EP1153364A1 - Color image processing method and apparatus thereof - Google Patents

Color image processing method and apparatus thereof

Info

Publication number
EP1153364A1
EP1153364A1 EP00903005A EP00903005A EP1153364A1 EP 1153364 A1 EP1153364 A1 EP 1153364A1 EP 00903005 A EP00903005 A EP 00903005A EP 00903005 A EP00903005 A EP 00903005A EP 1153364 A1 EP1153364 A1 EP 1153364A1
Authority
EP
European Patent Office
Prior art keywords
color
pixels
image processing
pixel
distance
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP00903005A
Other languages
German (de)
French (fr)
Other versions
EP1153364A4 (en
Inventor
Hyun Doo Shin
Yang Lim Choi
Yining Dept. Elec. & Computer Engineering DENG
B. S. Dept. Electrical & Computer Eng. MANJUNATH
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Samsung Electronics Co Ltd
University of California
Original Assignee
Samsung Electronics Co Ltd
University of California
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Samsung Electronics Co Ltd, University of California filed Critical Samsung Electronics Co Ltd
Publication of EP1153364A1 publication Critical patent/EP1153364A1/en
Publication of EP1153364A4 publication Critical patent/EP1153364A4/en
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G06T5/70—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20192—Edge enhancement; Edge preservation

