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8/3/2019 Notes Ip11
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(11)(11) Representation and DescriptionRepresentation and Description
- Low-level image processing* Image enhancement, restoration, transformation
Image ImageEnhancement
EnhancedImage
Restored/Transformed
ImageRestoration/Transformation
- Mid-level image processing (image understanding)* Object representation, description
Restored/Transformed
ImageImageSegmentation
SegmentedImage
Representation/Description/Features
Object
Representation/Description
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- High-level image processing (recognition and interpretation)* Object recognition, interpretation of object relationships
ObjectRecognition
Objects
Meaning/
RelationshipsImageInterpretation
Representation/Description/
Features
(a)(a) Chain codeChain code
- Chain codes: represent a boundary by a connected sequence of straight-line segments of specified length and direction* Choose an appropriate grid to approximate objects
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0
3
2
1
4-direction: 8-direction:
04
1
2
3
56
7
2
0 0
3
3
3
3
3
3
22
12
1
1
10
01
1
07
6
6
6
5
53
3
2
1
2
20
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* Chain code (clockwise): 4-direction: 00333332322121110101, 8-direction: 07666553321202
* Problem 1: different starting points result in different chain codes# Solution: normalization redefine the starting point such that the chain
code forms a smallest number# E.g.: 6553320000 0000655332
* Problem 2: object rotation results in different chain codes# Solution: difference code coding with the difference of directions
(counter-clockwise)
# E.g.: 0000655332 0006706076 0006706076 (normalization)
(b)(b) Polygonal approximationPolygonal approximation
- Polygonal approximation: approximate a boundary using a polygon
- Minimum perimeter polygons* Choose an appropriate grid The boundary is enclosed by a set of concatenated cells
* Allow the boundary to shrink as a rubber band
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- Merging techniques
1. Merge points along a boundary until the least square error line fit ofthe points merged so far exceeds a threshold
2. Record the the two end point of the line3. Repeat Steps 1 and 2 until all boundary points are processed
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- Splitting techniques
Subdivide a boundary segment successively into two parts until aspecified criterion is satisfied
1. Find two points on the boundary that are farthest away and draw a line
Splitting line divides the boundary into two boundary segments2. For each boundary segment, find a point on the boundary that has a
maximum perpendicular distance to its corresponding line
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3. Draw two lines joining the point and the two end points, respectively,of the corresponding splitting line Subdivide the boundary segment into two parts
4. Repeat Steps 2~3 until the perpendicular distance is less than a
threshold
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(c)(c) SignatureSignature
- Signature: a 1D functional representation of a boundary* Plot the distance from the centroid to the boundary as a function of
angles:signature = r(), = 0 ~ 2
(d)(d) Boundary descriptorsBoundary descriptors
- Descriptions of boundary
* Perimeter: length of the chain code* Diameter: maxi,j(D(pi,pj))
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pi
pj
Ma= max
i,j(D(p
i,p
j))
Ma
ma
and forms a
basic rectangle
Ma
ma
* Major axis (Ma) and minor axis (ma)* Eccentricity: Ma/ma
- MATLAB p = bwperim(bw, conn): find perimeter pixels* bw: binary image* conn: 4 or 8 (connectivity)* Perimeter pixels: 1-valued pixels that are connected to at least one 0-
valued pixels* p
: returned perimeter binary image- MATLAB S = diameter(L): find descriptions of a boundary
* L: a labeled image
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* S: a structure with the following fields# S.Diameter, S.MajorAxis, S.MinorAxis, S.BasicRectangle
(e)(e) Regional descriptorsRegional descriptors
- Compactness: perimeter2/area
- Topological: Euler number,E= C H (number of connected component number of holes)
E= 1 3 = 2 E= 1 1 = 0 E= 1 2 = 1
- MATLAB D = regionprops(L, properties)
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* L: labeled image* Properties: 'Area', 'BoundingBox', 'Centroid'* D: a structure with the fields specified when invoking regionprops( )
* E.g., D.Area
- Texture* Statisticcal approaches
* Intensity mean: m=i=0
L1
zip zi ,
* nth moment about the mean: n=
i=0L1
zimnpzi
Moment Expression Description
Mean m=i=0L1
zipzi Measure of average intensity
Standard deviation =zz=2
Measure of average contrast
Smoothness R = 11/(1+2
) Measure of smoothness of intensity
Third moment 3=i=0L1
zim3pzi Measure of skewness of the
histogram; 0: symmetric, positive:skewed to the right; negative:
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skewed to the left
Uniformity U=i=0
L1
p2zi Measure of uniformity; maximum: all
graylevels are equal
Entropy e=i=0
L1
pzi log2 zi Measure of randomness
* Sprectral approach: based on Fourier spectrum* Structural approach: structure of the texture primitives
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(f)(f) High-level image processingHigh-level image processing
- High-level image processing (computer vision)* Recognition and interpretation
* Object recognition: identify objects based on object models* Interpretation: using artificial intelligence to infer the following
# Properties of objects: identity, size, material, 2D/3D position, orientation# Relationships among objects: occlusion, relative position
Further inference: planning of path, operations, control
- Applications: robotics, industrial automatic inspection, autonomousnavigation, document image analysis, bio-information recognition
11-13