DIGITAL IMAGE PROCESSING - scut.edu.cn · 2019-04-18 · 4/18/2019 DIGITAL IMAGE PROCESSING 39 ....
Transcript of DIGITAL IMAGE PROCESSING - scut.edu.cn · 2019-04-18 · 4/18/2019 DIGITAL IMAGE PROCESSING 39 ....
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Xiangyu Yu
School of Electronic and Information Engineering,
South China University of Technology, P. R. China
DIGITAL IMAGE PROCESSING
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LECTURE 10 REPRESENTATION & DESCRIPTION
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REPRESENTATION & DESCRIPTION
After segmentation, we get pixels along the boundary or pixels contained in a region.
representing a segmented in 2 ways
in terms of its external characteristics (its boundary) focus on shape characteristics
in terms of its internal characteristics (pixels comprising the region) focus on regional properties, e.g., color, texture
sometimes, we may need to use both ways
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OVERVIEW
Description describes the region based on the chosen representation
e.g. representation boundary
description length of the boundary, orientation of the straight line joining its extreme points, and the number of concavities in the boundary.
Descriptors should be insensitive to changes in size, translation, rotation, …
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SENSITIVITY
feature selected as descriptors should be as insensitive as possible to variations in size
translation
rotation
following descriptors satisfy one or more of these properties.
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Image regions (including segments) can be represented by either the border or the pixels of the region. These can be viewed as external or internal characteristics, respectively.
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REPRESENTATION
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REPRESENTATION
The segmentation techniques yield raw data in the form of pixels along a boundary or pixels contained in a region
these data sometimes are used directly to obtain descriptors
It is standard practice to use schemes that compact the segmented data into representations that facilitate the computation of descriptors.
standard uses techniques to compute more useful data (descriptors) from the raw data in order to decrease the size of data.
G3E-P.818
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REPRESENTATION SCHEMES
Segmentation Raw data (pixels)
Representations
Computation of descriptors
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Boundary Following: We assume (1) that we are working with binary images in which object and background points are labeled 1 and 0, respectively
(2) the images are padded with a border of 0s to eliminate the possibility of an object merging with the image border.
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Given a binary region R or its boundary, an algorithm for following the border of R, or the given boundary, consists of the following steps:
1. Let the starting point, b0, be the uppermost, leftmost point in the image that is labeled 1. Denote by c0 the west neighbor of b0. Clearley, c0 always is a background point. Examine the 8-neighbors of b0, starting at c0 and proceeding in clockwise direction. Let b1 denote the first neighbor encountered whose value is 1, and let c1 be the (background) point immediately preceding b1 in the sequence. Store the location of b0 and b1 for use in Step 5.
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2. Let b=b1 and c=c1
3. Let the 8-neighbors of b, starting at c and proceeding in a clockwise direction, be denoted by n1,n2,...,n8. Find the first nk labeled 1.
4. Let b=nk and c=nk-1
5. Repeat Step 3 and 4 until b=b0 and the boundary point found is b1. The sequence of b points found when the algorithm stops constitutes the set of ordered boundary points.
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The boundary-following algorithm works equally well if a region, rather than its boundary, is given in Fig.11.2
That is, procedure extracts the outer boundary of a binary region. If the objective is to find the boundaries of holes in a region(these are called the inner boundaries of the region), a simple approach is to extract the holes, a simple approach is to extract and treat them as 1-valued region on a background of 0s.
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REPRESENTATION SCHEMES
Common external representation methods are: Chain code
Polygonal approximation
Signature
Boundary segments
Skeleton (medial axis)
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CHAIN CODES
Represent a boundary by a connected sequence of straight-line segments of specified length and direction.
Directions are coded using the numbering scheme
Based on 4- or 8-connectivity
Direction of each segment coded by a numbering scheme
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CHAIN CODES
Problems: the resulting chain of codes tends to be quite long
any small disturbances along the boundary due to noise or imperfect segmentation cause changes in the code that may not be related to the shape of the boundary(Easily disturbed by noise, and sidetracked)
Solution: Resampling using larger grid spacing
Normalizations
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Figure Connectivity in chain codes.
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REPRESENTATION CHAIN CODE
4-direction
8-direction
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CHAIN CODES
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RESAMPLING FOR CHAIN CODES
0033…01
0766…12
Spacing of the sampling
grid affect the accuracy of
the representation
(4-directional)
(8-directional)
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Figure Chain codes by different connectivity.
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Figure Start point invariance in chain codes.
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The chain code of boundary depends on the starting point. However, the code can be normalized with respect to starting point by a straightforward procedure.
We simply treat the chain code as a circular sequence of direction numbers and redefine the starting point so that the resulting sequence of numbers forms an integer of minimum magnitude.
We can normalize also for rotation (in angles that are integer multiples of the directions in Fig. 11.3) by using the first difference of the chain code instead of the code itself.
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NORMALIZATION FOR CHAIN CODES
With respect to starting point:
Make the chain code a circular sequence
Redefine the starting point which gives an integer of minimum magnitude
E.g. 101003333222 normalized to 003333222101
For rotation:
Using first difference (FD) obtained by counting the number of direction changes in counterclockwise direction
E.g FD of a 4-direction chain code 10103322 is 3133030
For scaling:
Altering the size of the resampling grid.
