fuzzy logic ppt
TRANSCRIPT
Fuzzy Logic and Fuzzy Set Theorywith examples from Image Processing
By: Rafi Steinberg
4/2/20081
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Some Fuzzy Background
Lofti Zadeh has coined the term “Fuzzy Set” in 1965 and opened a new field of research and applicationsA Fuzzy Set is a class with different degrees of membership. Almost all real world classes are fuzzy!Examples of fuzzy sets include: {‘Tall people’}, {‘Nice day’}, {‘Round object’} …
If a person’s height is 1.88 meters is he considered ‘tall’?What if we also know that he is an NBA player?
3
Some Related Fields
Fuzzy Logic & Fuzzy Set
Theory
Evidence Theory
Pattern Recognition &
Image Processing
Control Theory
Knowledge Engineering
4
Overview
L. ZadehD. DuboisH. PradeJ.C. BezdekR.R. YagerM. SugenoE.H. MamdaniG.J. KlirJ.J. Buckley
Membership Functions
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A Crisp Definition of Fuzzy Logic
• Does not exist, however …- Fuzzifies bivalent Aristotelian (Crisp) logicIs “The sky are blue” True or False?
• Modus PonensIF <Antecedent == True> THEN <Do Consequent>IF (X is a prime number) THEN (Send TCP packet)• Generalized Modus PonensIF “a region is green and highly textured” AND “the region is somewhat below a sky region”THEN “the region contains trees with high confidence”
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Fuzzy Inference (Expert) SystemsInput_1
Fuzzy IF-THEN
Rules
Output
Input_2
Input_3
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Fuzzy Vs. Probability
Walking in the desert, close to being dehydrated, you find two bottles of water:
The first contains deadly poison with a probability of 0.1
The second has a 0.9 membership value in the Fuzzy Set “Safe drinks”
Which one will you choose to drink from???
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Membership Functions (MFs)
• What is a MF? • Linguistic Variable• A Normal MF attains ‘1’ and ‘0’ for some input
• How do we construct MFs?– Heuristic– Rank ordering– Mathematical Models– Adaptive (Neural Networks, Genetic Algorithms …)
1 2 1 2, 1, 0A Ax x x x
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Membership Function Examples
TrapezoidalTriangular
1
, ,1
smf a x cf x a c
e
Sigmoid
2
22; ,x c
gmff x c e
Gaussian
; , , , max min ,1 , ,0x a d x
f x a b c db a d c
; , , max min , , 0
x a c xf x a b c
b a c b
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Alpha Cuts
AA x X x
AA x X x
Strong Alpha Cut
Alpha Cut0
0.2 0.5 0.8 1
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Linguistic Hedges
Operate on the Membership Function (Linguistic Variable)1. Expansive (“Less”, ”Very Little”)2. Restrictive (“Very”, “Extremely”)3. Reinforcing/Weakening (“Really”, “Relatively”)
Less x
4Very Little x
2Very x
4Extremely x
A Ax x c
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Aggregation Operations
1
2121 ,,,
n
aaaaaah nn
0 0, ,1iand a i i n
, min
1 ,
0 ,
1 ,
, max
h
h Harmonic Mean
h Geometric Mean
h Algebraic Mean
h
Generalized Mean:
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Aggregation Operations (2)• Fixed Norms (Drastic, Product, Min)• Parametric Norms (Yager)
T-norms:
, 1
, , 1
0 ,D
b if a
T a b a if b
otherwise
Drastic Product
, min ,ZT a b a b ,T a b a b
Zadehian
,BSS a b a b a b , 0
, , 0
1 ,D
b if a
S a b a if b
otherwise
S-Norm Duals:
, max ,ZS a b a b
Bounded Sum DrasticZadehian
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Aggregation Operations (3)
DrasticT-Norm Product Zadehian
min
Generalized Mean
Zadehian max
BoundedSum
Drastic S-Norm
Algebraic (Mean)
Geometric
Harmonic
b (=0.8)a (=0.3)
1
, min 1, 0,w w wu a b a b for w
Yager S-Norm
Yager S-Norm for varying w
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Crisp Vs. Fuzzy
Fuzzy Sets• Membership values on [0,1]• Law of Excluded Middle and Non-
Contradiction do not necessarily hold:
• Fuzzy Membership Function• Flexibility in choosing the
Intersection (T-Norm), Union (S-Norm) and Negation operations
Crisp Sets• True/False {0,1}• Law of Excluded Middle and Non-
Contradiction hold:
• Crisp Membership Function• Intersection (AND) , Union (OR),
and Negation (NOT) are fixed
A A
A A
A A
A A
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Image Processing
BinaryGray LevelColor (RGB,HSV etc.)
Can we give a crisp definition to light blue?
