Source: http://www.docstoc.com/docs/83155999/Apparatus-To-Detect-Homogeneous-Region-Of-Image-Using-Adaptive-Threshold---Patent-7970208
Timestamp: 2014-12-23 03:56:16
Document Index: 39288797

Matched Legal Cases: ['Application No. 2004', 'art 10', 'art 20', 'art 10', 'art 20', 'art 210', 'art 220', 'art 230', 'art 210', 'art 210', 'art 230', 'art 220', 'art 220', 'art 230', 'art 230', 'art 240', 'art 250', 'art 240', 'art 240', 'art 250', 'art 240', 'art 240', 'art 210', 'art 220', 'art 230', 'art 240', 'art 230', 'art 240', 'art 250', 'Application No. 2004', 'art 10', 'art 20', 'art 10', 'art 20']

Apparatus To Detect Homogeneous Region Of Image Using Adaptive Threshold - Patent 7970208
United States Patent: 7970208
A homogeneous region detector of an image using an adaptive threshold,
and a method of the same. The homogeneous region detector includes a
global region standard deviation calculation part to calculate a global
region standard deviation of a whole region of an input image, a local
region standard deviation calculation part to divide the input image into
a predetermined number of local regions and to calculate a local region
standard deviation of the each local region, and a homogeneous region
determination part to separate the homogeneous region from a feature
region in the input image using an adaptive threshold calculated based on
entropy of the input image. Accordingly, the homogeneous region can be
precisely detected by effectively separating the homogeneous region from
the feature region of the input image by using the threshold adaptively
calculated based on the entropy of the input image, so that the
homogeneous region can be applied to various fields of image processing.
Han; Seung-hoon (Suwon-si, KR)
11/237,942
10-2004-0113795
382/173  ; 382/171; 382/172; 382/270
G06K 9/34&amp;nbsp(20060101); G06K 9/38&amp;nbsp(20060101)
382/170-173,190,195,205,206,282
2002/0028008
2002/0172412
2003/0174887
2004/0008892
2004/0076337
2005/0163374
2007/0160286
2002-92609
2004-72446
1996-27399
98-72846
1999-69865
2004-46271
Trier and Jain, &quot;Goal-Directed Evaluation of Binarization Methods&quot;, IEEE 1995, p. 47-58. cited by examiner
Sahoo, P et al. &quot;Threshold selection using a minimal histogram entropy difference&quot;, Optical Engineering 36, 1997 p. 1976-1981. cited by examiner
Navon et al. &quot;Color image segmentation based on adaptivve local thresholds&quot;, Image and Vision Computing 23, 2004, p. 69-85. cited by examiner
Gonzalez, Rafael and Woods, Richard, &quot;Digital Image Processing&quot;, 2002, Prentice Hall, Second Edition, pp. 437-440, 600-606. cited by examiner
Sezgin, Mehmet and Sankur, Bulent, &quot;Survey over image thresholding techniques and quantitative performance evaluation&quot;, Jan. 2004, Journal of Electronic Imaging 13(1), pp. 146-168. cited by examiner.
1.  An apparatus to detect homogeneous region of an input image, the apparatus comprising: a global region standard deviation calculation part to calculate a global region
standard deviation of a whole region of the input image;  a local region standard deviation calculation part to divide the input image into a predetermined number of local regions and to calculate a local region standard deviation of each local region;
and a homogeneous region determination part to separate a homogeneous region from a non-homogeneous region in the input image using an adaptive threshold calculated based on an entropy constant of the input image and the global region standard deviation,
wherein the homogeneous region determination part comprises: an adaptive threshold calculation part to calculate the adaptive threshold based on the global region standard deviation and the entropy constant of the input image;  and a comparison part to
compare the calculated adaptive threshold with the local region standard deviation of each local region and to determine one of the local regions to be the homogeneous region when the adaptive threshold is greater than the respective local region
standard deviation, wherein the adaptive threshold calculation part calculates the adaptive threshold assuming that a local region entropy calculated based on the adaptive threshold, the global region standard deviation and the local region standard
deviation is the same as the entropy constant.