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Chain codes can be based on either 4-connectedness or 8-connectedness.
The first difference of the chain code: This difference is obtained by counting the number of direction changes (in a counterclockwise direction) For example, the first difference of the 4-direction chain code 10103322 is 3133030.
Assuming the first difference code represent a closed path, rotation normalization can be achieved by circularly shifting the number of the code so that the list of numbers forms the smallest possible integer.
Size normalization can be achieved by adjusting the size of the resampling grid.
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CHAIN CODES
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DIFFERENTIAL CHAIN CODE
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Chain code representations for a feature outline starting at the blue pixel
and proceeding clockwise, showing the conventional direction codes and
below them the differential chain code R6E-P.613
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Normalization for rotation – first difference
Counting (counterclockwise) the number of direction changes that separate two adjacent element of the code
Normalization for starting position – shape number
The first difference of smallest magnitude
Normalization for size
Multi-scaling resampling
CHAIN CODES NORMALIZATION
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G3E-P.822 4/18/2019 DIGITAL IMAGE PROCESSING 29 G3C-P.517
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POLYGONAL APPROXIMATIONS
a digital boundary can be approximated with arbitrary accuracy by a polygon
To capture the essence of the boundary shape with the fewest possible polygonal segments.
not trivial and time consuming
Various methods: Minimum perimeter polygons
Merging techniques (least square error line fit)
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Polygonal approximations: to represent a boundary by straight line segments, and a closed path becomes a polygon.
The number of straight line segments used determines the accuracy of the approximation.
Only the minimum required number of sides necessary to preserve the needed shape information should be used (Minimum perimeter polygons).
A larger number of sides will only add noise to the model.
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POLYGONAL APPROXIMATIONS
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MINIMUM PERIMETER POLYGONS
Two techniques: merging & splitting
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Minimum perimeter polygons: (Merging and splitting)
Merging and splitting are often used together to ensure that vertices appear where they would naturally in the boundary.
A least squares criterion to a straight line is used to stop the processing.
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POLYGONAL APPROXIMATIONS
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REPRESENTATION POLYNOMIAL APPROXIMATIONS
Merging Techniques Splitting Techniques
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1S
2S
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MERGING TECHNIQUE
Repeat the following steps:
Merging unprocessed points along the boundary until the least-square error line fit exceed the threshold.
Store the parameters of the line
Reset the LS error to 0
Intersections of adjacent line segments form vertices of the polygon.
Problem: vertices of the polygon do not always correspond to inflections (e.g. corners) in the original boundary.
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G3E-P.829
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POLYGONAL APPROXIMATIONS
Merging technique problem: corners
Solution: Splitting: to subdivide a segment successively into two parts until a given criterion is satisfied.
Objective: seeking prominent inflection points.
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SPLITTING TECHNIQUE
Subdivide a segment successively into two parts until a specified criterion is met.
For example
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G3E-P.830
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SPLITTING TECHNIQUES
1. find the major axis 2. find minor axes which perpendicular to major
axis and has distance greater than a threshold 3. repeat until we can’t split anymore
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G3E-P.830
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SIGNATURES
A 1-D functional representation of a boundary
Basic idea : reduce the boundary representation to a 1-D function, which might be easier to describe than a 2-D boundary
One simple approach : Plot the distance from the centroid to the boundary as a function of angle. It is invariant to translation, but not to rotation and scaling.
Rotation : select the farthest point from the centroid as the starting point
Scaling : normalize the function by variance
G3E-P.830-831
The idea behind a signature is to convert a two
dimensional boundary into a representative one
dimensional function.
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G3E-P.831
map 2D function to 1D function 4/18/2019 DIGITAL IMAGE PROCESSING 40
SIGNATURES
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REPRESENTATION SIGNATURE
Distance signature of circle shapes
Distance signature of rectangular shapes
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θr
Ar(θ)
4
2
3
4
5
4
3
2
7
4
2
θ
A
r(θ)
4
2
3
4
5
4
3
2
7
4
2
θ
2A
A
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SIGNATURES
Invariable to translation, but depends on rotation & scaling
normalization of the procedure is necessary
e.g. select the same starting point
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For the signature just described Invariant to translation (everything w.r.t. the centroid)
Depending on rotation and scaling
How to remove the dependence on rotation? Selecting point farthest from the centroid
Farthest point on the eigen axis (more robust)
Using the chain code Starting at the point farthest from the reference point or using the major axis of the region can be used to decrease
dependence on rotation.
How to remove the dependence on scaling? Scale all functions to span the range, say, [0,1] (not very robust)
Normalize by the variance Scale invariance can be achieved by either scaling the signature function to fixed amplitude or by dividing the function values
by the standard deviation of the function.
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SIGNATURES
Changes in size change the amplitude values
Scaling the functions so that they always span the same range of values, e.g. [0,1] might help.
Disadvantage: it only depends on minimum & maximum values.
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SIGNATURES
Another way to generate signatures:
Plot the angle between a line tangent to the boundary and a reference line as a function of position along the boundary
e.g. horizontal segments would correspond to straight lines as the tangent angle would be constant there.
