Information
-
Patent Grant
-
6236466
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Patent Number
6,236,466
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Date Filed
Tuesday, October 27, 199826 years ago
-
Date Issued
Tuesday, May 22, 200123 years ago
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Inventors
-
Original Assignees
-
Examiners
Agents
-
CPC
-
US Classifications
Field of Search
US
- 358 19
- 358 447
- 358 455
- 358 462
- 358 456
- 358 457
- 358 458
- 358 465
- 358 466
- 358 298
- 358 532
- 358 534
- 358 535
- 358 536
- 382 237
- 382 270
- 382 266
- 382 274
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International Classifications
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Abstract
The present invention relates to an image processing method for converting a gray-level image into a binary image by using an image processing system. The gray-level image comprises a plurality of gray-level pixels arranged in a matrix format. The binary image comprises the same number of binary pixels arranged in the same manner as the gray-level image. The image processing system comprises a memory for storing programs and the gray-level and binary images, and a processor for executing the programs stored in the memory. The image processing method comprises two steps. The first step is to examine the gray-level pixels of the gray-level image so as to locate and define gray-level pixels with boundary characteristics as boundary points according to a predetermined boundary determination method, and to define gray-level pixels next to each of the boundary points as neighboring points according to a predetermined neighboring point determination method. The second step is to convert each of the boundary points and neighboring points of the gray-level image into a corresponding binary pixel according to a first pixel conversion method, and to convert each of the gray-level pixels other than the boundary points and neighboring points in the gray-level image into a corresponding binary pixel according to a second pixel conversion method to generate the binary image.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to an image processing system, and more particularly, to a system and method for converting a gray-level image into a binary image.
2. Description of the Prior Art
Many image-processing devices need to convert gray-level images into binary images. Line-art/bi-level conversion method and half-tone conversion method are the most commonly used two conversion methods. The line-art/bi-level method uses a gray-level threshold value between 0 and 255 to convert a gray-level pixel. A gray-level pixel with a value greater than the threshold is converted into 1, otherwise it is converted into 0. The converted binary image usually shows a character of strong contrast. Such a conversion method is ideal for text image conversion since the edges of text data usually show strong contrast character, but it is inappropriate for converting graphic images because graphic images usually have a lot of continuous gray-level variations.
The half-tone conversion method includes dither method, error diffusion method, and correlative density assignment of adjacent pixels (CAPIX). The half-tone conversion method can effectively reflect continuous variations of gray levels and can thus be adequately used to convert graphic images. However, in converting text images it will blur the edges of text data and thus should be avoided.
Text and graphic images need to use different conversion methods for converting each of them into a binary image. But since the prior art image conversion method can only be used to convert a complete image each time, a user must make a decision to select a single conversion method which is suitable for converting one type of images even though the image to be converted may contain both types of images.
SUMMARY OF THE INVENTION
It is therefore a primary objective of the present invention to provide an image processing system which can convert both types of images so that the above mentioned problem can be solved.
In a preferred embodiment, the present invention comprises an image processing method for converting a gray-level image into a binary image by using an image processing system, the gray-level image comprising a plurality of gray-level pixels arranged in a matrix format, the binary image comprising the same number of binary pixels arranged in the same manner as the gray-level image, the image processing system comprising a memory for storing programs and the gray-level and binary images, and a processor for executing the programs stored in the memory, the image processing method comprising the following steps:
(1) examining the gray-level pixels of the gray-level image so as to locate and define gray-level pixels with boundary characteristics as boundary points according to a predetermined boundary determination method, and defining gray-level pixels next to each of the boundary points as neighboring points according to a predetermined neighboring point determination method; and
(2) converting each of the boundary points and neighboring points of the gray-level image into a corresponding binary pixel according to a first pixel conversion method, and converting each of the gray-level pixels other than the boundary points and neighboring points in the gray-level image into a corresponding binary pixel according to a second pixel conversion method to generate the binary image.
It is an advantage of the present invention that the image processing system assigns an edge-closeness parameter to each of the gray-level pixels in a gray-level image to identify two types of gray-level pixels. The two types of gray-level pixels are then separately converted by using two different image conversion methods. Furthermore, the image processing method can intensify the boundary image by adjusting the maximum value of the edge-closeness parameter thus increasing the flexibility of using the image processing system.
This and other objectives of the present invention will no doubt become obvious to those of ordinary skill in the art after having read the following detailed description of the preferred embodiment which is illustrated in the various figures and drawings.
