbor filtering unit. The Garbor filtering unit is comprised of filters having a predetermined coefficient value based on characteristic scales and orientations. For example, the Garbor filtering unit can be comprised of 24 filters by combining four scales and six orientations. Namely, an input image is filtered by 24 filters having different scale and orientation coefficient values. Therefore, 24 images filtered by a filter having different filtering coefficient values are obtained. A mean and variance calculator calculates mean and variance from the filtered 24 images. Such a mean and variance value shows a regulation in the image and can be used for analyzing the texture information of the image.
However, since the apparatus extracts the mean and variance from the filtered image, it can extract information on the degree of regulation the texture has; however, it cannot analyze the orientation and the periodicity of the texture in detail.

STATEMENT OF INVENTION
Accordingly, the present invention relates to an apparatus for analyzing image texture information after receiving an image, comprising:
a filtering unit (20) for filtering a still image having a plurality of pixels of M rows x N columns with filters having different filtering coefficients, the filtering unit outputting a plurality of images;
a X axis projecting unit (222) for calculating a gray level mean value of a row of N pixels, for each row, for the filtered plurality of images;
a Y axis projecting unit (224) for calculating a gray level mean value of a columns of M pixels, for each column, for the filtered plurality of images;
a graph generating unit (24) for generating graphs showing a trend of gray level mean values from the gray level mean values output from the X axis projecting unit (222) and from the Y axis projecting unit (224), for the filtered plurality of images;
a graph storing unit (26) for storing the graphs; and
a texture information analyzing unit (28) for analyzing texture information of the image using the graphs.
Disclosure of the Invention
To solve the above problem, it is an objective of the present invention to provide an apparatus for analyzing image texture information

and able to analyze the orientation and periodicity of a texture in detail.
It is another object of the present invention to provide a method for analyzing image texture information by which it is possible to analyze the orientation and periodicity of a texture in detail.
Accordingly, to achieve the first objective, there is provided an apparatus for analyzing image texture information after receiving an image, comprising: a filtering unit for filtering a still image, comprised of a plurality of pixels of M rows x N columns, by using filters having different filtering coefficients; X axis projecting means for calculating a gray level mean value of a row of N pixels, for each row, with respect to the filtered plurality of images; and, Y axis projecting means for calculating a gray level mean value of a column of M pixels, for each column, with respect to the filtered plurality of images.
It is preferable that the apparatus further comprises: graph generating means for generating graphs showing the trend of gray level mean values from the gray level mean values output from the X axis projecting means and from the Y axis projecting means, with respect to the plurality of filtered images; graph storing means, for storing the graphs; and texture information analyzing means for analyzing the texture information of the image using the graphs.
The texture information analyzing means preferably analyzes the texture information of the image, either using one characteristic of a group of characteristics comprised of the shape, the peak, and the periodicity of the graph, or the combination of the characteristics. The filtering unit is preferably a Garbor filter that includes filters constructed by combining different scale coefficients and different orientation coefficients.
To achieve the second objective, there is provided a method for analyzing image texture information after receiving an image, comprising: a step of reading a still image, comprised of pixels of M rows x N columns; a step of filtering the still image using filters having different filtering coefficients, and outputting a plurality of images; an X axis projecting step for calculating a gray level mean value of a row of N

pixels, for each row, with respect to the filtered plurality of images; a Y axis projecting step for calculating a gray level mean value of a column of M pixels, for each column, with respect to the filtered plurality of images; a step of generating graphs showing the change of gray level mean values, with respect to the plurality of filtered images from the gray level mean values obtained in the X and Y axes projection steps; a step of storing the graphs; and a texture information analyzing step of analyzing the texture information of an image using the graphs.
Brief Description of the Drawings
The above objectives and advantages of the present invention will become more apparent by describing in detail a preferred embodiment thereof with reference to the attached drawings in which:
FIG. 1 is a block diagram showing the structure of a conventional apparatus for analyzing image texture information;
FIG. 2 is a block diagram showing the structure of an apparatus for analyzing image texture information according to an embodiment of the present invention;
FIG. 3 shows processes of performing X and Y axes projections with respect to a still image comprised of MxN pixels;
FIG. 4 is a graph showing the mean value of gray levels in rows as an example of the result of performing an X axis projection with respect to a still image;
FIG. 5 is a graph showing a mean value of gray levels in columns as the example of the result of performing a Y axis projection with respect to the still image;
FIG. 6 shows examples of X and Y axes projection graphs with respect to still images; and
FIG. 7 is a flowchart showing the main steps of a method for analyzing image texture information according to an embodiment of the present invention.
Best mode for carrying out the Invention
Hereinafter, preferred embodiments of an apparatus for analyzing image texture information according to the present invention and a method there for will be described in detail with reference to the attached drawings.
FIG. 2 is a block diagram showing the structure of an apparatus for analyzing image texture information according to an embodiment of the present invention. Referring to FIG. 2, the apparatus for analyzing the image texture information includes a filtering unit 20 and a projecting unit 22. The projecting unit 22 comprises an X axis projecting unit 222 and a Y axis projecting unit 224.
The operation of the apparatus for analyzing the image texture information will now be described.
First, a still image comprised of NxM pixels is input to the filtering unit 20. A Garbor filter, including filters constructed by combining different scales and orientations, as was described with reference to FIG. 1, or a similar filter, is preferably used as the filtering unit 20 so as to clearly distinguish an orientation and a periodicity of a filtered image. For example, when the combined number of filters used for filtering is K, filtered images /1 to /K are output from the filtering unit. The output filtered image is comprised of the NxM pixels like the still image input to the filtering unit 20. The number of filtered images is equal to the combined number K of filters, with respect to the input image. Also, the filtering unit 20 can include only filters having different scale coefficient values or filters having different orientation coefficient values, as is known to anyone skilled in the art. The variation of the filtering unit does not restrict the scope of the present invention defined by the attached claims.
The filtered images /1 to 1K are sequentially input to the projecting unit. The respective images are projected in the X axis projecting unit 222 and the Y axis projecting unit 224. FIG. 3 describes processes for performing X and Y axes projections with respect to a still image comprised of MxN pixels. The processes of performing the X and Y axes