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Fuzziness Vs. Vagueness
Vagueness=Insufficient Specificity
“I will be back sometime”
Fuzzy Vague
“I will be back in a few minutes”
Fuzzy
Fuzziness=Unsharp Boundaries
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Fuzziness
“As the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes” – L. Zadeh
• A possible definition of fuzziness of an image:
2min ,ij ij
i j
FuzzM N
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Example: Finding an Image Threshold
Membership Value
Gray Level
1
, ,1
smf a x cf x a c
e
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Mathematical Morphology
• Operates on predefined geometrical objects in an image• Structured Element (SE) represents the shape of interest• Initially developed for binary images; extended to grayscale using
aggregation operations from Fuzzy Logic
Some Examples: Dilation, Erosion, Open, Close, Hit&Miss, Skeleton
0 1 0
0 1 0
0 1 0
SE
68 12 4 32 60
16 12 4 32 60
16 40 28 56 12
16 40 52 8 12
40 72 76 8 12
imerode
68 12 4 32 60
92 80 28 56 64
16 40 52 80 88
40 100 76 84 12
44 72 100 8 36
im
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Fuzzy Mathematical Morphology
“Does it fit” “How well does a SE fit”
,
,
, max , ,
, min , ,
Gj k B
Gj k B
D A B a m j n k b j k
E A B a m j n k b j k
For B=0 , max
, min
GB
G B
D A B A
E A B A
max min
min max
G BB
G B B
O A
C A
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Some Basic Concepts
, ,
# 2 L
L a b c
elements in the Power Set
Universe of Discourse:
Power Set of X= P(X)= {Null , {a} , {b} , {c} ,{a , b},{b , c}, {a , c}, {a,b,c}}
Singletons of the Power Set of X: { {a} , {b} , {c} }An Event=An Element of the Power SetBasic Probability Assignment (BPA)
Focal Element
m(A)=0.2
m(C)=0.5
m(B)=0.3
m(A)=0.2Consonant Body of
Evidence
1
0i
iA P X
m A
m
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Fuzzy Measures
Additive, Sub Additive, Super Additive MeasuresExamples: {Probability}, {Belief, Plausibility}, {Necessity, Possibility}
0 , 1g g X
, ,if A B A B P X then g A g B (2) Monotonicity:
(1) Boundary Condition:
: 0,1set function g P x
(3) Uniform Convergence
1i iA
increasing sequence of measurable sets we have uniform convergence:
lim limi ii iA A
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Example: Fuzzy MeasureMembership
in the Set “Our Line”
Symbolic Representation of the Measure
Observed Image
1
0.7
0.5
0.7
0.2
0.3
0.2
0
1 3,x x 1 2,x x
1 2 3, ,x x x
2 3,x x
3x
2x
1x
1 2 3, ,Line x x x
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The Choquet Integral
• Is defined over a Fuzzy Measure• Consider a gray level input
f(x3) f(x2) f(x1)
1 0.4 0.8
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Example: Choquet Integral CalculationCorresponding
MeasureSet
Representation
0.2 1 0 0
0.2 1 0 0
0.5 1 0 1
0.5 1 0 1
0.5 1 0 1
0.5 1 0 1
1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 10.64 1 0.4 0.8
10f
9f
7f
8f
5f
6f
3f
4f
1f
2f
/10
1x 2x 3x
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Sugeno Measures
1
, 1 ,1
AA
A
1,
A B A B A B
for and A B
Sugeno Inverse for λ={-0.99, -0.9, -0.5, 0, 1, 10}
Sugeno Inverse:
Sugeno Measure’s Additional Axiom:
1 1 ii
x Compute λ from the normalization rule:
Optimistic/Pessimistic Aggregation of Evidence
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Finding the Sugeno MeasureWe need to solve the third order equation:
Solutions: {0, -15, 5/3}Since λ=0 is the trivial additive solution and since λ =-15 is out of range (λ>-1) we choose λ=5/3 and obtain:
21 1 0.3 1 0.2
1 3, 0.47x x
2 3 1 2, , 0.6x x x x
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Example: Sugeno Integral Calculation
3 1 3 1 2 3, , , , ,h g h a g x h b g x x h c g x x x
0.9 0.2 , 0.5 0.47 , 0 1
max min 0.9,0.2 , min 0.5,0.47 0.47
-> We cannot aggregate with the Sugeno Union since the segmenting alpha cut values are not part of our initial frame of discernment
-> Zadehian Max-Min are ‘good’ default operators
h(q) is the alpha cut that entirely includes the measure of q.
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Example: Finding Edges
12ˆ min 1, min ,
1ij
mn iji j ijW
max min min
1max max max
ij ij ij ijspatial spatial spatial
ij ij
ij ij ijspatial global spatial
g g g
g g
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O.K. So Now What?