2.  An apparatus to detect homogeneous region of an input image, the apparatus comprising: a global region standard deviation calculation part to calculate a global region standard deviation of a whole region of the input image;  a local region
standard deviation calculation part to divide the input image into a predetermined number of local regions and to calculate a local region standard deviation of each local region;  and a homogeneous region determination part to separate a homogeneous
region from a non-homogeneous region in the input image using an adaptive threshold calculated based on an entropy constant of the input image and the global region standard deviation, wherein the adaptive threshold is calculated by the following
expression: .times..times..times..sigma..times..times..times..sigma.  ##EQU00009## wherein T1 denotes the adaptive threshold, K denotes an entropy constant of the input image, and .sigma..sub.g denotes the global region standard deviation of the input
3.  The apparatus of claim 2, wherein the entropy constant K comprises a bit/symbol measurement of the input image.
4.  A method to detect a homogeneous region of an image using an adaptive threshold in a homogenous region detector, the method comprising: calculating a global region standard deviation of a whole region of an input image, dividing the input
image into a certain number of local regions, and calculating a local region standard deviation of each local region;  and separating a homogeneous region from a non-homogeneous region in the input image using an adaptive threshold calculated based on an
entropy constant of the input image and the global region standard deviation, wherein the above steps are computed by a digital computer, wherein the separating of the homogenous region from the non-homogenous region in the image comprises: calculating
the adaptive threshold based on the global region standard deviation and the entropy constant of the input image;  and comparing the calculated adaptive threshold with the local region standard deviation .sigma..sub.m of each local region and determining
one of the local regions to be the homogeneous region when the adaptive threshold is greater than the respective local region standard deviation, and wherein an adaptive threshold calculation part calculates the adaptive threshold assuming that a local
region entropy calculated based on the adaptive threshold, the global region standard deviation and the local region standard deviation is the same as the entropy constant.
5.  A method to detect a homogeneous region of an image using an adaptive threshold in a homogenous region detector, the method comprising: calculating a global region standard deviation of a whole region of an input image, dividing the input
entropy constant of the input image and the global region standard deviation, wherein the above steps are computed by a digital computer, and wherein the adaptive threshold is calculated by the following expression:
.times..times..times..sigma..times..times..times..sigma.  ##EQU00010## wherein, T1 denotes the adaptive threshold, K denotes an entropy constant of the input image, and .sigma..sub.g denotes the global region standard deviation of the input image.
6.  The method of claim 5, wherein the entropy constant comprises a bit/symbol measurement of the input image.
7.  A computer readable recording medium comprising executable codes to perform a method of detecting a homogenous region of an image, the method comprising: calculating a global region standard deviation of a whole region of an input image,
dividing the input image into a certain number of local regions, and calculating a local region standard deviation of each local region;  and separating a homogeneous region from a non-homogeneous region in the input image using an adaptive threshold
calculated based on an entropy constant of the input image and the global region standard deviation, wherein the separating of the homogenous region from the non-homogeneous region in the image comprises: calculating the adaptive threshold based on the
global region standard deviation and the entropy constant of the image;  and comparing the calculated adaptive threshold with the local region standard deviation .sigma..sub.m of each local region and determining one of the local regions to be the
homogeneous region when the adaptive threshold is greater than the respective local region standard deviation, wherein an adaptive threshold calculation part calculates the adaptive threshold assuming that a local region entropy calculated based on the
adaptive threshold, the global region standard deviation and the local region standard deviation is the same as the entropy constant.
8.  The apparatus of claim 2, wherein the homogeneous region determination part comprises: an adaptive threshold calculation part to calculate the adaptive threshold based on the global region standard deviation and the entropy constant of the
input image;  and a comparison part to compare the calculated adaptive threshold with the local region standard deviation of each local region and to determine one of the local regions to be the homogeneous region when the adaptive threshold is greater
than the respective local region standard deviation.
9.  The method of claim 5, wherein the separating of the homogenous region from the non-homogeneous region in the image comprises: calculating the adaptive threshold based on the global region standard deviation and the entropy constant of the
input image;  and comparing the calculated adaptive threshold with the local region standard deviation .sigma..sub.m of each local region and determining one of the local regions to be the homogeneous region when the adaptive threshold is greater than
the respective local region standard deviation.  Description
This application claims benefit under 35 U.S.C.  .sctn.119(a) of Korean Patent Application No. 2004-113795, filed Dec.  28, 2004, the disclosure of which is incorporated herein in its entirety by reference.
The present general inventive concept relates to an apparatus to detect a homogeneous region of an image, and a method thereof.  More particularly, the present general inventive concept relates to a homogeneous region detector capable of
correctly detecting a homogeneous region of an image using an adaptive threshold appropriate to properties of the image, and a method thereof.