BRIEF DESCRIPTION OF THE DRAWING
FIG. 1
is a block diagram of an image processing system according to the present invention.
FIGS. 2A
to
2
M show a pixel matrix of a gray-level image.
FIG. 2N
is a binary image converted from the gray-level image in
FIGS. 2A
to
2
M.
FIG. 3
shows a conversion matrix.
FIG. 4
is another embodiment of the conversion matrix in FIG.
3
.
FIG. 5
shows a binary image generated by using the conversion matrix in FIG.
4
.
FIGS. 6 and 7
are flowcharts of an image processing method according to the present invention.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT
Please refer to FIG.
1
.
FIG. 1
is a block diagram of an image processing system
10
according to the present invention. The image processing system
10
is used for converting a graylevel image into a binary image. The gray-level image comprises a plurality of gray-level pixels arranged in a matrix format. The binary image comprises the same number of binary pixels arranged in the same manner as the gray-level image. The image processing system
10
comprises a memory
12
for storing the gray-level and binary images, a boundary detector
14
for examining the gray-level image so as to locate gray-level pixels with boundary characteristics and define them as boundary points, a neighboring point detector
16
for defining gray-level pixels next to each of the boundary points as neighboring points according to a predetermined neighboring point determination method, and a pixel converter
18
for converting each of the boundary points and neighboring points of the gray-level image into a corresponding binary pixel according to a line-art pixel conversion method, and converting each of the gray-level pixels other than the boundary points and neighboring points in the gray-level image into a corresponding binary pixel according to a half-tone pixel conversion method to generate the binary image.
The boundary detector
14
, neighboring point detector
16
and pixel converter
18
can be executed by using their corresponding hardware, or grouped in a processor (not shown) and executed by using corresponding software. If executed by software, programs have to be stored in the memory
12
and then executed by the processor. Many prior art techniques are capable of using the boundary detector
14
to examine if a gray-level pixel has boundary characteristics and will not be discussed here but the use of the neighboring point detector
16
to determine if a gray level pixel is a neighboring point is explained in FIG.
2
.
Please refer to
FIGS. 2A
to
2
M.
FIGS. 2A
to
2
M show a pixel matrix
20
of a gray-level image in various phases of examining neighboring points.
FIG. 2A
comprises a gray-level image with a dark-colored central portion and light-colored sides. The gray-level image is formed by a 10×10 pixel matrix
20
. When the neighboring point detector
16
examines whether a gray-level pixel is a neighboring point, it will assign each gray-level pixel a corresponding edge-closeness parameter. When a pixel is determined to be a boundary point, its edge-closeness parameter will be assigned a predetermined maximum value such as 2 in this embodiment. The rest of the gray-level pixels each will be assigned a smaller number such as 0 depending on how far the pixel is away from the boundary points. If the edge-closeness parameter of a gray-level pixel is between the maximum and 0, the gray-level pixel is a neighboring point.
When assigning edge-closeness parameters, the neighboring point detector
16
will define a conversion matrix with a fixed dimension first, and then process each of the gray-level pixels in a left to right and top to bottom sequence. The 3×2 matrix enclosed by the bold-faced square in
FIGS. 2B
to K is a conversion matrix
22
which comprises the gray-level pixels currently being processed. Please refer to FIG.
3
.
FIG. 3
shows the conversion matrix
22
. The conversion matrix
22
comprises a target pixel
24
enclosed by a double-layered square and four reference pixels
26
filled with slash lines. The target pixel
24
is the gray-level pixel to be assigned an edge-closeness parameter. The four reference pixels
26
are used as decision-making references for assigning the edge-closeness parameter to the target pixel
24
. If the target pixel
24
within the conversion matrix
22
is detected as a boundary point by the boundary detector
14
, the target pixel
24
will be assigned a number 2 as its edge-closeness parameter. If the target pixel
24
within the conversion matrix
22
is not a boundary point, and the edge-closeness parameters of the reference pixels
26
are either undefined or are 0, the target pixel
24
will be assigned a value of 0. If the target pixel
24
within the conversion matrix
22
is not a boundary point, and the reference pixels
26
contain at least one pixel with an assigned value greater than 0, the target pixel
24
will be assigned a number equal to 1 less than the greatest assigned value of the reference pixels
26
.
