Apparatus for analyzing image texture and method therefor

• US 6,424,741 B1
• Filed: 03/19/1999
• Issued: 07/23/2002
• Est. Priority Date: 03/19/1999
• Status: Expired due to Fees

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Choi, Yang-lim, Shin, Hyun-doo
• Primary Examiner(s): MEHTA, BHAVESH M
• Application Number:
  US09/272,321
• Time in Patent Office: 1,222 Days
• Field of Search: 382/170, 382/173, 382/243, 382/203, 382/260-265, 382/270-273, 382/254, 382/108, 382/307-308, 345/582, 345/427, 345/428, 708/322, 708/300
• US Class Current: 382/170
• CPC Class Codes: G06T 2207/20008 Globally adaptive
• CPC Class Codes: G06T 2207/20072 Graph-based image processing
• CPC Class Codes: G06T 7/44 using image operators, e.g. filters, edge density metrics or local histograms

Abstract

An apparatus for analyzing image texture information and a method therefor are provided. The apparatus for analyzing the image texture information includes a filtering unit for filtering a still image including a plurality of pixels of M rows×N columns with filters having different filtering coefficients, an X axis projector 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 a Y axis projector 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 possible to describe the orientation and the periodicity of the texture in extracting the texture information.

Claims

1. An apparatus for analyzing image texture information after receiving an image, comprising:

a filtering unit for filtering a still image having a plurality of pixels of M rows×

N columns with filters having different filtering coefficients, the filtering unit outputting a plurality of images;

X axis projecting means for calculating a gray level mean value of a row of N pixels, for each row, for 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, for the filtered plurality of images.

9. An apparatus as claimed in claim 1, wherein the filtering unit is a Garbor filter including filters constructed by combining different scale coefficients and different orientation coefficients.

2. An apparatus for analyzing image texture information after receiving an image, comprising:

a filtering unit for filtering a still image having a plurality of pixels of M rows×

N columns with filters having different filtering coefficients, the filtering unit outputting a plurality of images;

X axis projecting means for calculating a gray level mean value of a row of N pixels, for each row, for the filtered plurality of images;

Y axis projecting means for calculating a gray level mean value of a column of M pixels, for each column, for the filtered plurality of images;

graph generating means for generating graphs showing a 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, for the plurality of filtered images;

graph storing means for storing the graphs; and

texture information analyzing means for analyzing texture information of the image using the graphs.

3. An apparatus as claimed in claim 2, wherein the texture information analyzing means analyses the texture information of the image using one characteristic of a group of characteristics comprised of the shape, the peak, and the periodicity of the graph or using a combination of the characteristics.

4. An apparatus as claimed in claim 3, wherein the filtering unit is a Garbor filter including filters constructed by combining different scale coefficients and different orientation coefficients.

5. A method for analyzing image texture information after receiving an image, comprising:

a step of filtering the still image with 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, for the filtered plurality of images;

an Y axis projecting step for calculating a gray level mean value of a column of M pixels, for each column for the filtered plurality of images;

a step of generating graphs showing a change of gray level mean values of the plurality of filtered images using 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.

6. A method as claimed in claim 5, wherein the texture information analyzing step includes a step of analyzing the texture information of an image using one characteristic of a group of characteristics consisting of the shape, the peak, and the periodicity of the graph or using a combination of the characteristics.

7. A method as claimed in claims 5 or 6, wherein the filtering step is performed by a Garbor filter including filters constructed by combining different scale coefficients and different orientation coefficients.

8. An apparatus for analyzing image texture information after receiving an image, comprising:

a filtering unit comprising a plurality of filters having different filtering coefficients, the filtering unit being operative to filter a still image having a plurality of pixels of M rows×

N columns and output a plurality of images;

an X axis projecting unit operative to calculate a gray level mean value of a row of N pixels, for each row, for the filtered plurality of images; and

an Y axis projecting unit operative to calculate a gray level mean value of a column of M pixels, for each column, for the filtered plurality of images.

10. An apparatus as claimed in claim 8, wherein the filtering unit is a Garbor filter including filters constructed by combining different scale coefficients and different orientation coefficients.

11. A method for analyzing image texture information after receiving a still image, composing:

filtering the still image with filters having different filtering coefficients, and generating a filtered plurality of images;

calculating a gray level mean value of a row of N pixels, for a predetermined number of rows contained in the filtered plurality of images;

calculating a gray level mean value of a column of M pixels, for a predetermined number of columns contained in the filtered plurality of images;

generating graphs, showing a change of gray level mean values of the plurality of filtered images, using the gray level mean values;

storing the graphs; and

analyzing texture information of the still image using the graphs.