Definitions

  • the present invention relates to an image processing method, and more particularly, to a color image processing method which is a pre-processing method required for retrieving a color feature descriptor used in indexing and searching a color image.
  • color feature descriptors for indexing and searching color images are defined.
  • a color feature descriptor is retrieved from an input image.
  • the color image processing method in order to retrieve a color feature descriptor, an input image is segmented into a plurality of regions, quantized color vectors for the segmented regions are obtained, and then the color feature descriptor of a pertinent region is determined using the quantized color vectors.
  • noise components may exist in the image.
  • good color quantization is important in accurately representing color information in the image.
  • pre-processing such as filtering or noise removal is necessarily performed before quantization.
  • filtering methods such as vector median filtering or vector directional filtering have been employed.
  • filtering method used in conventional color image processing methods are uniformly applied to an image, non-noisy pixels may be modified, which causes edge blurring in the original image.
  • a feature of the present invention is embodied by a color image processing method includes the steps of (a) sorting image pixels according to the color distance between the image pixels and a central pixel, (b) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (c) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
  • the color image processing method may further include the step of defining a window having a predetermined size within an input color image, wherein the image pixels are pixels within the window.
  • the method preferably further includes the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
  • the predetermined number is preferably less than or equal to L/2.
  • L is a predetermined positive number indicating the size of an L ⁇ L window.
  • the method preferably further includes the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
  • the step (b) may include grouping the sorted pixels using a function based on a Fisher's discriminant estimation method.
  • the method may further include the steps of selecting / pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group, and performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group.
  • the method may further include the steps of selecting pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group, and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters, wherein T(n) is the maximum color distance within the peer group.
  • the method may further include the steps of selecting pixels whose number corresponds to the size of the peer group, ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group, and weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group, and performing color quantization using a value obtained by multiplying the average of the T(n) values of the whole image with a predetermined constant as the number of dusters.
  • the step (c) preferably includes replacing the central pixel X tract(n) with a new pixel X ' a (n) by the following Expression:
  • pfn are the pixels constituting the peer group and Wf are predetermined weights corresponding to pfn).
  • the step (c) preferably includes replacing the color vector of the central pixel with an average weighted by a predetermined weight that is larger for a pixel closer to the central pixel and is smaller for a pixel distant from the central pixel.
  • the predetermined weight is preferably a value determined by a standard Gaussian function.
  • the color image processing method may further include the step of performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)). wherein T(n) is the maximum color distance within one group.
  • a color image processing method including the steps of (a) receiving a color image frame and segmenting the same into a plurality of color images by a predetermined segmentation method, (b) sorting image pixels according to the color distance between the image pixels and a central pixel, with respect to an image selected among the segmented color images, (c) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (d) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
  • a color image processing method including the steps of (a) defining a window having a predetermined size within an input color image, (b) selecting pixels having a color vector similar to that of the central pixel within the window and defining the selected pixels as a group, and (c) performing filtering of blurring using only the pixels within the defined group.
  • the present invention is also embodied by a computer readable medium having program codes executable by a computer to perform a color image processing method, the method including the steps of (a) defining a window having a predetermined size within an input color image, (b) sorting image pixels according to the color distance between the image pixels and a central pixel, (c) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (d) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
  • the present invention provides a color image processing apparatus including sorting means for setting a window of a predetermined size within an input color image and sorting image pixels in the window according to the color distance between the image pixels and a central pixel, grouping means for grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and filtering means for performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
  • FIGS. 1A and IB are flow diagrams showing a color image processing method according to the present invention.
  • FIG. 2 is a block diagram of a color image processing apparatus according to the present invention.
  • a color image is input (step 100).
  • the color image may be in one region selected among image regions segmented by an appropriate segmentation method.
  • X 0 (n) which represents the color vector of a pixel positioned at a position n at the center of the L L window in an input color image, may be used to represent the corresponding pixel positioned at the center, that is a central pixel.
  • the corresponding color vectors of all pixels in the window are sorted in an ascending order according to the magnitude of the color distance dfn) (step 104).
  • the color vectors sorted in ascending order will be represented y Xfn).
  • color distance difference is calculated by Expression (2) (step 106): f l (n) ⁇ d l ⁇ 1 (n) ⁇ d l (n) (2)
  • step 108 color vectors in which f(n) is greater than a predetermined threshold Q are removed. That is. the color vectors in which f(n) is greater than a predetermined threshold Q are considered impulse noise to then be removed.
  • it is more preferably to perform the step 108 with respect to the beginning L/2 pixels and the latter L/2 pixels in ascending order, than to perform it with respect to all the (L : - 1 ) pixels in the L 'L window, except of the central pixel X n).
  • filtering is performed on pixels in a peer group to be described later, rather than on all the pixels in the L X L window.
  • the peer groups are obtained as follows.
  • the pixels sorted in ascending order according to their color distance from the color vector of the central pixel are divided into two groups.
  • the first group consists of 0th through (i -l)th pixels, and the second group consists of ;th through Rth pixels.
  • the Expression (4) is based on Fisher's discriminant estimation method.
  • the actual range of is from 1 through the numbers obtained by subtracting the number corresponding to color vectors of the pixels considered as impulse noise for removal from K. However, since the number corresponding to color vectors of the pixels considered as impulse noise and removed is not so large. it is assumed that the range of is from 1 through K.
  • the peer group P(n) when is reset to variables ranging from 0 through the value obtained in the step 1 12, the peer group P(n) consists of pixels p,(n).
  • W are the standard Gaussian weights corresponding to p,(n) (step 114).
  • the standard Gaussian weights W are determined by a standard Gaussian function. A pixel closer to the center of an image has a larger standard Gaussian weight and a pixel distant from the center has a smaller standard Gaussian weight. The procedure of replacing pixels in such a manner is smoothing or filtering.
  • the pixels in smooth regions are more heavily weighted than the pixels in highly noisy regions. Weighting the pixels in highly noisy regions more lightly than the pixels in the smooth regions is based on the analysis result of eye-perception, that is. the eye-perception is more sensitive to changes in detailed regions than in smooth regions.
  • the value obtained by multiplying a predetermined constant with the average of T(n) values of all images is preferably used as the number of clusters.
  • the color image processing method according to the present invention only the pixels having large color distances from the central pixel are removed, and then a peer group having a color vector similar to that of the central pixel is defined to then perform filtering thereon.
  • edge blurring of an image rarely occurs due to removal and filtering of impulse noise.
  • the information on the extent of quantization to be performed on an image can be obtained.
  • the color image processing method is programmable by a computer program. Codes and code segments constituting the computer program can be easily derived by a computer programmer in the art. Also, the program is stored in computer readable media and is readable and executable by the computer, thereby embodying the color image processing method.
  • the media include magnetic recording media, optical recording media, carrier wave media, and the like.
  • FIG. 2 is a block diagram of a color image processing apparatus according to the present invention.
  • the color image processing apparatus according to the present invention includes a segmenting unit 200. a sorting unit 202, an impulse noise removing unit 204. a grouping unit 206, a filtering unit 208 and a quantizing unit 210.
  • the segmenting unit 200 includes a sorting unit 202, an impulse noise removing unit 204. a grouping unit 206, a filtering unit 208 and a quantizing unit 210.
  • the segmenting unit 200 includes a sorting unit 202, an impulse noise removing unit 204. a grouping unit 206, a filtering unit 208 and a quantizing unit 210.
  • the 200 receives a color image frame and segments the color image frame into a plurality of color images by a predetermined segmentation method.
  • the sorting unit 202 sets an L ⁇ L window (L is a predetermined positive integer.) within the color images, and sorts the pixels within the window according to the color distance between each pixel and the central pixel. Thus, the sorting unit 202 outputs color vectors of the sorted pixels.
  • the impulse noise removing unit 204 removes the pixels that have a difference in color distance from the central pixel greater than a predetermined threshold, with respect to the beginning L/2 pixels and the latter L/2 pixels among the sorted pixels.
  • the grouping unit 206 receives color vectors of all the noise-removed pixels in the L ⁇ L window and divides the same into two groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, by calculating the function represented by the Expression (4) using the variance and average of the color distances between the sorted pixels.
  • the filtering unit 208 performs filtering by replacing the central pixels with pixels in a group having a small difference from the color vector of the central pixels in the window.
  • the quantizing unit 210 weights the color vectors of the respective pixels by exp(-T(n)), in which T(n) is the maximum color distance within a group having a small difference in the color vector from the central pixel within the window, and performs quantization using the value obtained by multiplying a predetermined constant with the average of T(n) values of all images as the number of clusters.
  • the information on the number of clusters for quantization to be performed can be obtained based on the smoothness or details of an image to be processed.
  • quantization is can be effectively performed using the information.
  • the present invention can be applied to the fields of color image indexing or searching applications.