Slope-density function = a histogram of tangent-angle values.
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BOUNDARY SEGMENTS
Boundary segments: decompose a boundary into segments.
Decomposition of a boundary into segments reduces the boundary’s complexity and simplifies the description process.
Use of the convex hull of the region enclosed by the boundary is a powerful tool for robust decomposition of the boundary.
Convex hull H of a set S is the smallest convex containing S
the set different H-S is called convex deficiency D of the set S
Scheme: independent of size and orientation.
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BOUNDARY SEGMENTS
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BOUNDARY SEGMENTS
Prior to partitioning, smooth the boundary:
e.g. by replacing each pixel by the average coordinates of m of its neighbors along the boundary
or use a polygonal approximation prior to finding the convex deficiency
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THE SKELETON OF A REGION
To reduce a plane region to a graph
by e.g. obtaining the skeleton of the region via thinning.
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Use skeleton to represent a region
Skeletonizing (thinning) a region
Medial axis transformation (MAT) : for each point (p) in a region R, find its closest neighbor in the boundary (B). If it has more than one closest neighbor in B, then this point belongs to the medial axis of the region (skeleton)
Computationally expensive
a fast MAT algorithm for binary image
G3E-P.834
THE SKELETON OF A REGION
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THE SKELETON OF A REGION
Find medial axis transformation (MAT):
The MAT of region R with border B is found as:
For each point p in R, we find its closest neighbor in B.
If p has more than one, it belongs to the Medial Axis (skeleton) of R.
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Skeletons: produce a one pixel wide graph that has the same basic shape of the region, like a stick figure of a human. It can be used to analyze the geometric structure of a region which has bumps and “arms”.
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SKELETONS
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THE SKELETON OF A REGION
To improve computational efficiency, in essence we perform thinning:
Edge points of a region are iteratively deleted if:
End points are not deleted
Connectedness is not broken
No excessive erosion is caused
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G3E-P.835
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Before a thinning algorithm:
A contour point is any pixel with value 1 and having at least one 8-neighbor valued 0.
Let
98321 ...)( pppppN
29832
1
,, ,...,,
sequence ordered in the
ns transitio1-0 ofnumber the:)(
ppppp
pT
G3E-P.835
REPRESENTATION SKELETONS
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Step 1: Flag a contour point p1 for deletion if the following conditions are satisfied
6)(2 )a( 1 pN
1)( b)( 1 pT
0 )c( 642 ppp
0 )d( 864 ppp
G3E-P.835
REPRESENTATION SKELETONS
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Step 2: Flag a contour point p1 for deletion again. However, conditions (a) and (b) remain the same, but conditions (c) and (d) are changed to
0 )c'( 842 ppp
0 )'d( 862 ppp
G3E-P.835
REPRESENTATION SKELETONS
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REPRESENTATION SKELETONS
Step1:
(a)
(b)
(c)
(d)
Step2:
(c’)
(d’)
57
12 ( ) 6N p
1( ) 1T p
4 6 8 0p p p
2 4 6 0p p p
2 4 8 0p p p
2 6 8 0p p p
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G3E-P.835
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A thinning algorithm: (1) applying step 1 to flag border points for deletion
(2) deleting the flagged points
(3) applying step 2 to flag the remaining border points for deletion
(4) deleting the flagged points
This procedure is applied iteratively until no further points are deleted.
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REPRESENTATION SKELETONS
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One application of skeletonization is for character recognition.
A letter or character is determined by the center-line of its strokes, and is unrelated to the width of the stroke lines.
REPRESENTATION SKELETONS: EXAMPLE
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Representative Shape Descriptors
R6E-P.600
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DESCRIPTORS
Boundary information can be used directly, or converted into features that describe properties of a region, such as Length of the boundary
Orientation of straight line joining its extreme points
Number of concavities in the boundary
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Boundary information can be used directly, or converted into features that describe properties of a region. There are several simple geometric measures that can be useful for describing a boundary.
The length of a boundary: the number of pixels along a boundary gives a rough approximation of its length.
diameters
eccentricity
Orientation of straight line joining its extreme points
Number of concavities in the boundary
Curvature: the rate of change of slope
To measure a curvature accurately at a point in a digital boundary is difficult
The difference between the slops of adjacent boundary segments is used as a descriptor of curvature at the point of intersection of segments
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BOUNDARY DESCRIPTORS
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BOUNDARY DESCRIPTORS (PROPERTIES)
Length: approximated by the # of pixels
Diameter:
Basic rectangle: defined by
Major axis: the line giving the diameter
Minor axis: the line perpendicular to major axis
Eccentricity: ratio of the 2 axes
ji
ji
ppD ,max,
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LENGTH OF A BOUNDARY
Length:
the number of pixels along a boundary
give a rough approximation of its length
Number of vertical and horizontal components + √2 times the number of diagonal components
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DIAMETERS
Diameter:
D is a distance measure
pi and pj are points on the boundary B
Major axis (connecting the two extreme points)
)],([max)(Diam,
jiji
ppDB
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ECCENTRICITY
ratio of the major to the minor axis
major axis = the line connecting the two extreme points that comprise the diameter
minor axis = the line perpendicular to the major axis
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CURVATURE
Curvature: Rate of change of slope
i.e. using the difference between the slopes of adjacent boundary segments, which have been represented as straight lines, as a descriptor of curvature at the point of intersection of the segments.
