Image Processing : Basic Concept

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Image Processing Basic Concept
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Imaging Systems Overview

• Consists of two primary components
• Hardware Image acquisition system, computer,
  and display devices
• Software Image manipulation(??), analysis, and
  processing

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• Image is accessed (??) as a 2-D array (??) of
  data, where each data point is referred to as a
  pixel (??)
• Notation
• I(r,c) Brightness (??) of image at the pt
  (r,c)
• where
• r row(?), and c column(?)

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Visible Light Imaging

• Reflectance (??) function determines manner in
• which objects (??) reflect light

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• Sensors Converts (??) light energy into
  electrical energy

a) Single imaging sensor b) Linear ( line)
sensor c) 2-D or array sensor

• CCD 4kx4k CMOS less power, cheaper, image
  quality not as good as CCD

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Image Representation

• Optical (??) image Collection of spatially
  distributed (????) light energy measured by an
  image sensor to generate I(r,c)
• Matrix 2-D array like the image model,
• I(r,c)
• Vector One row or column in a matrix

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Image Types

• Binary (???) images Simplest type of images,
  which can take two values, typically black or
  white, or 0 or 1
• Gray scale (??) images One-color or monochrome
  images that contains only brightness information
  and no color information
• Color images 3 band monochrome images, where
  each band corresponds to a different color,
  typically red, blue and green or RGB

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• Color pixel vector Single pixels values for a
  color image, (R,G,B)
• Multispectral(???) Images Images of many bands
  containing information outside of the visible
  spectrum(????)

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Color Transform/Color Model

• Mathematical model or algorithm to map(??) RGB
  data into another color space (????)
• Decouples (??) brightness and color information
• Hue(??)/Saturation(???)/Lightness(??) (HSL) Color
  Transform
• Describes colors in terms that we can more
  readily understand

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• Hue corresponds to color, saturation corresponds
  to the amount of white in color, and lightness is
  the brightness
• For example a deep, bright orange color would
  have a large intensity (bright), a hue of
  orange , and a high value of saturation
  (deep(??))
• But in terms of RGB components, this color would
  have the values as R 245, G 110, and B20

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• Equations for mapping RGB to HSL are
• where

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Digital Image File Formats

• Bitmap images (raster images) Images that can be
  represented by our image model, I (r,c)

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• Image file header (????) A set of parameters
  (??) found at the start of the file image and
  contains information regarding
• Number of rows (??)(height, ?)
• Number of columns (??)(width, ?)
• Number of bands (???)
• Number of bits per pixel (?????? ??)(bpp)
• File type (????)

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• Look-up table (LUT) Used for storing RGB values
  for 8-bit color images

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• Common image file formats are
• BIN, RAW
• PPM,PBM,PGM
• BMP
• JPEG
• TIFF
• GIF
• RAS
• SGI
• PNG
• PICT, FPX
• EPS
• VIP

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Matlab ????I/O??????

• ?Matlab?,?????(pixel)?????0?1???????1????,0?????
• ???????RGB,??? red(?) ?green(?) ?blue(?)???????
• ?????????????,??????,?????????

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Matlab ????I/O??????

• ?????????????,?????????????????RGB ??????????

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Matlab ????I/O??????

• Show ???imshow( )
• ??????imshow(???? A,?? N) ,?????? A?N???????????
• ?N???,??24?????,???256???
• ????A?????A( , , 3)???????,A( , ,
  1)??????? A( , , 2)??????? A( , ,
  3)????????

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Matlab ????I/O??????

• ????????????,??????????????,???????????????imshow
  (???? A, lim_l lim_h)
• ?????? A ??????lim_l,????????lim_h,??????

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Matlab ????I/O??????

• ???????????,????Matlab?workspace?,??imread(????)
  ??,??imshow( )???????
• imwrite( )????????????,??????imwrite(????,
  ????????, ????)

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Ex2_1.m

• clear close all
• Aimread('1.bmp')
• figure ?????
• imshow(A)
• size(A)
• figure
• imshow(A(,,2)) ?show????
• imwrite(A(,,2),'ex2_1.tif','tif')
• BA(100150,150200,1) ????????
• figure
• imshow(B)
• figure
• imshow(B,100 200)

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Convolution Mask (????)