• We have a fuzzy result, however in many cases we need to make a crisp decision (On/Off)
• Methods of defuzzifying are:– Centroid (Center of Mass)– Maximum– Other methods
A
A
x x dxCentroid
x dx
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Fuzzy Inference (Expert) SystemsService Time
Fuzzy IF-THEN
Rules
Tip Level
Food Quality
Ambiance
Fuzzify: Apply MF on
input
Generalized Modus Ponens with specified aggregation
operations
Defuzzify: Method of Centroid,
Maximum, ...
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Automatic Speech Recognition (ASR) via Automatic Reading of Speech Spectrograms
Phoneme Classes:VowelsSemi-vowels/DiphthongsNasalsPlosivesFricativesSilence
Examples of Fuzzy Variables:Distance between formants (Large/Small)Formant location (High/Mid/Low)Formant length (Long/Average/Short)Zero crossings (Many/Few)Formant movement (Descending/Ascending/Fixed)VOT= Voice Onset Time (Long/Short)Phoneme duration (Long/Average/Short)Pitch frequency (High/Low/Undetermined)Blob (F1/F2/F3/F4/None)
“Don’t ask me to carry…"
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Applying the Segmentation Algorithm
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Suggested Fuzzy Inference SystemFeature Vector from Spectrogram
Identify Phoneme Class using Fuzzy IF-THEN Rules
Vowels
Find Vowel
Fricatives
Nasals
Output Fuzzy MF for each Phoneme
Assign a Fuzzy Value for each Phoneme, Output Highest N Values to a
Linguistic model
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Summary• Fuzzy Logic can be useful in solving Human related tasks• Evidence Theory gives tools to handle knowledge• Membership functions and Aggregation methods can be selected according to the problem at hand
Some things we didn’t talk about:• Fuzzy C-Means (FCM) clustering algorithm• Dempster-Schafer theory of combining evidence• Fuzzy Relation Equations (FRE)• Compositions• Fuzzy Entropy
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References[1] G. J. Klir ,U. S. Clair, B. Yuan“Fuzzy Set Theory: Foundations and Applications “, Prentice Hall PTR 1997, ISBN: 978-0133410587 [2] H.R. Tizhoosh;“Fast fuzzy edge detection” Fuzzy Information Processing Society, Annual Meeting of the North American, pp. 239 – 242, 27-29 June 2002. [3] A.K. Hocaoglu; P.D. Gader; “ An interpretation of discrete Choquet integrals in morphological image processing Fuzzy Systems “, Fuzzy Systems, FUZZ '03. Vol. 2, 25-28, pp. 1291 – 1295, May 2003. [4] E.R. Daugherty, “An introduction to Morphological Image Processing”, SPlE Optical Engineering Press, Bellingham, Wash., 1992.[5] A. Dumitras, G. Moschytz, “Understanding Fuzzy Logic – An interview with Lofti Zadeh”, IEEE Signal Processing Magazine, May 2007 [6] J.M. Yang; J.H. Kim, ”A multisensor decision fusion strategy using fuzzy measure theory ”, Intelligent Control, Proceedings of the 1995 IEEE International Symposium on, pp. 157 – 162, Aug. 1995 [7] R. Steinberg, D. O’Shaugnessy ,”Segmentation of a Speech Spectrogram using Mathematical Morphology ” ,To be presented at ICASSP 2008.[8] J.C. Bezdek, J. Keller, R. Krisnapuram, N.R. Pal, ” Fuzzy Models and Algorithms for Pattern Recognition and Image Processing ” Springer 2005, ISBN: 0-387-245 15-4 [9] W. Siler, J.J. Buckley,“Fuzzy Expert Systems and Fuzzy Reasoning“, John Wiley & Sons, 2005, Online ISBN: 9780471698500[10] http://pami.uwaterloo.ca/tizhoosh/fip.htm[11] "Heavy-tailed distribution." Wikipedia, The Free Encyclopedia. 22 Jan 2008, 17:43 UTC. Wikimedia Foundation, Inc. 3 Feb 2008 http://en.wikipedia.org/w/index.php?title=Heavy-tailed_distribution&oldid=186151469[12] T.J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill 1997. ISBN: 0070539170
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Heavy-Tail DistributionsExamples: Alpha Stable (Cauchy, Pareto), Weibull, Student-T, Log-Normal …
Problem – different samples with very low probability occur very frequentlySolution: Smoothing the probability density function; Good or Bad??Another Solution: Use Possibility (Membership function) and Necessity as envelopes
Example: Amazon sells far more books that are ‘very unpopular’ than popular books
Another example: Automatic translation – most words in English have a very low frequency of occurrence. However, we often find such rare words in a sentence.
Bonus Slide
lim Pr , 0x
xe X x