Chapter 4: Image Segmentation · 7 Point Detection Point (isolated) detection can be achieved...
Transcript of Chapter 4: Image Segmentation · 7 Point Detection Point (isolated) detection can be achieved...
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Faculty Orientation Workshop on BE (E&TC/Elex) Revised Syllabus 2015 Course
Under the aegis of BoS-E&TC, SPPU, Pune
Unit 4: Image Segmentation
Digital Image And Video Processing (Elective-I)
Dr. Ashwini M. Deshpande
Cummins College of Engineering for Women
Dept. of E&Tc Engg.
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Topics to be covered Topic to be referred
from
Pixel classification, Bi-level thresholding, Multi-level
thresholding, Adaptive thresholding, Otsu’s method
Sec. 10.3
Edge detection-
First order derivative- Prewitt and Sobel,
Second order derivative- LoG, DoG,
Canny
Sec. 10.1
Edge linking, Hough Transform Sec. 10.2
Region growing, Region splitting and merging Sec. 10.4
Morphological operators- Dilation, Erosion, Opening,
Closing, Hit or Miss transform, Boundary extraction
Sec. 9.2, 9.3, 9.4, 9.5.1
Thinning, Thickening, Skeletons Sec. 9.5.5, 9.5.6, 9.5.7
T1. Gonzalez and Woods, "Digital Image Processing", Pearson Education,
(3rd Edition)
Contents (8 Hrs)
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Introduction
◼ Segmentation is to to partition an image intoits constituent regions or objects that stronglycorrelate
◼ Important step in automatic pictorial patternrecognition and scene analysis
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Segmentation Examples
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Principal approaches
◼ Segmentation algorithms generally are based onone of 2 basic properties of intensity values-◼ Discontinuity : to partition an image based on sharp
changes in intensity
◼ Similarity : to partition an image into regions that are similar according to a set of predefined criteria
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Detection of Discontinuities
◼ To detect the three basic types of gray-level discontinuities◼ points, lines, edges
◼ The common way is to run a mask through the image
=
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992211 ...i
ii zwzwzwzwR
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Point Detection
◼ Point (isolated) detection can be achieved simply using the mask above
◼ A point can be detected at the location on which the mask is centered. If, |R| T
◼ where ◼ T is a nonnegative threshold ◼ R is the sum of products of the coefficients with the gray
levels contained in the region encompassed by the mark.
❑ Points are detected at those pixels in the subsequent filtered image that are above a set threshold
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Point Detection
X-ray image of
a turbine blade
Result of point
detection
Result of
thresholding
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Line Detection
◼ Horizontal mask will result with max response when a line passed through the middle row of the mask with a constant background. Similar idea is used with other masks.
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Line Detection
◼ Apply every mask on the image◼ Let R1, R2, R3, R4 denotes the response of
the horizontal, +45 degree, vertical and-45 degree masks, respectively.
◼ If, at a certain point in the image|Ri| > |Rj|,
◼ For all ji, that point is said to be morelikely associated with a line in thedirection of mask i.
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Example
Binary image of a
wire bond mask
After
processing
with -45°line
detector
Result of
thresholding
filtering result
• Figures from Gonzalez
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Edge Detection
◼ An edge is a set of connected pixelsthat lie on the boundary between tworegions◼ first-order derivative (Gradient
operator)
◼ second-order derivative (Laplacianoperator)
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Characterizing edges
◼ An edge is a place of rapid change inthe image intensity function
imageintensity function
(along horizontal scanline) first derivative
edges correspond to
extrema of derivative
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Ideal and Ramp Edges
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Derivatives of 1-D Digital Functions
15© 1992-2008 R.C. Gonzalez & R.E. Woods
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First and Second derivatives
the signs of the derivatives would be reversed for an edge that transitions from light to dark
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Second derivatives
◼ Produces 2 values for every edge in animage (an undesirable feature)
◼ An imaginary straight line joining theextreme positive and negative values ofthe second derivative would cross zeronear the midpoint of the edge. (zero-crossing property)
◼ Useful for locating the centers of thickedges
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Gradient Operator
◼ first derivatives are implemented using the magnitude of the gradient.
=
=
y
fx
f
G
G
y
xf
21
22
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+
=
+==
y
f
x
f
GGmagf yx
yx GGf +
commonly approx.