• Mask Operation (????)
• ????? 3 x 3 (???? 5 x 5, 7 x 7)

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• Convolution Consist of following process
• Overlay (??) the mask (??) on the image
• Multiply the coincident terms (???????)
• Sum all the results (?????)
• Move to the next pixel, across the entire image

Convolution mask for first order hold (?????????)
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• Spatial Filters (?????)
• Operate on raw (???) image data in the (r,c)
  space, by considering small neighborhoods (??),
  3x3, 5x5, 7x7, and moving sequentially
  (???)across and down the image
• Returns a result based on a linear (??) or
  nonlinear (???) operation

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• Consists of three types of filters
• Mean filters (?????)
• Median filters (?????)
• Enhancement filters (?????)
• Many spatial filters are linear filters
  implemented with a convolution mask (????????)
  the result is a weighted sum (???) of a pixel and
  its neighbors

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• Mask coefficients (??) tend to effect (??) the
  image in the following general ways
• Coefficients are positive (??) blurs (???) the
  image
• Coefficients are alternating positive and
  negative (????) sharpens (??) the image
• Coefficients sum to 1 (?????1) brightness
  retained (??)
• Coefficients sum to 0 (?????1) dark (?) image

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• Mean filters (?????)
• Averaging filters (?????)
• Tend to blur (???) the image
• Adds a softer look to the image
• Example 3x3 convolution mask (????)

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Mean filter
Mean filtered image
Original image
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• Median filters (?????)
• Nonlinear filter
• Sorts (??) the pixel values in a small
  neighborhood and replaces the center (???) pixel
  with the middle value (???) in the sorted list
  (?????)
• Output image (????) needs to be written to a
  separate image (a buffer (???)), so that results
  are not corrupted (??)
• Neighborhood (??) can be of any size but 3x3, 5x5
  and 7x7 are typical (??)

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Median filter
Original image with salt and pepper noise (?????)
Median filtered image (3x3)
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Ex3_2.m (mean filter ? median filter???)

• Image ???
• Image_noisy ??????
• Image_low ???????????
• Image_med ???????????
• Imageimread('ex2_1.tif')
  ????
• ??????????????,??0.06 ?????
• Image_noisy imnoise (Image ,'salt
  pepper',0.06)
• Image2_noisydouble(Image_noisy)/255 ??
  double??
• h1/9 1/9 1/91/9 1/9 1/9 1/9 1/9 1/9
  ???????
• Image_lowfilter2(h, Image2_noisy)
  ??????
• Image_medmedfilt2(Image_noisy,3 3)
  ?????33??
• imshow(Image)
• figure,imshow(Image_noisy)
• figure,imshow(Image_low)
• figure,imshow(Image_med)

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• Enhancement filters (?????)
• Implemented with convolution masks having
  alternating positive and negative (????)
  coefficients
• Enhance the image by sharpening (??)
• Two types considered here
• Laplacian-type (?????) filters
• Difference filters (???)

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• 1. Laplacian-type (?????) filters
• Are rotationally invariant (?????), that is they
  enhance the details (????) in all directions
  equally (???????)
• Example convolution masks of Laplacian-type
  filters are

Filter 1
Filter 2
Filter 3
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Laplacian filter
Contrast enhanced (????) Version of Laplacian
filtered image
Laplacian filtered image
Original image
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• 2. Difference (??) filters
• Also called as emboss (??) filters
• Enhances the details in the direction specific
  (????) to the mask selected
• Four primary difference filter convolution masks,
  corresponding to the edges (?) in the vertical
  (??), horizontal (??), and two diagonal
  directions (????) are

Diagonal 2
Horizontal
Diagonal 1
Vertical
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Difference filter
Original image
Difference filtered image
Difference filtered image added to the original
image, with contrast enhanced (????)
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Ex3_3.m (????????)

• close all clear
• Aimread('cat.bmp')
• m,nsize(A) ????size?mxn
• imshow(A)
• CM_lapa0 -1 0 -1 5 -1 0 -1 0 Laplacian????
  