When assigning edge-closeness parameters to gray-level pixels in the first two rows of the gray-level matrix
20
, all twenty pixels are assigned a value 0 because the boundary detector
14
has not detected any boundary points and each reference pixel
26
is either undefined or assigned a value of 0. In the conversion matrix
22
in
FIGS. 2B
to
2
E, all target pixels
24
are assigned a value 2 because each is a boundary point. In
FIG. 2F
, the target pixel
24
is not a boundary point, and three of the four reference pixels
26
have edge-closeness parameters assigned as 0 with the other reference pixel
26
assigned a value of 2, thus the target pixel
24
is assigned a value equal to 1 less than the greatest value 2 which is 1. In
FIG. 2G
, the target pixel
24
is not a boundary point, and the four reference pixels
26
are all assigned values of 2, thus the target pixel
24
is assigned a value of 1. In
FIGS. 2H and 2I
, because the target pixels
24
are both boundary points, they are assigned values of 2. In
FIG. 2J
, the target pixel
24
is not a boundary point, and the four reference pixels
26
are assigned values of 1, thus the target pixel
24
is assigned a value of 0. In
FIG. 2K
, the target pixel
24
is not a boundary point, and the greatest value of the four reference pixels
26
is 2, thus the target pixel
24
is assigned a value of 1.
FIG. 2L
shows the edge-closeness parameters of all gray-level pixels. The gray-level pixels that are assigned values of 2 are boundary points, and those whose values are assigned between 0 and 2 are neighboring points.
After all edge-closeness parameters are assigned, all points are outputted to the pixel converter
18
where boundary points and neighboring points undergo line-art pixel conversion processing and other gray-level pixels undergo half-tone pixel conversion processing. In line-art pixel conversion, a gray-level boundary value is first defined to be used in determining if gray-level pixels will be converted to white or black. The gray-level boundary value should be defined as between the gray-level values of gray-level pixels inside and outside a boundary so that the gray-level pixels at two sides of the boundary can be converted into different colors thus intensifying the boundary image. In
FIG. 2M
, pixels with the letter H are converted by using the half-tone pixel conversion, pixels with the letter L/W are converted by using the line-art pixel conversion and are converted into white gray-level pixels, and pixels with the letter L/B are converted by using the line-art pixel conversion and are converted into black gray-level pixels. All gray-level pixels will be converted into binary pixels by the pixel converter
18
.
FIG. 2N
shows a binary image converted from the gray-level image in
FIGS. 2A
to
2
M. The binary image in
FIG. 2N
shows that at two sides of the boundary, the darker side becomes even darker, and the lighter side becomes even lighter thus the boundary appears very clear. At portions not along the boundary line such as the center and sides of the matrix
20
, the pixels are converted by using half-tone pixel conversion, thus the color appears to change smoothly like the gray-level pixels before conversion.
The aforementioned conversion matrix
22
has a dimension of 3×2 with the middle pixel in the second row defined as the target pixel
24
. However, the dimension of the conversion matrix
22
and the position of the target pixel
24
are flexible and can be defined according to the capacity of the memory
12
and the processing speed of the image processing system
10
. Moreover, the number and the position of the reference pixels
26
of the conversion matrix
22
are also variable. Please refer to
FIGS. 4 and 5
.
FIG. 4
shows another conversion matrix
28
.
FIG. 5
shows a binary image generated by using the conversion matrix
28
in FIG.
4
. The conversion matrixes
28
and
22
differ in that the conversion matrix
28
only has three reference pixels
26
whereas the conversion matrix
26
has four.
FIG. 5
shows that when the gray-level matrix
20
is converted by using fewer reference pixels, fewer gray-level pixels will be defined as neighboring points, thus fewer gray-level pixels will be converted by using the line-art pixel conversion method while more will be converted by using the half-tone pixel conversion method, and thus the edges of the generated binary image will become less distinct. The number and position of the reference pixels can be defined according to different requirements in the sharpness of the edges.
Please refer to
FIGS. 6 and 7
.