Convex segment: change in slope at p is nonnegative
Concave segment: change in slope at p is negative
Ranges in the change of slope: Less than 10° line
More than 90° corner
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BOUNDARY DESCRIPTORS
Other Boundary Descriptors:
Shape numbers
Fourier descriptors
Moments
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Boundary Descriptors Shape Numbers
First difference
The shape number of a boundary is defined as the first difference of smallest magnitude.
The order n of a shape number is defined as the number of digits in its representation.
G3E-P.838
4-directional code
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Boundary Descriptors Shape Numbers
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Boundary Descriptors Shape Numbers
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Boundary Descriptors Fourier Descriptors
This is a way of using the Fourier transform to analyze the shape of a boundary.
The x-y coordinates of the boundary are treated as the real and imaginary parts of a complex number.
Then the list of coordinates is Fourier transformed using the DFT.
The Fourier coefficients are called the Fourier descriptors.
The basic shape of the region is determined by the first several coefficients, which represent lower frequencies.
Higher frequency terms provide information on the fine detail of the boundary.
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FOURIER DESCRIPTOR Given a set of boundary points (x0, y0), (x1, y1), (x2, y2), …,. (xK-1, yK-1).
Points on the boundary can be treated as a complex number s(k)=x(k)+jy(k)
Represent the sequence of coordinates as s(k)=[xk, yk], for k=0, 1, 2, …, K-1.
Using complex representation for each point:
s(k) = xk + jyk
Advantage: reducing 2D to 1D problem
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BOUNDARY DESCRIPTORS FOURIER DESCRIPTORS (1)
Step1:
Step2: (DFT)
Step3: (reconstruction)
if a(u)=0 for u>P-1
Disadvantage: Just for closed boundaries
74
( ) ( ) ( )s k x k jy k
1 2 /
0
1( ) ( )
K j uk K
ka u s k e
K
1 2 /
0( ) ( )
P j uk K
us k a u e
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BOUNDARY DESCRIPTORS FOURIER DESCRIPTORS (2)
What’s the reason that previous Fourier descriptors can’t be used for non-closed boundaries?
How can we use the method to descript non-closed boundaries?
(a)linear offset
(b)odd-symmetric
extension
75
•Original segment
s1(k)
(x0, y0)
(xK1, yK1) s2(k)
(b) Linear
offset
s3(k)
(c) Odd symmetric extension
Step 2
Step 3
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BOUNDARY DESCRIPTORS FOURIER DESCRIPTORS (3)
The proposed method is used not only for non-closed boundaries but also for closed boundaries.
Why we used proposed method to descript closed boundaries rather than previous method?
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Figure An ellipse and its Fourier description.
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Figure Example of angular Fourier descriptors.
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Figure Example of a contour defined by elliptic
Fourier descriptors.
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Figure Fourier approximation.
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Figure Example of elliptic Fourier descriptors.
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Figure Reconstructing an ellipse from a Fourier
description.
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Unrolling a feature shape by plotting the x- and y-coordinates of boundary points as a function of position along the boundary (the red and blue graphs) and combining them as real and imaginary parts of complex numbers (the magenta line).
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1D FOURIER (REVIEW) Fourier descriptor : the discrete Fourier transform (DFT) of s(k) is
The complex coefficients a(u), for u=0,1, 2, …, K-1, are called the Fourier descriptor of the boundary
The inverse Fourier restores s(k)
1
0
/21 K
k
KukjeksK
ua
1
0
/2K
u
Kukjeuaks
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FOURIER APPROXIMATION Usually, only the first few coefficients are used to reprsent the shape (k=0:M, M<N)
Using only the first P coefficients (a(u)=0 for u>P)
Please note that k still ranges from 0 to K-1
The smaller P becomes, the more detail that is lost on the boundary
There are ways to make the descriptor insensitive to translation, rotation, and scale changes.
1
0
/2ˆP
u
Kukjeuaks
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Boundary Descriptors Fourier Descriptors
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BOUNDARY DESCRIPTORS POLYNOMIAL APPROXIMATION(1)
Lagrange Polynomial
Cubic Spline Interpolation
87
0 ,0 , ,
0
( ) ( ) ( ) ( ) ( ) ( ) ( )n
n n n n k n k
k
P x f x L x f x L x f x L x
0 1 1,
0 1 1
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( ) ( )( ) ( )
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k k k k k k n
x x x x x x x xL x
x x x x x x x x
( 1)
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( 1)!
n
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f xf x P x x x x x x x
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' '
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" "
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( ) ( ) ( )
( ) ( )
( ) ( )
j j j j j
j j j j
j j j j
S x f x S x
S x S x
S x S x
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BOUNDARY DESCRIPTORS POLYNOMIAL APPROXIMATION(2)
Proposed method(1)
Step1: rotate the boundary and let two end point locate at x-axis
Step2: use second order polynomial to approximate the boundary
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( )f x
x
( ')f x
'x
( ')f x
'x
y
0 'x1 'nx
a
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n
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j
b ae y y y x b
a
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BOUNDARY DESCRIPTORS POLYNOMIAL APPROXIMATION(3)
Proposed method(2)
If the boundary is closed, how can we do?