• CM_diff1 0 0 0 1 0 0 0 -1 difference ????
  
• Bfilter2(CM_lapa,A) ??Laplacian ????
• Cfilter2(CM_diff,A) ??difference ????
• figure imshow(B/256)
• figure imshow(C/256)

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• Binary Image Analysis (??????)
• Binary images are useful in many computer vision
  applications which require simple object shape
  such as positioning a robot to grasp (??) an
  object, to check a manufactured object for
  defects (??), FAX, OCR(??????)

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• Binary Image Analysis (??????)
• Most cameras provide us color or gray level
  images, thus we need to convert those images into
  binary images
• Next, we extract (??) simple binary features and
  use them to classify (??) binary objects

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• Thresholding via Histogram (?????????)
• Thresholding is required to create a binary image
  (????) from a gray level image (????)
• This is done by specifying a threshold value
  (??????) which will set all values above the
  specified gray level to 1 and everything below
  the specified value to 0
• Typically 255 is used for 1 and 0 is used for
  the 0 value

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• In many applications the threshold value is
  determined experimentally (?????) and is highly
  dependent on lighting conditions and object to
  background contrast (?????,???????????????)
• It is much easier to find a good threshold value
  with proper lighting, and good background to
  object contrast

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Figure 3.3-1 Effects of Lighting and Object to
Background Contrast on Thresholding
b) Result of thresholding (???) image (a)
a) An image of a bowl with high object to
background contrast (??????? ????) and good
lighting (????)
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Figure 3.3-1 Effects of Lighting and Object to
Background Contrast on Thresholding (contd)
d) Result of thresholding image (c)
c) An image of a bowl with poor object to
background contrast (??????? ????) and poor
lighting(????)
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• The histogram (???) is a plot of gray level
  versus the number of pixels (??????????) in the
  image at each gray level
• Histogram of an image is examined (??) to select
  the proper (???) threshold value
• The peaks (??) and valleys (??) in the histogram
  are examined and a threshold is experimentally
  selected (??????) that will best separate (?????)
  the object from the background (??)

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Figure 3.3-2 Histograms (???)
Threshold ?
b) The histogram of image (a), showing the
threshold that separates object and
background (????????? ??????)
a) An image of a bowl with high object to
background contrast and good lighting
Image after threshold (????????)
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Figure 3.3-2 Histograms (contd)
Threshold ?
d) The histogram of image (c), showing what
appears to be a good threshold, but it does
not successfully separate object and
background
c) An image of a bowl with poor object to
background contrast and poor lighting
Image after threshold
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Ex3_5.m (??????)

• Aimread('car_number.bmp') imshow(A)
  XA(,,3)
• m,nsize(X)
• figure, imshow(X)
• figure, imhist(X) ?????????
• th70 ?????
• Czeros(m,n)
• for i1m
• for j1n
• if X(i,j)gtth
• C(i,j)1
• end
• end
• end
• figure,imshow(C)

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• Histogram equalization (?????)
• A technique where the histogram of the resultant
  image is as flat as possible (???????????)
• The theoretical basis for histogram equalization
  involves probability theory, where we treat the
  histogram as the probability distribution of the
  gray levels (?????????????)
• Its function is similar to that of a histogram
  stretch but often provides more visually pleasing
  results across a wider range of images
  (??????????????????????)

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• Consists of 4 steps
• 1. Find the running sum of the histogram
• values (??????????)
• 2. Normalize the values from step (1) by
• dividing by the total number of pixels
  (?(1)? ??????????)
• 3. Multiply the values from step (2) by the
• maximum gray level value and round
  (?(2)? ?????????????????)
• 4. Map the gray level values to the results
• from step (3) using a one-to-one
• correspondence (???????????? ??????(3)?????)