Generally, a homogeneous region in an image is used for a variety of image processing fields.  For example, the homogeneous region may be used for estimation of image noise since a signal-to-noise (S/N) ratio of the homogeneous region is low.
Also, due to spatial redundancy of the homogeneous region, efficiency of image compression can be improved using the homogeneous region.  Furthermore, when detecting scene transition of video, the homogeneous region enhances motion tolerance of frame
differences.  For image interpolation, an interpolation value can be obtained thorough a simple calculation in the homogeneous region, thereby saving costs.  As explained above, the homogeneous region is applied to diverse fields of image processing.
Therefore, correct detection of the homogeneous region is an important matter.
FIG. 1 is a view illustrating a conventional method for detecting a homogeneous region of an image.  Referring to FIG. 1, a conventional homogeneous region detector 100 comprises a local region standard deviation calculation part 10 and a
comparison part 20.  The local region standard deviation calculation part 10 divides an input image into M.times.M regions and calculates the standard deviation of each region.  The comparison part 20 compares the standard deviation calculated with
respect to the each region to a preset fixed threshold T. A region having a smaller standard deviation than the preset fixed threshold T is determined to be a homogeneous region.
However, with the conventional detecting method using the fixed threshold, it is hard to precisely detect the homogeneous region according to an image or image noise.  For example, a texture region could be detected as a homogeneous region, or a
real homogeneous region could fail to be detected as the homogeneous region according to the fixed threshold.  When detection of the homogeneous region is not correct, noise estimation in the detected region or other post-processing of the image cannot
The foregoing and/or other aspects of the present general inventive concept may be achieved by providing a homogeneous region detector using an adaptive threshold, the homogeneous region detector including a global region standard deviation
calculation part to calculate a global region standard deviation of a whole region of an input image, a local region standard deviation calculation part to divide the input image into a certain number of local regions and to calculate a local region
standard deviation of each local region, and a homogeneous region determination part to separate a homogeneous region from a feature region in the input image using the adaptive threshold calculated based on entropy of the input image.
The homogeneous region determination part may include an adaptive threshold calculation part to calculate the adaptive threshold based on the global region standard deviation and an entropy constant K of the input image, and a comparison part to
compare the adaptive threshold with the local region standard deviation of each local region and to determine one of the local regions to be the homogeneous region when the adaptive threshold is greater than the respective local region standard
.times..times..times..sigma..times..times..times..times..sigma.  ##EQU00001##
wherein, T1 denotes the adaptive threshold, K denotes the entropy constant, and .sigma..sub.g denotes the global region standard deviation of the input image.
The foregoing and/or other aspects of the present general inventive concept may also be achieved by providing a homogeneous region detector to detect a homogeneous region of an input image, the homogenous region detector including a calculation
unit to calculate a global standard deviation of the input image, to calculate a plurality of local standard deviations corresponding to a plurality of local regions of the input image, and to calculate an adaptive threshold based on the calculated
global standard deviation and an entropy constant of the input image, and a comparison unit to compare the plurality of local standard deviations to the adaptive threshold calculated by the calculation unit to determine whether the corresponding
plurality of local regions of the input image are homogeneous.
The foregoing and/or other aspects of the present general inventive concept may also be achieved by providing a homogenous region detector to detect a homogenous region of an input image, the homogenous region detector including an adaptive
threshold calculation unit to calculate an adaptive threshold according to a predetermined property of the input image, a standard deviation calculation unit to divide the input image into a plurality of regions and to calculate a local standard
deviation of each region, and a comparison unit to compare the local standard deviation of each region to the adaptive threshold calculated by the adaptive threshold calculation part to determine a homogenous region of the input image.
The foregoing and/or other aspects of the present general inventive concept may be achieved by providing a method of detecting a homogeneous region of an image using an adaptive threshold, the method including calculating a global region
standard deviation of a whole region of an input image, dividing the input image into a certain number of local regions and calculating a local region standard deviation of each local region, and separating a homogeneous region from a feature region in
the input image using the adaptive threshold calculated based on entropy of the input image.
The separating of the homogeneous region from the feature region may include calculating the adaptive threshold based on the global region standard deviation and an entropy constant of the input image, and comparing the adaptive threshold with
the local region standard deviation of each local region and determining one of the local regions to be the homogeneous region when the adaptive threshold is greater than the respective local region standard deviation.