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◼ The direction of this gradient:
**correction (Gy/Gx)
= −
y
x
G
Gyx 1tan),(
Gradient Operator
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Gradient and Edge
20© 1992-2008 R.C. Gonzalez & R.E. Woods
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Roberts cross-gradient operators
Prewitt operators
Sobel operators
Gradient Masks
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Diagonal edges with Prewitt and Sobel masks
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Example
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Example
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Example
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Second Order Derivative Methods - Laplacian
2
2
2
22 ),(),(
y
yxf
x
yxff
+
=(linear operator)
Laplacian operator
)],(4)1,()1,(
),1(),1([2
yxfyxfyxf
yxfyxff
−−+++
−++=
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Laplacian of Gaussian (LoG)
◼ Laplacian combined with smoothing to find edges via zero-crossing.
2
2
2)(
r
erh−
−=
where r2 = x2+y2, and is the standard deviation
2
2
24
222 )(
r
er
rh−
−−=
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Mexican hat
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Example
a). Original imageb). Sobel Gradientc). Spatial Gaussian smoothing functiond). Laplacian maske). LoGf). Threshold LoGg). Zero crossing
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Second Order Derivative Methods -Difference of Gaussian - DoG
◼ LoG requires large computation time for a large edge detector mask
◼ To reduce computational requirements, approximate the LoG by the difference of two LoG – the DoG
2
2
)2
22(
2
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)2
22(
22),(
22
21
yxyx
eeyxDoG
+−
+−
−=
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Canny Edge Detector
Original image
Smoothing by Gaussian convolution
Differential operators along x and y axis
Non-maximum suppressionfinds peaks in the image gradient
Hysteresis thresholding locates edge strings
Edge map
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Canny Edge Detector Example
original image vertical edges horizontal edges
norm of the gradient after thresholding after thinning
MATLAB: edge(image, ‘canny’)
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➢Approaches:-Thresholding
- Region growing technique
- Region splitting and merging technique
Similarity based image Segmentation
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Thresholding
◼Thresholding is usually the first step in any segmentation approach
◼We have talked about simple single value thresholding already
◼Single value thresholding can be given mathematically as follows:
=
Tyxfif
Tyxfifyxg
),( 0
),( 1),(
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Thresholdingimage with dark background and a light object
image with dark background and two light objects
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Problems With Single Value Thresholding
◼Single value thresholding only works forbimodal histograms
◼Images with other kinds of histogramsneed more than a single threshold
Multi-level thresholding
Bi-level thresholding
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Multi-level thresholding◼ A point (x,y) belongs-
◼ to an object class if T1 < f(x,y) T2
◼ to another object class if f(x,y) > T2
◼ to background if f(x,y) T1
◼ T depends on ◼ only f(x,y) : only on gray-level values Global
threshold
◼ both f(x,y) and p(x,y) : on gray-level values and its neighbors Local threshold
◼ f(x,y), p(x,y) and (x,y) : on gray-level values, its neighbors and pixel location Adaptive threshold
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Basic Global Thresholding
◼Based on the histogram of an image
◼Partition the image histogram using a single global threshold
◼The success of this technique very strongly depends on how well the histogram can be partitioned
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Basic Global Thresholding: Example
generate binary image
use T midway between the max and min gray levels-
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Basic Global Thresholding Algorithm
◼ Based on visual inspection of histogram1. Select an initial estimate for T.2. Segment the image using T. This will produce two
groups of pixels: G1 consisting of all pixels with gray level values > T and G2 consisting of pixels with gray level values T
3. Compute the average gray level values 1 and 2 for the pixels in regions G1 and G2
4. Compute a new threshold value5. T = 0.5 (1 + 2)6. Repeat steps 2 through 4 until the difference in T in
successive iterations is smaller than a predefined parameter To.
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Basic Adaptive Thresholding
◼ Subdivide original image into small areas.
◼ Utilize a different threshold to segmenteach subimages.
◼ Since the threshold used for each pixeldepends on the location of the pixel interms of the subimages, this type ofthresholding is adaptive.
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The Role of Illumination
f(x,y) = i(x,y) r(x,y)
a) computer generated reflectance functionb) histogram of reflectance functionc) computer generated illumination function (poor)d) product of a) and c)e) histogram of product image
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Example : Adaptive Thresholding
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Further subdivisiona). Properly and improperly segmented subimages from previous example b)-c). corresponding histogramsd). further subdivision of the improperly segmented subimage.e). histogram of small subimage at topf). result of adaptively segmenting d).