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• Example
• 3-bits per pixel image range is 0 to 7.
• Given the following histogram
• Number of Pixels
• Gray Level Value (Histogram values)
• 0 10
• 1 8
• 2 9
• 3 2
• 4 14
• 5 1
• 6 5
• 7 2

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• 1) Create a running sum of the histogram values.
  (??????????) This means the first value is 10,
  the second is 10818, next 108927, and so on.
  Here we get 10, 18, 27, 29, 43, 44, 49, 51
• 2) Normalize by dividing by the total number of
  pixels. (?(1)? ??????????)
• The total number of pixels is 1089214150
  51 (note this is the last number from step 1),
  so we get 10/51, 18/51, 27/51, 29/51, 43/51,
  44/51, 49/51, 51/51
• 3) Multiply these values by the maximum gray
  level values, in this case 7, and then round the
  result to the closest integer (?(2)???????????????
  ???). After this is done we obtain 1, 2, 4, 4,
  6, 6, 7, 7

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• 4) Map the original values to the results from
  step 3 by a one-to-one correspondence
  (??????????????????(3)?????). This is done as
  follows
• 
  
• Original Gray Histogram
• Level Value Equalized Values
• 0 1
• 1 2
• 2 4
• 3 4
• 4 6
• 5 6
• 6 7
• 7 7

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• All pixels in the original image with gray level
  0 are set to 1, values of 1 are set to 2, 2 set
  to 4, 3 set to 4, and so on. After the histogram
  equalization values are calculated and can be
  implemented efficiently with a look-up-table
  (LUT)(?????????,???????), as discussed in Chapter
  2
• We can see the original histogram and the
  resulting histogram equalized histogram in Fig.
  8.2.14. Although the result is not flat, it is
  closer to being flat than the original
  histogram(????????,?????????????)

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Histogram Equalization Examples (???????)
1.
Input image
Resultant image after histogram equalization
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Histogram Equalization Examples (contd)
2.
Input image
Resultant image after histogram equalization
Note As can be seen histogram equalization
provides similar results regardless
of the input image (??????????????,
????????????)
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• Histogram equalization of a digital image will
  not typically provide a histogram that is
  perfectly flat, but it will make it as flat as
  possible (???????????????,?????????????)
• Histogram equalization may not always provide the
  desired effect, since its goal is fixed to
  distribute the gray level values as evenly as
  possible. (????????????)To allow for interactive
  histogram manipulation, the ability to specify
  the histogram is necessary

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Ex8_3.m (??????)

• ??????
• image_1 ???image_2 ???????????
• image_1imread('L5_1.bmp') ????
• image_2histeq(image_1)????????????
• imshow(image_1) ?????
• figure,imshow(image_2) ????????
• figure,imhist(image_1) ?????????
• figure,imhist (image_2) ???????????

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• Morphological Filtering (????)
• Morphology(???) relates to form and structure of
  objects (????????)
• Morphological filtering simplifies a segmented
  image to facilitate the search for objects of
  interest(???????????,???????????)
• This is done by smoothing out object outlines
  (?????????), filling small holes (?????),
  eliminating small projections (?????), and with
  other similar techniques

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• The two principal morphological operations(?????)
  are dilation (??)and erosion (??)
• Dilation allows objects to expand (??), thus
  potentially filling in small holes (?????), and
  connecting disjoint objects (????????)
• Erosion shrinks (??) objects by etching away
  (eroding) (??) their boundaries (??)

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• These operations can be customized for an
  application by the proper selection of the
  structuring element (?????????), which determines
  exactly how the objects will be dilated or eroded
• Basically, the structuring element is used to
  probe (??) the image to find how it will fit, or
  not fit, into the image object(s)

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• Dilation(??) It is performed by laying the
  structuring element on the image and sliding it
  across the image in a manner similar to
  convolution (???????????????????)
• If the origin of the structuring element
  coincides with a 0 in the image, there is no
  change move to the next pixel (???????????????0
  ,????,??????)
• If the origin of the structuring element
  coincides with a 1 in the image, perform the OR
  logic operation on all pixels within the
  structuring element (???????????????1
  ,?????????????OR????)

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• With a dilation operation, all the 1 pixels in
  the original image will be retained
• (??), any boundaries will be expanded (??????),
  and small holes will be
• filled (??????)