.times..times..times..sigma..times..times..times..times..sigma.  ##EQU00002##
The foregoing and/or other aspects of the present general inventive concept may also be achieved by providing a method of detecting a homogenous region of an input image, the method including calculating an adaptive threshold according to a
predetermined property of the input image, calculating a plurality of local standard deviations corresponding to a plurality of regions of the input image, and comparing the plurality of local standard deviations to the calculated adaptive threshold to
determine a homogenous region of the input image.
FIG. 2 illustrates a homogenous region detector 200 usable with an image processing apparatus according to an embodiment of the present general inventive concept.  Referring to FIG. 2, the homogeneous region detector 200 includes a global region
standard deviation calculation part 210, a local region standard deviation calculation part 220 and a homogeneous region determination part 230.
The global region standard deviation calculation part 210 calculates a global region standard deviation .sigma..sub.g of an input image.  The global region standard deviation .sigma..sub.g obtained by the global region standard deviation
calculation part 210 is input to the homogeneous region determination part 230 to be used to detect a homogeneous region.
The local region standard deviation calculation part 220 divides the input image into M.times.M regions and calculates a local region standard deviation .sigma..sub.m of each region.  The local region standard deviation .sigma..sub.m obtained by
the local region standard deviation calculation part 220 corresponding to each region of the input image is input to the homogeneous region determination part 230 to be used to detect the homogeneous region.
The homogeneous region determination part 230 comprises an adaptive threshold calculation part 240 and a comparison part 250.  The adaptive threshold calculation part 240 calculates an adaptive threshold T1 based on entropy of the input image.
That is, the adaptive threshold calculation part 240 calculates the adaptive threshold T1 on the basis of the global region standard deviation .sigma..sub.g and an entropy constant K of the image.  Here, the entropy constant K refers to bit/symbol.  That
is, the entropy constant K can be a measure of information per symbol in the input image.  The comparison part 250 compares the adaptive threshold T1 calculated by the adaptive threshold calculation part 240 with the local region standard deviation
.sigma..sub.m of each region of the input image to determine the homogeneous region of the input image.  When the local region standard deviation .sigma..sub.m of one of the regions of the input image is less than the adaptive threshold T1, the
comparison part 240 determines that the region is a homogeneous region.
FIG. 3 is a flowchart illustrating a method of detecting a homogeneous region of an image using an adaptive threshold according to an embodiment of the present general inventive concept.  Referring to FIGS. 2 and 3, the global region standard
deviation .sigma..sub.g of the input image and the local region standard deviation .sigma..sub.m of each region of the input image are respectively calculated by the global region standard deviation calculation part 210 and the local region standard
deviation calculation part 220 (S310).  The global region standard deviation .sigma..sub.g of the input image and the local region standard deviation .sigma..sub.m of each region of the input image are then supplied to the homogeneous region
determination part 230
The adaptive threshold calculation part 240 in the homogeneous region determination part 230 calculates the adaptive threshold T1 based on the global region standard deviation .sigma..sub.g and the entropy constant K of the input image (S320).
The adaptive threshold T1 is calculated by the adaptive threshold calculation part 240 as follows.
A probability distribution P.sub.g of a global region of the input image with respect to brightness and a probability distribution P.sub.m of the homogeneous region with respect to brightness can be calculated from the global region standard
deviation .sigma..sub.g.  Assuming that the global region standard deviation has a wide range, the probability distribution P.sub.g of the global region of the input image with respect to the brightness can be approximated to a uniform density function
as illustrated in [Expression 1] below.
.function..apprxeq..times..sigma..times..sigma.&amp;lt;&amp;lt;.times..sigma..tim- es..times.  ##EQU00003##
wherein, P.sub.g denotes the probability distribution of the global region of the input image, .sigma..sub.g denotes the global region standard deviation of the input image, and x denotes a pixel value of each pixel of the input image.
Assuming that a standard deviation of the homogeneous region has a very narrow range, the probability distribution P.sub.m of the homogeneous region with respect to the brightness can be approximated to the following expression.
.function..apprxeq..times..sigma..times..sigma.&amp;lt;&amp;lt;.times..sigma..tim- es..times.  ##EQU00004##
The entropy of each local region is calculated based on the probability distributions P.sub.g(x) and P.sub.m(x) of the global and homogeneous regions obtained through [Expression 1] and [Expression 2], respectively, as illustrated in [Expression
3] below.