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Automatic thresholding method
◼ Otsu’s method
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Optimal global thresholding◼ This method treats pixel values as probability density
functions. ◼ The goal of this method is to minimize the probability
of misclassifying pixels as either object or background. ◼ There are two kinds of error:
◼ mislabeling an object pixel as background, and◼ mislabeling a background pixel as object.
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Otsu’s Thresholding Method
◼ Based on a very simple idea: Find the threshold thatminimizes the weighted within-class variance.
◼ This turns out to be the same as maximizing thebetween-class variance.
◼ Operates directly on the gray level histogram [e.g.256 numbers, P(i)], so it’s fast (once the histogram iscomputed).
◼ Basic idea is that well-thresholded classes should bedistinct w.r.t. intensity values and conversely athreshold giving the best separation between classeswould be the best (optimum) threshold.
(1979)
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Matlab function for Otsu’s method
◼ function level = graythresh(I)◼ GRAYTHRESH Compute global thresholdusing Otsu's method. Level is a normalized intensity value that lies in the range [0, 1].
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◼ Edges and thresholds sometimes do not give good results for segmentation.
◼ Region-based segmentation is based on the connectivity of similar pixels in a region.◼ Each region must be uniform.
◼ Connectivity of the pixels within the region is very important.
◼ There are two main approaches to region-based segmentation: region growing and region splitting.
Region based segmentation
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Region Growing
◼ Region-growing approaches exploit the fact that pixels which are close together have similar gray values.
◼ Algorithm-
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◼ Let R represent the entire image region.
◼ Segmentation is a process that partitions R into subregions, R1,R2,…,Rn, such that,
RRi
n
i=
=1 (a)
jijiRR ji = , and allfor (c)
niRi ,...,2,1 region, connected a is (b) =
niRP i ,...,2,1for TRUE)( (d) ==
jiji RRRRP and regionsadjacent any for FALSE)( (e) =
Region Growing- Properties
• Drawbacks: large execution time, selection of homogeneity property, selection of seed.
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Region Growing
criteria:1. The absolute gray-
level difference between any pixel and the seed has to be less than 65
2. The pixel has to be 8-connected to at least one pixel in that region (if more, the regions are merged)
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Region Splitting◼ The opposite approach to region growing is
region splitting.◼ It is a top-down approach and it starts with
the assumption that the entire image ishomogeneous
◼ If not, then the method requires that theregion be split into two regions.
◼ If this is not true, the image is split into foursub images
◼ This splitting procedure is repeatedrecursively until we split the image intohomogeneous (uniform) regions
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Region Splitting◼ One method to divide a region is to use a quadtree
structure.
◼ Quadtree: a tree in which nodes have exactly fourdescendants.
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Split
◼ Splitting techniques disadvantage- they create regions that may be adjacent and homogeneous, but not merged.
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◼ Opposite to region split-bottom-up method
- Remove child nodes if parent node satisfy the desired property.
- Image size must be in integer powers of 2.
- If most of the homogeneous regions are small, then split technique is inferior to merge technique in terms of time requirement and vice versa.
- Split and Merge: If no priori knowledge is available start somewhere at the middle level.
Region Merging
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Region Merging
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Region Splitting and Merging
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Applications
◼ 3D – Imaging
◼ Several applications in the field of MRI
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Edge linking and Boundary detection
◼ Local processing
◼ Global processing
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◼ Two properties of edge points are useful for edgelinking:◼ the strength (or magnitude) of the detected edge points
◼ their directions (determined from gradient directions)
◼ Adjacent edge points with similar magnitude anddirection are linked.
◼ For example, an edge pixel with coordinates (x0,y0) in apredefined neighborhood of (x,y) is similar to the pixelat (x,y) if,
Local processing
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Local processing example
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Global Processing
◼ Hough Transform can be used todetermine whether points lie on a curveof a specified shape
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Hough Transform – Line Detection
◼ Consider the slope-intercept equationof line:
◼ Rewrite the equation as follows:
(a, b are constants, x is a variable, y is a function of x)
(now, x, y are constants, a is a variable, b is a function of a)
P.V.C. Hough, Machine Analysis of Bubble Chamber Pictures, Proc. Int. Conf. High Energy Accelerators and Instrumentation, 1959
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◼ Hough transform: a way of finding edge points in an image that lie along a straight line.
◼ Example: xy-plane v.s. ab-plane (parameter space)baxy ii +=
Hough transform
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Properties in slope-intercept space
◼ Each point (xi , yi) defines a line in thea − b space (parameter space).