Abstract

A color image processing method and apparatus thereof are provided. The color image processing method includes the steps of (a) sorting image pixels according to the color distance between the image pixels and a central pixel, (b) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (c) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel valves of pixels in the groups. The color image processing method can reduce the generation of edge blurring of the image in removing impulse noise from an image and filtering the same.

Description

COLOR IMAGE PROCESSING METHOD AND APPARATUS THEREOF
Technical Field
The present invention relates to an image processing method, and more particularly, to a color image processing method which is a pre-processing method required for retrieving a color feature descriptor used in indexing and searching a color image.
Background Art In object-based digital image compression standards such as MPEG-7, color feature descriptors for indexing and searching color images are defined. A color feature descriptor is retrieved from an input image.
In the color image processing method, in order to retrieve a color feature descriptor, an input image is segmented into a plurality of regions, quantized color vectors for the segmented regions are obtained, and then the color feature descriptor of a pertinent region is determined using the quantized color vectors. However. noise components may exist in the image. Also, good color quantization is important in accurately representing color information in the image. Thus, pre-processing such as filtering or noise removal is necessarily performed before quantization. Conventionally, in order to remove noise from an image, filtering methods such as vector median filtering or vector directional filtering have been employed. However, since filtering method used in conventional color image processing methods are uniformly applied to an image, non-noisy pixels may be modified, which causes edge blurring in the original image.
Disclosure of the Invention
To solve the above problem, it is an objective of the present invention to provide a color image processing method and apparatus, by which filtering can be performed without edge blurring of the color image and noise in the color image can be eliminated, the method being a pre-processing method required for retrieving a color feature descriptor for indexing and searching the color image.
It is another object of the present invention to provide a computer readable medium having a program executable by a computer to perform the color image processing method. A feature of the present invention is embodied by a color image processing method includes the steps of (a) sorting image pixels according to the color distance between the image pixels and a central pixel, (b) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (c) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
The color image processing method may further include the step of defining a window having a predetermined size within an input color image, wherein the image pixels are pixels within the window. Before the step (b), the method preferably further includes the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
The predetermined number is preferably less than or equal to L/2. in which L is a predetermined positive number indicating the size of an LχL window.
Before the step (b), the method preferably further includes the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels. Also, the step (b) may include grouping the sorted pixels using a function based on a Fisher's discriminant estimation method.
The step (b) may include the sub-steps of (b-1) setting a first group consisting of 0th through (i-l)th pixels, and a second group consisting of th through Rth pixels, wherein is an integer from 0 through R and R=L:- 1. (b-2) obtaining the respective averages of the color distance differences for pixels of the first and second groups by the following Expressions: 1 ' ~~ ' 1 κ ι,(i) = - ∑ dj (n) a2 (i) = . ∑ dj (n)
1 r- K + l - and ./;='' . (b-3) obtaining the
respective variances of the color distance differences for pixels of the first and second groups are obtained by the following Expressions:
calculating a value J(i) by the following Expression, using the obtained average and variance:
. and (b-5) obtaining the value of which makes
J(i) maximum by the following Expression:
mfnj - (max .J(i)j
' , and selecting pixels ranging from a pixel having a
small color distance to a pixel having the obtained value of to determine the same as a peer group P(n).
Also, after the step (b-5). the method may further include the steps of selecting / pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group, and performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group.
Alternatively, after the step (b-5). the method may further include the steps of selecting pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group, and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters, wherein T(n) is the maximum color distance within the peer group.
Also, after the step (b-5), the method may further include the steps of selecting pixels whose number corresponds to the size of the peer group, ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group, and weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group, and performing color quantization using a value obtained by multiplying the average of the T(n) values of the whole image with a predetermined constant as the number of dusters.
The step (c) preferably includes replacing the central pixel X„(n) with a new pixel X 'a(n) by the following Expression:
where pfn) are the pixels constituting the peer group and Wf are predetermined weights corresponding to pfn).
Also, the step (c) preferably includes replacing the color vector of the central pixel with an average weighted by a predetermined weight that is larger for a pixel closer to the central pixel and is smaller for a pixel distant from the central pixel.
The predetermined weight is preferably a value determined by a standard Gaussian function.
The color image processing method may further include the step of performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)). wherein T(n) is the maximum color distance within one group.
According to another aspect of the present invention, there is provided a color image processing method including the steps of (a) receiving a color image frame and segmenting the same into a plurality of color images by a predetermined segmentation method, (b) sorting image pixels according to the color distance between the image pixels and a central pixel, with respect to an image selected among the segmented color images, (c) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (d) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups. According to still another aspect of the present invention, there is provided a color image processing method including the steps of (a) defining a window having a predetermined size within an input color image, (b) selecting pixels having a color vector similar to that of the central pixel within the window and defining the selected pixels as a group, and (c) performing filtering of blurring using only the pixels within the defined group.
The present invention is also embodied by a computer readable medium having program codes executable by a computer to perform a color image processing method, the method including the steps of (a) defining a window having a predetermined size within an input color image, (b) sorting image pixels according to the color distance between the image pixels and a central pixel, (c) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and (d) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups. Alternatively, the present invention provides a color image processing apparatus including sorting means for setting a window of a predetermined size within an input color image and sorting image pixels in the window according to the color distance between the image pixels and a central pixel, grouping means for grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, and filtering means for performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
Brief Description of the Drawings
The above objects and advantages of the present invention will become more apparent by describing in detail preferred embodiments thereof with reference to the attached drawings in which:
FIGS. 1A and IB are flow diagrams showing a color image processing method according to the present invention; and
FIG. 2 is a block diagram of a color image processing apparatus according to the present invention.
Best mode for carrying out the Invention
Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Referring to FIG. 1A illustrating a color image processing method according to the present invention, a color image is input (step 100). The color image may be in one region selected among image regions segmented by an appropriate segmentation method.
Next, with respect to color vectors of all pixels in a LχL window in an input color image, the color distance dfn) from the color vector X0(n) of a pixel positioned at a position n at the center of the L*L window is represented by Expression (1) (step 102):
Dis lan ce ( Xi (n), X0 (n)) , ..