Step1: use split approach divide the boundary to two parts.
Step2: use parabolic function to fit the boundary.
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1 'y
2 'y
1y
2y
1y
2y
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Boundary Descriptors Statistical Moments
Moments are statistical measures of data.
They come in integer orders.
Order 0 is just the number of points in the data.
Order 1 is the sum and is used to find the average.
Order 2 is related to the variance, and order 3 to the skew of the data.
Higher orders can also be used, but don’t have simple meanings.
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Boundary Descriptors Statistical Moments
Let r be a random variable, and g(ri) be normalized (as the probability of value ri occurring), then the moments are
1
0
)()()(K
k
i
n
in rgmrr
1
0
)( whereK
i
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MOMENTS OF SHAPE
The evaluation of moments represents a systematic method of shape analysis.
The most commonly used region attributes are calculated from the three low-order moments.
Knowledge of the low-order moments allows the calculation of the central moments, normalized central moments, and moment invariants.
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REGIONAL DESCRIPTORS
Simple descriptors:
Area
Perimeter
Compactness: (perimeter)2/area
Mean and median of the gray levels.
Topological descriptors:
# of holes
# of connected components
topological descriptors
texture
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SIMPLE DESCRIPTORS
The area of a region The number of pixels contained within its boundary
The perimeter of a region The length of its boundary
The compactness of a region perimeter * perimeter / area
Dimensionless, and thus insensitive to scale changes
Insensitive to orientation
Q: What shape gives the minimal compactness
The mean and median of the gray levels
The minimum and maximum gray-level values
The number of pixels with values above and below the mean
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SHAPE ANALYSIS
Shape measurements are physical dimensional measures that characterize the appearance of an object.
The goal is to use the fewest necessary measures to characterize an object adequately so that it may be unambiguously classified.
The shape may not be entirely reconstructable from the descriptors, but the descriptors for different shapes should be different enough that the shapes can be discriminated.
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Region Properties
Once a binary image has been processed we could obtain properties about the regions in the processed image.
Some of those properties are
Area, centroid, perimeter, perimeter length, circularity of the region and second circularity measure
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Table 11 1 Representative Shape Descriptors
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Area and Centroid
Area A = sum of all the 1-pixels in the region.
Centroid [r’,c’] is the average location of the pixels in the region
r’ = 1/A*Sum of all the rows in the region
c’ = 1/A*Sum of all the columns in the region.
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AREA
The area is the number of pixels in a shape.
The convex area of an object is the area of the convex hull that encloses the object.
original image net area filled area convex area
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PERIMETER
The perimeter [length] is the number of pixels in the boundary of the object.
The convex perimeter of an object is the perimeter of the convex hull that encloses the object.
perimeter external perimeter convex perimeter
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A simple definiton of the perimeter of a region without holes i set of its interior border pixels.
A pixel of a region is a border pixel if it has some neighboring pixel that is outside the region.
When 8-connectivity is used to determine whether a pixel inside the region is connected to a pixel outside the region, the resulting set of perimeter pixel is 4-connected.
When 4-connectivity is used to determine whether a pixel inside the region is connected to a pixel outside the region, the resulting set of perimeter pixel is 8-connected.
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PERIMETER
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THE PERIMETER:
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The length of Perimeter
To compute length |P| of perimeter P, the pixels in P must be ordered in a sequence P=<(r0,c0),. . . , (rk-1,ck-1)>, each pair of successive pixels in the sequence being neighbor, including the first and last pixels.
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MAJOR AND MINOR AXES
The major axis is the (x,y) endpoints of the longest line that can be drawn through the object. The major axis endpoints (x1,y1) and (x2,y2) are by computing the pixel distance between every combination of border pixels in the object boundary and finding the pair with the maximum length.
The minor axis is the (x,y) endpoints of the longest line that can be drawn through the object whilst remaining perpendicular with the major-axis. The minor axis endpoints (x1,y1) and (x2,y2) are found by computing the pixel distance between the two border pixel endpoints.
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ASPECT RATIO The major-axis length of an object is the pixel distance between the major-axis endpoints.
The minor-axis length of an object is the pixel distance between the minor-axis endpoints
The aspect ratio measures the ratio of the objects height to its width:
width
heightratioaspect
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COMPACTNESS (FORMFACTOR)
Compactness is defined as the ratio of the area of an object to the area of a circle with the same perimeter:
2perimeter
area4scompactnes
• A circle is used as it is the object with the most
compact shape: the measure takes a maximum
value of 1 for a circle
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COMPACTNESS (CONT.)
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CIRCULARITY OR ROUNDNESS
A measure of roundness or circularity (area-to-perimeter ratio) which excludes local irregularities can be obtained as the ratio of the area of an object to the area of a circle with the same convex perimeter:
• Roundness equals 1 for a circular object and less than 1
for an object that departs from circularity, except that it
is relatively insensitive to irregular boundaries.