◼ Points lying on the same line in the x − yspace, define lines in the parameterspace which all intersect at the samepoint.
◼ The coordinates of the point ofintersection define the parameters ofthe line in the x − y space.
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HT Algorithm
1. Quantize parameter space (a,b):
P[amin, . . . , amax][bmin, . . . , bmax] (accumulator array)
amax
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Algorithm (cont’d)
2. For each edge point (x, y)
For(a = amin; a ≤ amax; a++) {b = − xa + y; /* round off if needed *
(P[a][b])++; /* voting */}
3. Find local maxima in P[a][b]
if P[a j][bk]=M, then M points lie on the liney = a j x + bk
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Polar Representation of Lines
(no problem with vertical lines, i.e., θ =90)
ρ−θ space
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Example
long
vertical
lines
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Using the Hough transform for edge linking
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Morphology
◼ Morphological image processing is used toextract image components for representationand description of region shape, such asboundaries, skeletons, convex hull etc.
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Z2 and Z3
◼ Set in mathematical morphologyrepresent objects in an image◼ binary image (0 = white, 1 = black) : the
element of the set is the coordinates (x,y)of pixel belong to the object Z2
◼ gray-scaled image : the element of the set is thecoordinates (x,y) of pixel belong to the object andthe gray levels Z3
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Basic Set Theory
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Reflection and Translation
} ,|{ˆ Bfor bbwwB −=
} ,|{)( Afor azaccA z +=
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Example
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Structuring element (SE)
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▪ small set to probe the image under study▪ for each SE, define origin▪ shape and size must be adapted to geometricproperties for the objects
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How to describe SE
◼ many different ways!
◼ information needed:◼ position of origin for SE
◼ positions of elements belonging to SE
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Examples of Structuring element (SE)
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Basic morphological operations
◼ Erosion
◼ Dilation
◼ combine to◼ Opening object
◼ Closing background
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keep general shape but smooth with respect to
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Erosion
◼ Erosion of a set A by structuring element B: all z in A such that B is in A when origin of B=z
shrink the object
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}{ Az|(B)BA z =−The set of all points such that , translated by , is contained by .z B z A
| ( ) c
ZA B z B A= =
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}{ Az|(B)BA z =−
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Dilation
◼ Dilation of a set A by structuring element B: all z in A such that B hits A when origin of B=z
◼ grow the object
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}ˆ{ ΦA)Bz|(BA z =
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Dilation
}ˆ{ ΦA)Bz|(BA z =
B = structuring element
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Dilation : Bridging gaps
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Opening and Closing
◼ Opening generally smoothes the contourof an object and eliminates thinprotrusions
◼ Closing tends to smooth sections ofcontours but it generates fuses narrowbreaks and long thin gulfs, eliminatessmall holes, and fills gaps in the contour
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Opening
Erosion followed by dilation, denoted ∘
◼ eliminates protrusions
◼ breaks necks
◼ smooths contour
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BBABA −= )(
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Opening
BBABA −= )(})(|){( ABBBA zz =
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Closing
Dilation followed by erosion, denoted •
◼ smooth contour
◼ fuse narrow breaks and long thin gulfs
◼ eliminate small holes
◼ fill gaps in the contour
90
BBABA −=• )(
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Closing
BBABA −=• )(
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Properties
Opening(i) AB is a subset (subimage) of A(ii) If C is a subset of D, then C B is a subset of D B(iii) (A B) B = A B
Closing(i) A is a subset (subimage) of A•B(ii) If C is a subset of D, then C •B is a subset of D •B(iii) (A •B) •B = A •B
Note: repeated openings/closings has no effect!
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Hit-or-Miss Transformation ⊛ (HMT)
◼ find location of one shape among a set of shapes template matching
◼ composite SE: object part (B1) and background part (B2)
◼ does B1 fits the object while, simultaneously, B2 misses the object, i.e., fits the background?
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Hit-or-Miss Transformation
)]([)( XWAXABA c −−−=
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Boundary Extraction
)()( BAAA −−=
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Example
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Thinning
cBAA
BAABA
)(
)(
=
−=
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Thickening
)( BAABA =•
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SkeletonsK
kk ASAS
0
)()(=
=
BkBAkBAASk )()()( −−−=
})(|max{ −= kBAkK
))((0
kBASA k
K
k=
=
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Thank you !