wherein L is an arbitrary positive integer, and assuming that K=Lr- 1 , is an integer from 0 through K. Also, for convenience' sake of explanation, X0(n), which represents the color vector of a pixel positioned at a position n at the center of the L L window in an input color image, may be used to represent the corresponding pixel positioned at the center, that is a central pixel. Next, the corresponding color vectors of all pixels in the window are sorted in an ascending order according to the magnitude of the color distance dfn) (step 104). Hereinbelow. the color vectors sorted in ascending order will be represented y Xfn). Now, with respect to the color vectors sorted in ascending order, color distance difference is calculated by Expression (2) (step 106): fl (n) ^ dl^ 1(n)~ dl(n) (2)
and then color vectors in which f(n) is greater than a predetermined threshold Q are removed (step 108). That is. the color vectors in which f(n) is greater than a predetermined threshold Q are considered impulse noise to then be removed. According to experimental confirmation, it is more preferably to perform the step 108 with respect to the beginning L/2 pixels and the latter L/2 pixels in ascending order, than to perform it with respect to all the (L:- 1 ) pixels in the L 'L window, except of the central pixel X n). Also, according to the present invention, in order to prevent edge blurring of an image, filtering is performed on pixels in a peer group to be described later, rather than on all the pixels in the LXL window. The peer groups are obtained as follows.
First, the pixels sorted in ascending order according to their color distance from the color vector of the central pixel are divided into two groups. The first group consists of 0th through (i -l)th pixels, and the second group consists of ;th through Rth pixels.
Next, the average of the color distance differences for pixels of the first group and the average of the color distance differences for pixels of the second group are obtained as represented by Expressions (3a) and (3b), respectively:
and the variance of the color distance differences for pixels of the first group and the variance of the color distance difference for pixels of the second group are obtained as represented by Expressions (3c) and (3d), respectively:
$2 ( = ∑ \d,(n) - ι(
1= 1 • (3d)
wherein is a newly defined integer ranging from 0 through K.
Then, using the obtained average and variance, a value J(i) represented by Expression (4) is calculated (step 110):
wherein is an integer ranging from 1 through K. The Expression (4) is based on Fisher's discriminant estimation method.
Here, the actual range of is from 1 through the numbers obtained by subtracting the number corresponding to color vectors of the pixels considered as impulse noise for removal from K. However, since the number corresponding to color vectors of the pixels considered as impulse noise and removed is not so large. it is assumed that the range of is from 1 through K.
Next, the value of which makes J(i) maximum is obtained by Expression (5): arg m(n) - {max ,] ft)}
• (5)
to then detemiine the obtained value of ; as the magnitude of a peer group P(n) (step 112). In other words, when is reset to variables ranging from 0 through the value obtained in the step 1 12, the peer group P(n) consists of pixels p,(n).
Referring to FIG. I B. the central pixel X„(n) is replaced with a new pixel X'o(n) by Expression (6):
where W, are the standard Gaussian weights corresponding to p,(n) (step 114). Here, the standard Gaussian weights W, are determined by a standard Gaussian function. A pixel closer to the center of an image has a larger standard Gaussian weight and a pixel distant from the center has a smaller standard Gaussian weight. The procedure of replacing pixels in such a manner is smoothing or filtering.
Supposing the maximum color distance in each peer group d..„ ,(n) is T(n), exp(-T(n)) is applied to the color vectors of the respective pixels during color quantization (step 116). The maximum color distance dπι(11J ,(n) in each peer group indicates the largest value of the color distances of pixels whose number correspond to the size of the peer group ranging from the pixel having the minimum color distance, among the pixels sorted in ascending order according to their color distance from the central pixel. Thus, the smaller the maximum color distance dm(n ,(n) in each peer group, the shorter the color distance from the central pixel, so that dm(n)_,(n) is an indicator of the smoothness of an image. Also, since exp(-T(n)) is weighted to the color vectors of the respective pixels, the pixels in smooth regions are more heavily weighted than the pixels in highly noisy regions. Weighting the pixels in highly noisy regions more lightly than the pixels in the smooth regions is based on the analysis result of eye-perception, that is. the eye-perception is more sensitive to changes in detailed regions than in smooth regions.
According to the present invention, during quantization, the value obtained by multiplying a predetermined constant with the average of T(n) values of all images is preferably used as the number of clusters. In other words, during quantization, fewer clusters are used in smooth regions where the T(n) value is small, and more clusters are used in highly noisy regions where the T(n) value is large. In the color image processing method according to the present invention, only the pixels having large color distances from the central pixel are removed, and then a peer group having a color vector similar to that of the central pixel is defined to then perform filtering thereon. Thus, edge blurring of an image rarely occurs due to removal and filtering of impulse noise. Also, according to the present invention, the information on the extent of quantization to be performed on an image can be obtained.
The color image processing method is programmable by a computer program. Codes and code segments constituting the computer program can be easily derived by a computer programmer in the art. Also, the program is stored in computer readable media and is readable and executable by the computer, thereby embodying the color image processing method. The media include magnetic recording media, optical recording media, carrier wave media, and the like.
Also, the color image processing method can be implemented on a color image processing apparatus. FIG. 2 is a block diagram of a color image processing apparatus according to the present invention. Referring to FIG. 2, the color image processing apparatus according to the present invention includes a segmenting unit 200. a sorting unit 202, an impulse noise removing unit 204. a grouping unit 206, a filtering unit 208 and a quantizing unit 210. In the operation of the color image processing apparatus, the segmenting unit
200 receives a color image frame and segments the color image frame into a plurality of color images by a predetermined segmentation method.
The sorting unit 202 sets an LχL window (L is a predetermined positive integer.) within the color images, and sorts the pixels within the window according to the color distance between each pixel and the central pixel. Thus, the sorting unit 202 outputs color vectors of the sorted pixels.
The impulse noise removing unit 204 removes the pixels that have a difference in color distance from the central pixel greater than a predetermined threshold, with respect to the beginning L/2 pixels and the latter L/2 pixels among the sorted pixels.
The grouping unit 206 receives color vectors of all the noise-removed pixels in the LχL window and divides the same into two groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum, by calculating the function represented by the Expression (4) using the variance and average of the color distances between the sorted pixels. The filtering unit 208 performs filtering by replacing the central pixels with pixels in a group having a small difference from the color vector of the central pixels in the window.
The quantizing unit 210 weights the color vectors of the respective pixels by exp(-T(n)), in which T(n) is the maximum color distance within a group having a small difference in the color vector from the central pixel within the window, and performs quantization using the value obtained by multiplying a predetermined constant with the average of T(n) values of all images as the number of clusters.
In the above-described color image processing apparatus according to the present invention, since the pixels having large color distances from the central pixel are removed, a peer group having a color vector similar to that of the central pixel is defined to then perform filtering thereon. Thus, edge blurring of an image rarely occurs due to removal and filtering of impulse noise. Also, according to the present invention, the information on the number of clusters for quantization to be performed can be obtained based on the smoothness or details of an image to be processed. Thus, quantization is can be effectively performed using the information. As described above, according to the present invention, in removing impulse noise from an image and filtering the same, the generation of edge blurring of the image can be reduced.
Industrial Applicability
The present invention can be applied to the fields of color image indexing or searching applications.