.times..times..sigma..sigma..times..times..times..times..times..apprxeq..- sigma..sigma..times..times..times..times..sigma..times..times.  ##EQU00005##
In [Expression 3], H(m) denotes the entropy of each local region as divided into M.times.M regions.  When the local region entropy H(m) is an entropy constant K, it can be expressed as the following.
.times..times..apprxeq..sigma..sigma..times..times..times..times..sigma..- apprxeq..times..times.  ##EQU00006##
Thus, assuming that the local region entropy H(m) is substantially the same as the entropy constant K, when the global region standard deviation .sigma..sub.g and log 2.sigma..sub.g are transposed to a term including the entropy constant K, only
the local region standard deviation .sigma..sub.m remains on the left term of the expression.  The right term can now be expressed as the following.
.times..sigma..times..times..times..sigma..times..times.  ##EQU00007##
Referring to [Expression 5], assuming that the local region entropy H(m) is substantially the same as the entropy constant K, the adaptive threshold T1 can be finally obtained by transposing the global region standard deviation .sigma..sub.g and
log 2.sigma..sub.g to the term including the entropy constant K. That is the adaptive threshold T1 can be set to be equal to the maximum local region standard deviation .sigma..sub.m of the homogeneous region, which can be obtained by solving for the
local region standard deviation .sigma..sub.m in [Expression 4].  Accordingly, the adaptive threshold T1 is
.times..sigma..times..times..times..sigma.  ##EQU00008## as shown in [Expression 5].
When the adaptive threshold T1 is calculated at operation S320, the comparison part 250 compares the local region standard deviation am of each region of the input image with the adaptive threshold T1 (S330).  When the local region standard
deviation .sigma..sub.m corresponding to one of the regions of the input image is less than the adaptive threshold T1 as a result of the comparison (S340), the corresponding local region is determined to be the homogeneous region (S350).  When the local
region standard deviation is not less than the adaptive threshold T1 as a result of the comparison (S340), the corresponding local region is determined to be not the homogeneous region (S360).
The present general inventive concept may be embodied as executable code in computer readable media including storage media such as magnetic storage media (ROMs, RAMs, floppy disks, magnetic tapes, etc.), and optically readable media (CD-ROMs,
DVDs, etc.), and carrier waves (transmission over the Internet).
Furthermore, a homogeneous region and a feature region of an image, which is not homogeneous, can be effectively separated by using a threshold adaptively calculated according to entropy of the image, thereby precisely detecting the homogeneous
region of the image.  Accordingly, a homogeneous region can be applied to diverse fields of image processing.
Apparatus to detect homogeneous region of image using adaptive threshold, Seung-hoon Han, Application number 11 237-942, Image Analysis, threshold value, standard deviation calculation, color histograms, first step, the block, brightness value, Patent Application, second step, present invention relates, respect to
S This application claims benefit under 35 U.S.C. .sctn.119(a) of Korean Patent Application No. 2004-113795, filed Dec. 28, 2004, the disclosure of which is incorporated herein in its entirety by reference.BACKGROUND OF THE INVENTION 1. Field of the Invention The present general inventive concept relates to an apparatus to detect a homogeneous region of an image, and a method thereof. More particularly, the present general inventive concept relates to a homogeneous region detector capable ofcorrectly detecting a homogeneous region of an image using an adaptive threshold appropriate to properties of the image, and a method thereof. 2. Description of the Related Art Generally, a homogeneous region in an image is used for a variety of image processing fields. For example, the homogeneous region may be used for estimation of image noise since a signal-to-noise (S/N) ratio of the homogeneous region is low. Also, due to spatial redundancy of the homogeneous region, efficiency of image compression can be improved using the homogeneous region. Furthermore, when detecting scene transition of video, the homogeneous region enhances motion tolerance of framedifferences. For image interpolation, an interpolation value can be obtained thorough a simple calculation in the homogeneous region, thereby saving costs. As explained above, the homogeneous region is applied to diverse fields of image processing. Therefore, correct detection of the homogeneous region is an important matter. FIG. 1 is a view illustrating a conventional method for detecting a homogeneous region of an image. Referring to FIG. 1, a conventional homogeneous region detector 100 comprises a local region standard deviation calculation part 10 and acomparison part 20. The local region standard deviation calculation part 10 divides an input image into M.times.M regions and calculates the standard deviation of each region. The comparison part 20 compares the standard deviation calculated withrespect to th
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