FIGS. 6 and 7
are flowcharts of an image processing method
30
according to the present invention. The image processing method
30
comprises the following steps:
step
32
: start;
step
33
: defining the maximum value of the edge-closeness parameters;
step
34
: defining the number of rows and columns of the conversion matrix
22
, and defining the corresponding positions of the target pixel
24
and reference pixels
26
in the conversion matrix
22
;
step
35
: selecting a gray-level pixel as the target pixel
24
for assigning the edge-closeness parameter;
step
36
: detecting if the target pixel
24
is a boundary point; if not, go to step
38
;
step
37
: assigning the maximum value of the edge-closeness parameters to the target pixel
24
, go to step
41
;
step
38
: detecting if the edge-closeness parameters of all reference parameters are either undefined or assigned a value of 0; if not, go to step
40
;
step
39
: assigning 0 as the edge-closeness parameter to the target pixel
24
; go to step
41
;
step
40
: assigning an edge-closeness parameter to the target pixel
24
which is equal to 1 less than the greatest edge-closeness parameters of the reference pixels
26
;
step
41
: detecting if each of the gray-level pixels is assigned an edge-closeness parameter; if not, go to step
35
;
step
42
: outputting the edge-closeness parameter of each of the gray-level pixels to the pixel converter
18
for converting into a binary pixel;
step
43
: detecting if the edge-closeness parameter of a gray-level pixel is zero; if not, go to step
45
;
step
44
: converting the gray-level pixel by using the half-tone pixel conversion method; go to step
46
;
step
45
: converting the gray-level pixel by using the line-art pixel conversion method;
step
46
: detecting if all gray-level pixels are converted into binary pixels; if not, go to step
42
;
step
47
: end.
In step
33
, the maximum value of the edge-closeness parameters will directly affect the number of neighboring points. A greater maximum value is associated with a greater number of neighboring points therefore causing a greater number of gray-level pixels using the line-art conversion method that intensifies the boundary image. The maximum value of the edge-closeness parameter can be assigned according to the content of the original image. The image processing method
30
can either assign an edge-closeness parameter to a target pixel
24
after the status of the pixel
24
is determined, or find out all gray-level pixels with boundary characteristics first and then assign an edge-closeness parameter to each of the target pixels
24
.
In contrast to the prior art image processing system, the image processing system
10
of the present invention assigns an edge-closeness parameter to each of the gray-level pixels in a gray-level image to identify two types of gray-level pixels. The two types of gray-level pixels are then separately converted by two different image conversion methods in order to upgrade the generated image. Furthermore, the image processing method
30
can intensify the boundary image by adjusting the maximum value of the edge-closeness parameter thus increasing the flexibility of using the image processing system
10
.
Those skilled in the art will readily observe that numerous modifications and alterations of the device may be made while retaining the teachings of the invention. Accordingly, the above disclosure should be construed as limited only by the metes and bounds of the appended claims.
Claims
- 1. An image processing method for converting a gray-level image into a binary image by using an image processing system, the gray-level image comprising a plurality of gray-level pixels arranged in a matrix format, the binary image comprising the same number of binary pixels arranged in the same manner as the gray-level image, the image processing system comprising a memory for storing programs and the gray-level and binary images, and a processor for executing the programs stored in the memory, the image processing method comprising the following steps:(1) examining the gray-level pixels of the gray-level image so as to locate and define gray-level pixels with boundary characteristics as boundary points according to a predetermined boundary determination method, and defining gray-level pixels next to each of the boundary points as neighboring points according to a predetermined neighboring point determination method; and (2) converting each of the boundary points and neighboring points of the gray-level image into a corresponding binary pixel according to a first pixel conversion method, and converting each of the gray-level pixels other than the boundary points and neighboring points in the gray-level image into a corresponding binary pixel according to a second pixel conversion method to generate the binary image.
- 2. The image processing method of claim 1 wherein the first pixel conversion method is a line-art pixel conversion method.
- 3. The image processing method of claim 2 wherein the line-art pixel conversion method compares a gray-level pixel with a predetermined reference number and determines the gray-level pixel should be converted into 0 or 1 as a binary pixel according to the result of the comparison.
- 4. The image processing method of claim 1 wherein the second pixel conversion method is a half-tone pixel conversion method.
- 5. The image processing method of claim 1 wherein the neighboring point determination method comprises the following steps:(1) assigning a predetermined natural number to each of the boundary points of the gray-level image; (2) defining a neighboring area of a gray-level pixel which comprises the positions of the gray-level pixels adjacent to the gray-level pixel; (3) assigning a value to each of the undefined gray-level pixels which are not yet assigned in the gray-level image according to a predetermined sequence: assigning 0 to an undefined gray-level pixel if the gray-level pixels in the neighboring area of the undefined pixel are either undefined or assigned as 0, or else assigning a number to an undefined gray-level pixel which is equal to 1 less than the greatest assigned value of the gray-level pixels in the neighboring area if the gray-level pixels in the neighboring area contain at least one pixel with an assigned value greater than 0; and (4) defining the gray-level pixels with assigned values other than the predetermined natural number or 0 as the neighboring points.