Claims

What is claimed is:
1. A color image processing method comprising the steps of:
(a) sorting image pixels according to the color distance between the image pixels and a central pixel:
(b) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum; and
(c) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
2. The color image processing method according to claim 1 , further comprising the step of defining a window having a predetermined size within an input color image, wherein the image pixels are pixels within the window.
3. The color image processing method according to claim 1 . before the step (b). further comprising the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
4. The color image processing method according to claim 3, wherein the predetermined number is less than or equal to L/2, in which L is a predetermined positive integer indicating the size of an LχL window.
5. The color image processing method according to claim 2, before the step (b). further comprising the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
6. The color image processing method according to any of claims 1 through 5, wherein the step (b) includes grouping the sorted pixels using a function based on a Fisher's discriminant estimation method.
7. The color image processing method according to claim 1, wherein the step (b) comprises the sub-steps of:
(b-1) setting a first group consisting of 0th through (i-1 )th pixels, and a second group consisting of ixh through Rth pixels, wherein is an integer from 0 through K and K=V- 1 ;
(b-2) obtaining the respective averages of the color distance differences for pixels of the first and second groups by the following Expressions:
(b-3) obtaining the respective variances of the color distance differences for pixels of the first and second groups are obtained by the following Expressions:
(b-4) calculating a value J(i) by the following Expression, using the obtained average and variance:
aι(l) - aι(l)
J(U = s (ij + s2(ι) and
(b-5) obtaining the value of / which makes J(i) maximum by the following Expression: arg m(n) = {max J(i)}
and selecting pixels ranging from a pixel having a small color distance to a pixel having the obtained value of to determine the same as a peer group P(n).
8. The color image processing method according to claim 7, after the step (b-5), further comprising the steps of: selecting pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group.
9. The color image processing method according to claim 7, after the step (b-5), further comprising the steps of: selecting i pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters, wherein T(n) is the maximum color distance within the peer group.
10. The color image processing method according to claim 7, after the step (b-5), further comprising the steps of: selecting pixels whose number corresponds to the size of the peer group, ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group, and performing color quantization using a value obtained by multiplying the average of the T(n) values of the whole image with a predetermined constant as the number of clusters.
11. The color image processing method according to claim 7, wherein the step (c) includes replacing the central pixel X0(n) with a new pixel X0' (n) by the following Expression:
X'o (m) = T / i'
ι=0
where pfn) are the pixels constituting the peer group and , W are predetermined weights corresponding to p,(n).
12. The color image processing method according to claim 1. wherein the step (c) includes replacing the color vector of the central pixel with an average weighted by a predetermined weight that is larger for a pixel closer to the central pixel and is smaller for a pixel distant from the central pixel.
13. The color image processing method according to claim 12, wherein the predetermined weight is a value determined by a standard Gaussian function.
14. The color image processing method according to claim 1, further comprising the step of performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within one group.
15. A color image processing method comprising the steps of:
(a) receiving a color image frame and segmenting the same into a plurality of color images by a predetermined segmentation method;
(b) sorting image pixels according to the color distance between the image pixels and a central pixel, with respect to an image selected among the segmented color images;
(c) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum; and (d) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
16. The color image processing method according to claim 15, before the step (b). further comprising the step of defining a window having a predetermined size within the selected color image, wherein the image pixels are pixels within the window.
17. The color image processing method according to claim 15, before the step (b), further comprising the step of removing pixels having a difference in the color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
18. The color image processing method according to claim 17, wherein the predetermined number is less than or equal to L/2, in which L is a predetermined positive integer indicating the size of an L L window.
19. The color image processing method according to claim 15, before the step (b). further comprising the step of removing pixels having a difference in color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
20. The color image processing method according to any of claims 15 through 19, wherein the step (b) includes grouping the sorted pixels using a function based on a Fisher's discriminant estimation method.
21. The color image processing method according to claim 15, wherein the step (b) comprises the sub-steps of:
(b-1) setting a first group consisting of 0th through (i-l)th pixels, and a second group consisting of ith through Kth pixels, wherein i is an integer from 0 through K and A"=L2- 1;
(b-2) obtaining the respective averages of the color distance differences for pixels of the first and second groups by the following Expressions:
(b-3) obtaining the respective variances of the color distance differences for pixels of the first and second groups are obtained by the following Expressions:
S J (
(b-4) calculating a value J(i) by the following Expression, using the obtained average and variance:
(b-5) obtaining the value of i which makes J(i) maximum by the following
Expression: arg m(n) - {max J(ι)} l
and selecting pixels ranging from a pixel having a small color distance to a pixel having the obtained value of to be determined as a peer group P(n).
22. The color image processing method according to claim 15, wherein the step (c) includes replacing the color vector of the central pixel with an average weighted by a predetermined weight that is larger for a pixel closer to the central pixel and is smaller for a pixel distant from the central pixel.
23. The color image processing method according to claim 22, wherein the predetermined weight is a value determined by a standard Gaussian function.
24. The color image processing method according to claim 15, wherein the step (c) includes replacing the central pixel X0(n) with a new pixel X'0(n) by the following Expression:
