1. Genetic Algorithms and Image Understanding Sam Clanton Computer Integrated Surgery II March 14, 2001

2. Resources Bhanu, Bir and Lee, Sunkee. Genetic Learning for Adaptive Image Segmentation. Kluwer Academic Publishers, 1994 Goldberg, David. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison Wesley Longman, 1989.

3. Genetic Algorithms Optimization Problems Adaptive Systems Speed-Critical Applications Are Useful For…

4. General Problem to be Solved The k-armed bandit problem Picture: Goldberg How do we maximize our winnings? GA’s are good for multiple, many-armed bandits.

5. What Is a Genetic Algorithm? Operates on principle of survival of the fittest “Population Pool” of Parameters Genetic Operators - Reproduction, Crossover, and Mutation

6. Survival Of the Fittest Analogous to survival in biological system Fitness Function Optimization == Finding most fit parameter set for a particular problem S elk (an elk) ~ Ability to run away (elk, lions, tigers) Ability to run away (herd, lions, tigers) Spset(a pset) ~ Ability to perform task(pset, input) Ability to perform task(population, input)

7. Population Pool 24, 32, 76, 1 34, 43, 6, 17 “Surviving” parameter sets are kept around Individuals are extracted and applied when input resembles past input for that individual. Genetic operators add new individuals to pool Individuals can be dropped when they appear useless

8. Genetic Operators Affect survival of particular schema Schema - string representation of a feature Reproduction f(H) / f avg Crossover 1 - p c * L(H) / L(total) Mutation 1 - L(H) * p m

9. Feature Preservation Overall Equation m(H, t+1) = m(H, t) * F(H)/f avg Reproduction * (1 - p c L(H) / L(tot) Crossover - L(H) * p m ) Mutation

10. An Example - Reproduction StringInitial Pop X valF(x) = x * x Pselect (fitness/ total fitness) Exp. Count (fitness / avg fitness) Actual Count (roulette) 10110113169.14.581 21100024576.491.972 301000864.06.220 41001119361.311.231 Sum1170 Avg293 Max576

11. An Example - Crossover StringPop. Pool (w/ Crossover) MateCrossover Site New Pop. Pool X valueF(x) 10110|1240110012144 201100|0141100125625 311|000421101127729 410|011321000016256 Sum1754 Avg439 Max739

12. GA’s in Image Segmentation Optimization problem “Twiddling Knobs” Approach Relationship to “Many k-armed bandit” problem Figure: Bhanu, Lee

13. GA method for image segmentation Figure: Bhanu, Lee

14. Images differ in characteristics such as brightness, saturation, skewness, entropy, etc. Use these values as inputs to genetic algorithm Figure: Bhanu, Lee Image Analysis

15. Evolution of Segmentation System Figure: Bhanu, Lee

16. ` The project: Implementation in DSP / FPGA Image Capture Image Processor Genetic optimizer Collator Memory Output Edge Detectors FPGA DSP