2
convexperimeter
area4roundness
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Circularity(1):
With the area A and perimeter P defined, a common measure of the circularity of the region is the length of the perimeter squared divided by the area.
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ROUNDNESS (CONT.)
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CONVEXITY
Convexity is the relative amount that an object differs from a convex object. A measure of convexity can be obtained by forming the ratio of the perimeter of an object’s convex hull to the perimeter of the object itself:
external
convex
perimeter
perimeterconvexity
• This will take the value of 1 for a convex object, and will
be less than 1 if the object is not convex, such as one
having an irregular boundary.
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CONVEXITY (CONT.)
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SOLIDITY
Solidity measures the density of an object. A measure of solidity can be obtained as the ratio of the area of an object to the area of a convex hull of the object:
convex
net
area
areasolidity
• A value of 1 signifies a solid object, and a value
less than 1 will signify an object having an
irregular boundary (or containing holes).
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SOLIDITY (CONT.)
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EXTENSION
Extension is a measure of how much the shape differs from the circle. It takes value of zero if the shape is circular and increases without limit as the shape become less compact
1112 lnlnlog cE
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DISPERSION
Dispersion is the minimum extension that can be attained by compressing the shape uniformly. There is a unique axis, the long axis of the shape, along which the shape must be compressed in order to minimize its extension.
Dispersion is invariant to stretching, compressing or shearing the shape in any direction
2121212 ln2
1ln
2
1log cD
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ELONGATION
Elongation is the measure how much the shape must be compressed along its long axis in order to minimize the extension
Elongation never take a value of less than zero or greater than extension
2
1
2
1
2
12 ln
2
1ln
2
1log
cL
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CELL
SH
APE
No Extension Dispersion Elongation
1 0.1197 0.0542 0.0655
2 0.3998 0.0524 0.3474
3 0.7575 0.0550 0.7025
4 0.8725 0.1740 0.6985
5 0.0920 0.0262 0.0659
6 0.3784 0.0277 0.3506
7 0.7411 0.0313 0.7099
8 0.8591 0.1434 0.7157
9 0.0816 0.0138 0.0679
10 0.3617 0.0160 0.3457
11 0.7243 0.0199 0.7044
12 0.8350 0.1029 0.7321
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FIBER LENGTH
This gives an estimate as to the true length of a threadlike object.
Note that this is an estimate only. The estimate is fairly accurate on threadlike objects with a formfactor that is less than 0.25 and gets worse as the formfactor increases.
4
area16perimeterperimeterlength thread
2
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FIBER WIDTH
This gives an estimate as to the true width of a threadlike object.
Note that this is an estimate only. The estimate is fairly accurate on threadlike objects with a formfactor that is less than 0.25 and gets worse as the formfactor increases.
4
area16perimeter-perimeter widththread
2
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AVERAGE FIBER LENGTH
The number of skeleton end-points estimates the number of fibers (half the number of ends)
Average length:
Picture size =
3.56 in x 3.56 in
Skeletonization
Total length = 29.05 in
Number of
end-points = 14
points end ofnumber 0.5
length totallengthfiber average
Length=4.15 in
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EUCLIDEAN DISTANCE MAPPING
Euclidean Distance Map (EDM) converts a binary image to a grey scale image in which pixel value gives the straight-line distance from each originally black pixel within the features to the nearest background (white) pixel.
EDM image can be thresholded to produce erosion, which is both more isotropic and faster than iterative neighbor-based morphological erosion.
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REGIONAL DESCRIPTORS
Principal axes:
Eigenvectors of the covariance matrix
Ratio of large to small eigenvalue: insensitive to scale and rotation
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REGIONAL DESCRIPTORS
Other descriptors:
Mean and median of gray levels
Min. and max. gray-level values
# pixels with values above and below the mean
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Regional Descriptors Example
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TOPOLOGICAL DESCRIPTORS
Topology:
properties of figures that are unaffected by deformations
no tearing or joining though rubber sheet distortions.
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TOPOLOGICAL DESCRIPTORS
Examples:
number of of holes H
number of of connected components C:
A subset of maximal size such that any two points can be joined by a connected curve lying entirely within the subset.
Euler number E: E = C-H
also a topological property
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Regional Descriptors Topological Descriptors
Topological property 1:
the number of holes (H)
Topological property 2:
the number of connected
components (C)
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TOPOLOGICAL DESCRIPTORS
Regions represented by straight line segments (polygonal network):
–V: # of vertices
–Q: # of edges
–F: # of faces Euler formula:
V-Q+F = C-H = E
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Regional Descriptors Topological Descriptors
Topological property 3:
Euler number: the number of connected components subtract the number of holes
E = C - H
E=0 E= -1
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REGIONAL DESCRIPTORS TOPOLOGICAL E = V - Q + F = C – H
E: Euler number
V: the number of vertices
Q: the number of edges
F: the number of faces
C: the number of
connected component
H: the number of holes
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Representation & Description
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Regional Descriptors Topological Descriptors
Topological
property 4:
the largest
connected
component.
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TEXTURE DESCRIPTORS
Statistical approach
Describe the smoothness, coarseness, and graininess of the of region
Structural approach
Describe the arrangement
Spectral approach
Use Fourier spectrum to detect global periodicity
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Regional Descriptors Texture
Texture is usually defined as the smoothness or roughness of a surface.