where pfn) are the pixels constituting the peer group and , VV are predetermined weights corresponding to p,(n).
25. The color image processing method according to claim 21, after the step (b-5), further comprising the steps of: selecting i pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group.
26. The color image processing method according to claim 21 , after the step (b-5), further comprising the steps of: selecting pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters.
27. The color image processing method according to claim 19, after the step (b-5), further comprising the steps of: selecting pixels whose number corresponds to the size of the peer group, ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within the peer group, and performing color quantization using a value obtained by multiplying the average of the T(n) values of the whole image with a predetermined constant as the number of clusters.
28. The color image processing method according to claim 24, after the step (b-5), further comprising the steps of: selecting i pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters.
29. The color image processing method according to claim 24, after the step (b-5), further comprising the steps of: selecting pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the peer group; and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters.
30. A color image processing method comprising the steps of:
(a) defining a window having a predetermined size within an input color image; (b) selecting pixels having a color vector similar to that of the central pixel within the window and defining the selected pixels as a group; and
(c) performing filtering of blurring using only the pixels within the defined group.
31. A computer readable medium having program codes executable by a computer to perform a color image processing method, the method comprising the steps of:
(a) defining a window having a predetermined size within an input color image; (b) sorting image pixels according to the color distance between the image pixels and a central pixel;
(c) grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum; and (d) performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
32. The computer readable medium according to claim 31, wherein before the step (c), the color image processing method further comprises the step of removing pixels having a difference in the color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
33. The computer readable medium according to claim 31, wherein the color image processing method further comprises the steps of: selecting i pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the selected group; and performing color quantization by weighting the color vectors of the respective pixels exp(-T(n)), wherein T(n) is the maximum color distance within the peer group.
34. The computer readable medium according to claim 31 , wherein the color image processing method further comprises the steps of: selecting pixels ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the selected group; and obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters.
35. The computer readable medium according to claim 31, wherein the color image processing method further comprises the steps of: selecting pixels whose number corresponds to the size of the peer group, ranging from the pixel having the minimum color distance among the pixels sorted according to the color distance from the central pixel and setting the largest value of the color distances of the selected pixels as the maximum color distance within the selected group; and weighting the color vectors of the respective pixels by exp(-T(n)), wherein
T(n) is the maximum color distance within the selected group, and performing color quantization using a value obtained by multiplying the average of the T(n) values of the whole image with a predetermined constant as the number of clusters.
36. The computer readable medium according to claim 31, wherein the color image processing method further comprises the step of receiving a color image frame and segmenting the same into a plurality of color images by a predetermined segmentation method, wherein the color image is an image selected from the plurality of color images.
37. A color image processing apparatus comprising: sorting means for setting a window of a predetermined size within an input color image and sorting image pixels in the window according to the color distance between the image pixels and a central pixel; grouping means for grouping the sorted pixels into groups in which the difference in the intragroup color distance is minimum and the difference in the intergroup color difference is maximum; and filtering means for performing filtering by replacing a central pixel value with a predetermined pixel value determined by pixel values of pixels in the groups.
38. The color image processing apparatus according to claim 37, further comprising quantizing means for performing color quantization by weighting the color vectors of the respective pixels by exp(-T(n)), wherein T(n) is the maximum color distance within a group having the smallest difference in the color vector from the central pixel within the window.
39. The color image processing apparatus according to claim 37, further comprising quantizing means for obtaining the average of T(n) values of the whole image and performing color quantization using a value obtained by multiplying the average with a predetermined constant as the number of clusters, wherein T(n) is the maximum color distance within a group having the smallest difference in the color vector from the central pixel within the window.
40. The color image processing apparatus according to claim 37, further comprising quantizing means for weighting the color vectors of the respective pixels by exp(-T(n)), and performing color quantization using a value obtained by multiplying the average of T(n) values of the whole image with a predetermined constant as the number of clusters, wherein T(n) is the maximum color distance within a group having the smallest difference in the color vector from the central pixel within the window.
41. The color image processing apparatus according to claim 37, further comprising impulse noise removing means for removing pixels having a difference in the color distance from the central pixel greater than or equal to a predetermined threshold, with respect to a predetermined number of pixels at the beginning and latter parts among the sorted pixels.
42. The color image processing apparatus according to claim 37, further comprising segmenting means for receiving a color image frame and segmenting the same into a plurality of color images by a predetermined segmentation method, wherein the color image is an image selected from the plurality of color images.
EP00903005A 1999-02-05 2000-02-03 Color image processing method and apparatus thereof Withdrawn EP1153364A4 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US11874199P 1999-02-05 1999-02-05
US118741P 1999-02-05
PCT/KR2000/000090 WO2000046749A1 (en) 1999-02-05 2000-02-03 Color image processing method and apparatus thereof