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• Note In 1st printing
• of book, the structuring
• element is incorrect

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• Erosion (??) It is similar to dilation, but we
  turn pixels to '0', not '1'
• If the origin of the structuring element
  coincides with a '0' in the image, there is no
  change move to the next pixel (???????????????0
  ,????,??????)
• If the origin of the structuring element
  coincides with a 1 in the image, and any of the
  1 pixels in the structuring element extend
  beyond the object (1 pixels) in the image, then
  change the 1 pixel in the image, whose location
  corresponds to the origin of the structuring
  element, to a 0(???????????????1,???????1
  ???????1 ,?????????????????0).

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• With an erosion operation, the only remaining
  pixels are those that coincide to
• the origin of the structuring element where
  it is all contained in the object

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Note In first printing of book, the 4th row,
4th col 0, should be a 1, in the IMAGE
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• Opening (??) It consists of an erosion followed
  by a dilation (??????)
• It can be used to eliminate all pixels in regions
  that are too small to contain the structuring
  element (??????????????????????)
• In this case the structuring element is often
  called a probe, as it is probing the image
  looking for small objects to filter out of the
  image

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• The output image tends to take a shape similar
  to the structuring element itself

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• Closing (??) It consists of a dilation followed
  by erosion(??????)
• It can be used to fill in holes and small gaps
  (??????)
• It will connect small, adjacent objects
  (?????????)
• Closing tends to close up or fill in objects

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• Note that holes and gaps are filled, but, unlike
  dilation, more of the original
• boundary is retained

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• Closing and opening will have different
  results(?????????)even though both consist of an
  erosion and a dilation
• Therefore, order of operation is important
  (????????) for morphological operations
• Different structuring elements will also provide
  different results (???????????????). As noted
  before, objects in the output image will tend to
  take the shape of the structuring element
  (????????????????????)

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Figure 4.3-18 Binary Dilation with Various Shape
Structuring Elements
a) Original image, a microscope cell image that
has undergone a threshold operation
(original image courtesy of Sara Sawyer, SIUE)
b) Dilation with a circular (???)structuring
element
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Figure 4.3-18 Binary Dilation with Various Shape
Structuring Elements (contd)
d) Dilation with a cross shape (???)structuring
element
c) Dilation with a square (??) structuring
element
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Figure 4.3-19 Dilation with Different Size
Structuring Elements
b) Dilation with a circular structuring
element of size 3
a) Original image
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Figure 4.3-19 Dilation with Different Size
Structuring Elements (contd)
d) Dilation with a circular structuring
element of size 11
c) Dilation with a circular structuring
element of size 7
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Figure 4.3-20 Binary Erosion with Various Shape
Structuring Elements
b) Erosion with a circular structuring element
a) Original image, a microscope cell image
that has undergone a threshold operation
(original image courtesy of Sara Sawyer,
SIUE),
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Figure 4.3-20 Binary Erosion with Various Shape
Structuring Elements (contd)
d) Erosion with a cross shape structuring
element
c) Erosion with a square structuring element
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Figure 4.3-21 Binary Opening with Various
Shape Structuring Elements
b) Opening with a circular structuring element
a) Original image, a microscope cell image
that has undergone a threshold operation
(original image courtesy of Sara Sawyer,
SIUE),
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Figure 4.3-21 Binary Opening with Various
Shape Structuring Elements (contd)
d) Opening with a cross shape structuring
element
c) Opening with a square structuring element
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Figure 4.3-22 Binary Closing with Various Shape
Structuring Elements
b) Closing with a circular structuring element
a) Original image, a microscope cell image
that has undergone a threshold operation
(original image courtesy of Sara Sawyer,
SIUE),
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Figure 4.3-22 Binary Closing with Various Shape
Structuring Elements (contd)
d) Closing with a cross shape structuring
element
c) Closing with a square structuring element
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Figure 4.3-23 Opening and Closing with Different
Size Structuring Elements
a) Original microscopic cell image (courtesy
of Sara Sawyer, SIUE)
b) Image after undergoing a threshold
operation
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Figure 4.3-23 Opening and Closing with Different
Size Structuring Elements (contd)
d) Closing with a circular structuring
element of size 5
c) Opening with a circular structuring
element of size 5