In computer vision, it is the visual appearance of the uniformity or lack of uniformity of brightness and color.
There are two types of texture: random and regular. Random texture cannot be exactly described by words or equations; it must be described statistically. The surface of a pile of dirt or rocks of many sizes would be random.
Regular texture can be described by words or equations or repeating pattern primitives. Clothes are frequently made with regularly repeating patterns.
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REGIONAL DESCRIPTORS TEXTURE
Statistical approaches
smooth, coarse, regular
nth moment:
2th moment:
is a measure of gray level contrast(relative smoothness)
3th moment:
is a measure of the skewness of the histogram
4th moment:
is a measure of its relative flatness
5th and higher moments:
are not so easily related to histogram shape
143
1
0( ) ( ) ( )
L n
n i iiu z z m p z
1
0( )
L
i iim z p z
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Regional Descriptors Statistical Approaches
Let z be a random variable denoting gray levels and let p(zi), i=0,1,…,L-1, be the corresponding histogram, where L is the number of distinct gray levels.
The nth moment of z:
The measure R:
The uniformity:
The average entropy:
1
0
)( whereL
i
ii zpzm
)(log)( 2
1
0
i
L
i
i zpzpe
)(1
11
2 zR
1
0
2 )(L
i
izpU
1
0
)()()(L
k
i
n
in zpmzz
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The second moment is the variance and can be used to describe the smoothness
The third moment (skewness): measure skewness of the histogram
The fourth moment (kurtosis): measure the flatness of the histogram
Special moments
0
1
1
0
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146
Moments - examples
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G3E-P.850
TEXTURE DESCRIPTORS
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Regional Descriptors Statistical Approaches
Smooth Coarse Regular
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Regional Descriptors Structural Approaches
Structural concepts:
Suppose that we have a rule of the form S→aS, which indicates that the symbol S may be rewritten as aS.
If a represents a circle and the meaning of “circle to the right” is assigned to a string of the form aaaa… .
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Fourier spectrum is ideally suited for describing the directionality of periodic or almost periodic 2-D patterns in an image
Global texture patterns can be easily distinguishable as concentrations of high-energy bursts in the spectrum
Prominent peaks give the principal direction of the texture patterns
The location of the peaks give the fundamental spatial period of the patterns
Eliminating any periodic components via filtering leaves nonperiodic image elements which can then be described by statistical techneques
Spectral approach
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Regional Descriptors Spectral Approaches
For non-random primitive spatial patterns, the 2-dimensional Fourier transform allows the patterns to be analyzed in terms of spatial frequency components and direction.
It may be more useful to express the spectrum in terms of polar coordinates, which directly give direction as well as frequency.
Let is the spectrum function, and r and are the variables in this coordinate system.
For each direction , may be considered a 1-D function .
For each frequency r, is a 1-D function.
A global description:
0
)()( rSrS
),( rS
0
1
)()(R
r
rSS
),( rS
)(rS
)(rS
G3E-P.860
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152
Spectral approaches - examples
0
1
0
)()(
)()(
R
r
rSS
rSrS
G3E-P.860
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Regional Descriptors Spectral Approaches
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Regional Descriptors Spectral Approaches
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Regional Descriptors Moments of Two-Dimensional Functions
For a 2-D continuous function f(x,y), the moment of order (p+q) is defined as
The central moments are defined as
,...3,2,1,for ),(
qpdxdyyxfyxm qp
pq
dxdyyxfyyxx qp
pq ),()()(
00
01
00
10 and wherem
my
m
mx
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Regional Descriptors Moments of Two-Dimensional Functions
If f(x,y) is a digital image, then
The central moments of order up to 3 are
x y
qp
pq yxfyyxx ),()()(
00
00
00 ),(),()()( myxfyxfyyxxx yx y
0)(),()()( 00
00
1010
01
10 mm
mmyxfyyxx
x y
0)(),()()( 00
00
0101
10
01 mm
mmyxfyyxx
x y
10110111
00
011011
11
11
),()()(
mymmxm
m
mmmyxfyyxx
x y
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Regional Descriptors Moments of Two-Dimensional Functions
The central moments of order up to 3 are
1020
02
20 ),()()( mxmyxfyyxxx y
0102
20
02 ),()()( mymyxfyyxxx y
01201121
12
21 22),()()( mxmymxmyxfyyxxx y
10021112
21
12 22),()()( mymxmymyxfyyxxx y
10
2
2030
03
30 23),()()( mxmxmyxfyyxxx y
01
2
0203
30
03 23),()()( mymymyxfyyxxx y
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Regional Descriptors Moments of Two-Dimensional Functions
The normalized central moments are defined as
00
pq
pq
,....3,2for 12
where
qpqp
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Regional Descriptors Moments of Two-Dimensional Functions
A seven invariant moments can be derived from the second and third moments:
2
0321
2
123003210321
2
0321
2
1230123012305
2
0321
2
12304
2
0321
2
12303
2
11
2
02202
02201
)()(3))(3(
)(3)())(3(
)()(
)3()3(
4)(
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Regional Descriptors Moments of Two-Dimensional Functions
This set of moments is invariant to translation, rotation, and scale change.