Publications (2)

Publication Number Publication Date
EP1153364A1 true EP1153364A1 (en) 2001-11-14
EP1153364A4 EP1153364A4 (en) 2002-10-30

Family

ID=22380461

Family Applications (1)

Application Number Title Priority Date Filing Date
EP00903005A Withdrawn EP1153364A4 (en) 1999-02-05 2000-02-03 Color image processing method and apparatus thereof

Country Status (8)

Country Link
EP (1) EP1153364A4 (en)
JP (1) JP3558985B2 (en)
KR (1) KR100439697B1 (en)
CN (1) CN1209735C (en)
AU (1) AU2464500A (en)
MY (1) MY127890A (en)
TW (1) TW463133B (en)
WO (1) WO2000046749A1 (en)

Families Citing this family (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100378351B1 (en) * 2000-11-13 2003-03-29 삼성전자주식회사 Method and apparatus for measuring color-texture distance, and method and apparatus for sectioning image into a plurality of regions using the measured color-texture distance
JP3862613B2 (en) 2002-06-05 2006-12-27 キヤノン株式会社 Image processing apparatus, image processing method, and computer program
GB2400257A (en) * 2003-04-05 2004-10-06 Autodesk Canada Inc Removal of grain
US7372991B2 (en) 2003-09-26 2008-05-13 Seiko Epson Corporation Method and apparatus for summarizing and indexing the contents of an audio-visual presentation
US20060103892A1 (en) * 2004-11-18 2006-05-18 Schulze Mark A System and method for a vector difference mean filter for noise suppression
JP2009505265A (en) * 2005-08-17 2009-02-05 エヌエックスピー ビー ヴィ Method and apparatus for suppressing noise in a digital signal sequence, corresponding computer program, and corresponding computer-readable storage medium
JP4001162B2 (en) * 2005-11-04 2007-10-31 オムロン株式会社 Image processing method, image processing program and storage medium therefor, and image processing apparatus
US7952646B2 (en) * 2006-12-27 2011-05-31 Intel Corporation Method and apparatus for content adaptive spatial-temporal motion adaptive noise reduction
CN101546425B (en) * 2009-05-05 2011-04-13 广东工业大学 A vector filtering method of double-threshold type color image
KR101290200B1 (en) * 2012-02-13 2013-07-30 중앙대학교 산학협력단 Apparatus and method for reducing impulse noise
CN103778611B (en) * 2014-01-26 2016-08-17 天津大学 Utilize the switch weight vectors median filter method of rim detection
CN105809630B (en) * 2014-12-30 2019-03-12 展讯通信(天津)有限公司 A kind of picture noise filter method and system
CN104899899A (en) * 2015-06-12 2015-09-09 天津大学 Color quantification method based on density peak value
CN113298790B (en) * 2021-05-31 2023-05-05 奥比中光科技集团股份有限公司 Image filtering method, device, terminal and computer readable storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4541116A (en) * 1984-02-27 1985-09-10 Environmental Research Institute Of Mi Neighborhood image processing stage for implementing filtering operations
EP0318866A2 (en) * 1987-12-04 1989-06-07 Polaroid Corporation Method for enhancing image data by noise reduction or sharpening
US5684897A (en) * 1992-02-19 1997-11-04 Ezel Inc. Method for quantizing color image data by minimizing least squares error of binary coding