- 6. An image processing system for converting a gray-level image into a binary image, the gray-level image comprising a plurality of gray-level pixels arranged in a matrix format, the binary image comprising the same number of binary pixels arranged in the same manner as the gray-level image, the image processing system comprising:a memory for storing the gray-level and binary images; a boundary detector for examining the gray-level image so as to locate and define gray-level pixels with boundary characteristics as boundary points; a neighboring point detector for defining gray-level pixels next to each of the boundary points as neighboring points according to a predetermined neighboring point determination method; and a pixel converter for converting each of the boundary points and neighboring points of the gray-level image into a corresponding binary pixel according to a first pixel conversion method, and converting each of the gray-level pixels other than the boundary points and neighboring points in the gray-level image into a corresponding binary pixel according to a second pixel conversion method to generate the binary image.
- 7. The image processing system of claim 6 wherein the first pixel conversion method is a line-art pixel conversion method.
- 8. The image processing system of claim 7 wherein the line-art pixel conversion method compares a gray-level pixel with a predetermined reference number and determines the gray-level pixel should be converted into 0 or 1 as a binary pixel according to the result of the comparison.
- 9. The image processing system of claim 6 wherein the second pixel conversion method is a half-tone pixel conversion method.
- 10. The image processing system of claim 6 wherein the neighboring point determination method comprises the following steps:(1) assigning a predetermined natural number to each of the boundary points of the gray-level image; (2) defining a neighboring area of a gray-level pixel, which comprises the positions of the gray-level pixels adjacent to the gray-level pixel; (3) assigning a value to each of the undefined gray-level pixels which are not yet assigned in the gray-level image according to a predetermined sequence: assigning 0 to an undefined gray-level pixel if the gray-level pixels in the neighboring area of the undefined pixel are either undefined or assigned as 0, or else assigning a number to an undefined gray-level pixel which is equal to 1 less than the greatest assigned value of the gray-level pixels in the neighboring area if the gray-level pixels in the neighboring area contain at least one pixel with an assigned value greater than 0; and (4) defining the gray-level pixels with assigned values other than the predetermined natural number or 0 as the neighboring points.
- 11. An image processing method for converting a gray-level image into a binary image by using an image processing system, the gray-level image comprising a plurality of gray-level pixels arranged in a matrix format, the binary image comprising the same number of binary pixels arranged in the same manner as the gray-level image, the image processing system comprising a memory for storing programs and the gray-level and binary images, and a processor for executing the programs stored in the memory, the image processing method comprising the following steps:(1) detecting each of the gray-level pixels of the gray-level image according to a predetermined boundary determination method and a predetermined neighboring point determination method, using the boundary determination method to define the gray-level pixels with boundary characteristics as boundary points, and using the neighboring point determination method to define the gray-level pixels adjacent to each of the boundary points as neighboring points; and (2) converting each of the boundary points and neighboring points of the gray-level image into a corresponding binary pixel according to a first pixel conversion method, and converting each of the gray-level pixels other than the boundary points and neighboring points in the gray-level image into a corresponding binary pixel according to a second pixel conversion method to generate the binary image.
- 12. The image processing method of claim 11 wherein the first pixel conversion method is a line-art pixel conversion method.
- 13. The image processing method of claim 12 wherein the line-art pixel conversion method compares a gray-level pixel with a predetermined reference number and determines the gray-level pixel should be converted into 0 or 1 as a binary pixel according to the result of the comparison.
- 14. The image processing method of claim 11 wherein the second pixel conversion method is a half-tone pixel conversion method.
- 15. The image processing method of claim 11 wherein step (1) further comprises following steps:(a) detecting the gray-level pixel by using the boundary determination method and assigning it a predetermined natural number if it contains boundary characteristics according to the boundary determination method, if not, go to (b); (b) assigning 0 to the gray-level pixel if pixels adjacent to it are either undefined or assigned as 0; assigning a number to an undefined gray-level pixel which is equal to 1 less than the greatest assigned value of the adjacent pixels if the adjacent pixels contain at least one pixel with an assigned value greater than 0; and (c) defining the gray-level pixels with assigned values other than the predetermined natural number or 0 as neighboring points.
Priority Claims (1)
Number |
Date |
Country |
Kind |
87 113683 |
Aug 1998 |
TW |
|
US Referenced Citations (6)