2
0321
2
123003213012
2
0321
2
1230123003217
0321123011
2
0321
2
123002206
)()(3))(3(
)(3)())(3(
))((4
)()()(
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Regional Descriptors Moments of Two-Dimensional Functions
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Table 11.3 Moment invariants for the images in Figs. 11.25(a)-(e).
Regional Descriptors Moments of Two-Dimensional Functions
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Figure Horizontal axis ellipse moments.
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Figure Describing a shape by centralised moments.
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Figure Describing a shape by invariant moments.
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Connected Components
Components are objects that share at least one common neighbor (in 4- or 8- neighborhood).
Defn: A connected component labeling of binary image B is a labeled image LB in which the value of each pixel is the label of its connected component.
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MORPHOLOGY
Morphology deals with form and structure
Mathematical morphology is a tool for extracting image components useful in:
representation and description of region shape
preprocessing (filtering, thinning, etc.)
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BASIC MORPHOLOGICAL ALGORITHMS
Purpose:
to extract image components that are useful in the representation and description of shape.
Boundary Extraction:
) ()( BAAA
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BASIC MORPHOLOGICAL ALGORITHMS
Extraction of Connected Components:
ABXX kk )( 1k=1,2,3,…
Where X0=p
when Xk=Xk-1 the algorithm has converged.
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171
INTRODUCTION
Applicable to boundaries and regions.
First developed by Hotelling
Goal: Remove the correlation among the element of a random vector.
Aside: x is a random vector if each element xi of x is a random variable.
What is a random variable?
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MEAN AND COVARIANCE is a population of random vectors of length n
Mean vector is
Covariance matrix is defined as
K
k
kkx xK
xEm1
1
T
xx
K
k
T
kk
T
xkxkx mmxxK
mxmxEC 1
1
Expected value
Kkkx
..1
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EXAMPLE
Considering 4 vectors x1=(0,0,0)T, x2=(1,0,0)T, x3=(1,1,0)T and x4=(1,0,1)T
Mean vector and covariance matrix are
How to interpret entries of the covariance matrix?
1
1
3
4
1xm
311
131
113
16
1xC
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WHAT TO DO WITH CX?
We want to transform the vectors such that elements of the new vectors are uncorrelated.
Making the off-diagonal elements of covariance matrix 0.
Cx is real and symmetric, so there exists a set of n orthonormal eigenvectors, ei with corresponding eigenvalues in descending order. i
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HOTELLING TRANSFORM (PRINCIPAL-COMPONENT ANALYSIS)
Let A be a matrix in the form
Create a new set of vectors
T
n
T
T
e
e
e
A2
1Largest eigenvalue
Smallest eigenvalue
Kkky
..1
xmxAy
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MY AND CY FOR Mean vector is 0 (zero vector)
Covariance matrix is
Exactly what we want: 0 off-diagonal elements
Kkky
..1
n
T
xy AACC
0
0
2
1
Cx and Cy share the same
eigenvalues and
eigenvectors
Still remember matrix
diagonalization?
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RECONSTRUCTION OF X FROM Y
A is orthonormal, i.e. A-1 = AT
x can be recovered from corresponding y
Approximation can be made by using the first k eigenvectors of Cx to form Ak
x
T myAx
x
T
k myAx ˆ
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APPLICATION: BOUNDARY DESCRIPTION
e1 and e2 are the
eigenvectors of the
object
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(CON’D)
are the points on the boundary
x=(a,b)T, where a and b are the coordinate values w.r.t. x1- and x2-axes.
The result of Hotelling (principal component) transformation is a new coordinate system:
Origin at the centroid of the
boundary points
Axes are the direction of the
eigenvectors of Cx
Kkkx
..1
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(CON’D) Aligning the data with the eigenvectors, i.e.
, we get
Aside: is the variance of component yi along eigenvector ei
xmxAy
i
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DESCRIPTION INVARIANT TO …
Rotation:
Aligning with the principal axes removes rotation
Scaling:
Normalization using eigenvalues (variance)
Translation:
Accounted for by centering the object about its mean
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RELATIONAL DESCRIPTORS
To organize boundaries & regions to exploit any structural relationships that may exist between them.
Example:
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RELATIONAL DESCRIPTORS
In the previous example:
Recursive relationship involving the primitive elements a and b.
Rewriting rules:
S aA
A bS, and
A b
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RELATIONAL DESCRIPTORS
When dealing with disjoint structures, tree descriptors are used:
A tree T is a finite set of one or more nodes for which:
There is a unique node $ designated the root
The remaining nodes are partitioned into m disjointed sets T1, …, Tm, each of which in turn is a tree called a subtree of T.
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RELATIONAL DESCRIPTORS
The tree frontier is the set of nodes at the bottom of the tree (leaves), taken in order from left to right.
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RELATIONAL DESCRIPTORS
Two types of information are important (in a tree):
Information about a node stored as a set of words describing the node
Information relating a node to its neighbors stored as a set of pointers to those neighbors
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Representation & Description
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Representation & Description
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Representation & Description
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