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5222154A (en) * 1991-06-12 1993-06-22 Hewlett-Packard Company System and method for spot color extraction
KR0149517B1 (en) * 1992-07-18 1998-10-15 강진구 Multi-step type nonlinear filter for detecting edge and eliminating noise
KR100230254B1 (en) * 1996-04-17 1999-11-15 윤종용 Circuit and method for reducing a noise
KR100219544B1 (en) * 1997-05-26 1999-09-01 윤종용 Selectivity bandwidth pass filter
JPH1188705A (en) * 1997-09-11 1999-03-30 Matsushita Electric Ind Co Ltd Image reader

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4541116A (en) * 1984-02-27 1985-09-10 Environmental Research Institute Of Mi Neighborhood image processing stage for implementing filtering operations
EP0318866A2 (en) * 1987-12-04 1989-06-07 Polaroid Corporation Method for enhancing image data by noise reduction or sharpening
US5684897A (en) * 1992-02-19 1997-11-04 Ezel Inc. Method for quantizing color image data by minimizing least squares error of binary coding

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
JIA-GUU LEU: "Image smoothing based on pixel grouping" PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING. (ICIP). WASHINGTON, OCT. 23 - 26, 1995, LOS ALAMITOS, IEEE COMP. SOC. PRESS, US, vol. 3, 23 October 1995 (1995-10-23), pages 535-538, XP010197024 ISBN: 0-7803-3122-2 *
See also references of WO0046749A1 *

Also Published As

Publication number Publication date
JP3558985B2 (en) 2004-08-25
CN1341246A (en) 2002-03-20
KR20010113666A (en) 2001-12-28
WO2000046749A1 (en) 2000-08-10
MY127890A (en) 2006-12-29
JP2002536749A (en) 2002-10-29
CN1209735C (en) 2005-07-06
TW463133B (en) 2001-11-11
EP1153364A4 (en) 2002-10-30
AU2464500A (en) 2000-08-25
KR100439697B1 (en) 2004-07-14

Similar Documents

Publication Publication Date Title
US6778697B1 (en) Color image processing method and apparatus thereof
Wang et al. Best neighborhood matching: An information loss restoration technique for block-based image coding systems
US6535632B1 (en) Image processing in HSI color space using adaptive noise filtering
EP1519589A2 (en) Object tracking in vector images
WO2000046749A1 (en) Color image processing method and apparatus thereof
CN111260738A (en) Multi-scale target tracking method based on relevant filtering and self-adaptive feature fusion
AU753599B2 (en) Color image processing method and apparatus thereof
CN110827265B (en) Image anomaly detection method based on deep learning
US6970268B1 (en) Color image processing method and apparatus thereof
CN115908154A (en) Video late-stage particle noise removing method based on image processing
EP1509882A1 (en) Scene change detector algorithm in image sequence
Mohanty et al. A survey on moving object detection using background subtraction methods in video
US5862263A (en) Fractal image compression device and method using perceptual distortion measurement
CN112084371B (en) Movie multi-label classification method and device, electronic equipment and storage medium
Choi et al. Fast super-resolution algorithm using ELBP classifier
CN107424173B (en) Target tracking method based on extended local invariant feature description
Azimi-Sadjadi et al. Neural network decision directed edge-adaptive Kalman filter for image estimation
CN111291602A (en) Video detection method and device, electronic equipment and computer readable storage medium
JP2980810B2 (en) Motion vector search method and apparatus
KR102556777B1 (en) Efficient image restoration method and device based k-NN search
CN110248182B (en) Scene segment shot detection method
Prakash et al. Intelligent systems for redundancy removal with proficient run-length coding and statistical analysis using regression
Radha et al. Threshold based segmentation using median filter for sign language recognition system
EP1779670A1 (en) Adaptive classification system and method for mixed graphic and video sequences
Arora STANDARD DEVIATION BASED IMPLUSE NOISE FILTER

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20010802

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE

AX Request for extension of the european patent

Free format text: AL;LT;LV;MK;RO;SI

A4 Supplementary search report drawn up and despatched

Effective date: 20020916

AK Designated contracting states

Kind code of ref document: A4

Designated state(s): AT BE CH CY DE DK ES FI FR GB GR IE IT LI LU MC NL PT SE

RIC1 Information provided on ipc code assigned before grant

Free format text: 7G 06T 1/00 A, 7G 06T 7/60 B, 7G 06T 5/20 B, 7G 06T 7/40 B

17Q First examination report despatched

Effective date: 20050222

17Q First examination report despatched

Effective date: 20050222

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20100901