projections will be described with reference to FIGs. 2 and 3. First, processes of projecting a first image l1 , among the filtered images /1 to IK, will be described. The X axis projecting unit 222 receives the filtered image /1comprised of NxM pixels and calculates gray level mean values AVG_H1 to AVG_HM of the N pixels in a row, for each row. Therefore, a gray level mean value AVG_H1 is obtained with respect to a first row, a gray level mean value AVG_H2 is obtained with respect to a second row, and a gray level mean value AVG_HM is obtained with respect to an Mth row. Similar to this, the Y axis projecting unit 224 receives the filtered image l1 comprised of the NxM pixels and calculates gray level mean values AVG_V1 to AVG_VM of the M pixels in a column, for each column. Therefore, a gray level mean value AVG_V1 is obtained with respect to a first column, a gray level mean value AVG_V2 is obtained with respect to a second column, and a gray level mean value AVG_VN is obtained with respect to an Nth column.
The graph generating unit 24 receives the gray level mean values AVG_H1 to AVG_HM with respect to the X axis projection from the X axis projecting unit 222 and the gray level mean values AVG_V, toAVG_VM with respect to the Y axis projection from the Y axis projecting unit 224 and generates a graph showing the change of the gray level mean values with respect to each row and column. Namely, two graphs (the X axis projecting graph and the Y axis projecting graph) are generated with respect to one filtered image. FIGs. 4 and 5 show the gray level mean values with respect to rows and columns as graphs, an example of the result of performing the X and Y axes projections with respect to the still image. Since the filtered images, the number of which is equal to the combined number K of the filters with respect to one input image are output from the filtering unit 20, the graph generating means 24 generates 2xK projection graphs with respect to one image.
The graph storing unit 26 stores the X axis projection graph and the Y axis projection graph that are output from the graph generating unit 24. FIG. 6 shows examples of the X and Y axes projection graphs with

respect to each still image stored in the graph storing unit 26. Referring to FIG. 6, it is noted that 2xK projection graphs are stored with respect to one image.
The stored graphs have peculiarities corresponding to the characteristics of the filters used for filtering. A case in which a filtering unit 20 is the Garbor filter will be described. When an image comprised of lines having scales corresponding to a predetermined distance of a horizontal orientation, in the filtered image output from the filter having a horizontal orientation and a filtering coefficient corresponding to the predetermined distance, a peak corresponding to the predetermined distance is high. But, in a filter having a precisely vertical orientation, no peak is shown. As shown above, when the input image is matched to different filtering coefficients, characteristics including unique peaks, periodicity, or shape are shown on the projection graphs.
The texture information analyzing unit 28 analyses the texture information of the input image by analyzing such characteristics. The texture information analyzing unit 28 can analyze the texture information using one characteristic among the group of characteristics consisting of the shape, the peak, and the periodicity of the X and Y axes projecting graphs. However, it is preferable to analyze the texture information using a combination of the characteristics in order to obtain synthesized information.
FIG. 7 is a flowchart showing the main steps of a method for analyzing the image texture information according to an embodiment of the present invention and will now be described as follows. First, a still image comprised of M rows xN columns of pixels is read and filtered (step 73). Accordingly, a plurality of filtered images corresponding to the number of the filters having different filtering coefficients are obtained. For example, when the combined number of filters used for filtering is K, K filtered images are obtained. The X axis projection for calculating the gray level mean value of a row of N pixels, for each row, is performed with respect to each of the filtered plurality of images. The Y axis

projection for calculating the gray level mean value of a column of M pixels, for each column, is performed with respect to each of the filtered image (step 74). Therefore, describing with reference to FIG. 2, the gray level mean value AVG_H1 is obtained with respect to the first row, the gray level mean value AVG_H2 is obtained with respect to the second row and the gray level mean value AVG_HM is obtained with respect to the Mth row. Also, the gray level mean value AVG_V1 is obtained with respect to the first column, the gray level mean value AVG_V2 is obtained with respect to the second column. The gray level mean value AVG_VN is obtained with respect to the Nth column.
Graphs showing the change of the gray level mean values from the gray level mean values obtained in the X and Y axes projecting steps are generated with respect the plurality of filtered images (step 75). Since the filtered images corresponding to the combined number K of the filters are obtained with respect to one input image in the filtering step 73, 2*K projecting graphs are generated with respect to one image in the graph generating step 75. The generated graphs are stored in a predetermined memory (step 76). The texture information of the image is analyzed using the graphs as mentioned above with reference to FIG. 2. (step 77).
The method and apparatus according to the present invention use graphs projected to X and Y axes, and do not use average and standard deviations of data information obtained by filtering, so that it is possible to describe the orientation and the periodicity of the texture. Therefore, it is possible to analyze the orientation and the periodicity of the texture in detail by analyzing the texture information using the projection graphs.
Also, the method for analyzing the image texture information according to the present invention can be written as a program which can be performed in a computer. The method can be realized in a general personal computer by loading the program from a medium used by a computer. Different mediums include a magnetic recording medium such as a floppy disk or a hard disk, an optical recording medium such as a CD-ROM or a DVD and a carrier wave such as the transmission through

an Internet. Also, such functional programs, codes, and code segments can be easily estimated by a programmer of a technology field to which the present invention belongs to.
Industrial Applicability
It is possible to describe the orientation and the periodicity of the texture in extracting the texture information according to the method for analyzing image texture according to the present invention and the apparatus there for.




WE CLAIM:
1. An apparatus for analyzing image texture information after receiving an
image, comprising:
a filtering unit (20) for filtering a still image having a plurality of pixels of M rows x N columns with filters having different filtering coefficients, the filtering unit outputting a plurality of images;
a X axis projecting unit (222) for calculating a gray level mean value of a row of N pixels, for each row, for the filtered plurality of images;
a Y axis projecting unit (224) for calculating a gray level mean value of a columns of M pixels, for each column, for the filtered plurality of images;
a graph generating unit (24) for generating graphs showing a trend of gray level mean values from the gray level mean values output from the X axis projecting unit (222) and from the Y axis projecting unit (224), for the filtered plurality of images;
a graph storing unit (26) for storing the graphs; and
a texture information analyzing unit (28) for analyzing texture information of the image using the graphs.
2. The apparatus as claimed in claims 1, wherein texture information analyzing means
analyze the texture information of the image using one characteristics of a group of
characteristics comprised of the shape, the peak and the periodicity of the group or
using a combination of the characteristics.
3. The apparatus as claimed in claims 1 or 3, wherein the filtering unit is a Garbor
filter having filters constructed by combining different scale coefficient and
different orientation.
4. The apparatus for analyzing image texture information after receiving an image,
substantially as herein described with reference to the accompanying drawings.



Documents:

in-pct-2001-00663-del-abstract.pdf

in-pct-2001-00663-del-claims.pdf

in-pct-2001-00663-del-correspondence-others.pdf

in-pct-2001-00663-del-correspondence-po.pdf

in-pct-2001-00663-del-description (complete).pdf

in-pct-2001-00663-del-form-1.pdf

in-pct-2001-00663-del-form-13.pdf

in-pct-2001-00663-del-form-19.pdf

in-pct-2001-00663-del-form-2.pdf

in-pct-2001-00663-del-form-26.pdf

in-pct-2001-00663-del-form-3.pdf

in-pct-2001-00663-del-form-5.pdf

in-pct-2001-00663-del-gpa.pdf

in-pct-2001-00663-del-pct-101.pdf

in-pct-2001-00663-del-pct-210.pdf

in-pct-2001-00663-del-pct-304.pdf

in-pct-2001-00663-del-pct-308.pdf

in-pct-2001-00663-del-pct-332.pdf

in-pct-2001-00663-del-pct-409.pdf

in-pct-2001-00663-del-petition-138.pdf


Patent Number 231868
Indian Patent Application Number IN/PCT/2001/00663/DEL
PG Journal Number 13/2009
Publication Date 27-Mar-2009
Grant Date 12-Mar-2009
Date of Filing 25-Jul-2001
Name of Patentee SAMSUNG ELECTRONICS CO., LTD.
Applicant Address 416 MAETAN-DONG, PALDAL-GU SUWON-CITY, KYUNGKI-DO, 442-373, REPUBLIC OF KOREA.
Inventors:
# Inventor's Name Inventor's Address
1 HYUN-DOO SHIN 510-1302 MUJIGAE MAEUL CHEONGGU APT., 221 KUMI-DONG BUNDANG-GU, SUNGNAM-CITY, KYUNGKI-DO, 463-500, REPUBLIC OF KOREA.
2 YANG-LIM CHOI 102-1112 WOOMAN SUNKYUNG APT., 105 WOOMAN-DONG PALDAL-GU, KYUNGKI-DO, 442-190 REPUBLIC OF KOREA.
PCT International Classification Number G06T 7/00
PCT International Application Number PCT/KR00/00201
PCT International Filing date 2000-03-13
PCT Conventions:
# PCT Application Number Date of Convention Priority Country
1 09/272,321 1999-03-19 U.S.A.