Interactive Optimization by Genetic Algorithms

1
Interactive Optimization by Genetic Algorithms

• Cases Lighting Patterns and Image Enhancement
• Janne Koljonen
• Electrical Engineering and Automation, University
  of Vaasa

2
Outline

• Interactive Evolutionary Computation (IEC) in
  general.
• Image enhancement.
• LED adaptive luminence lighting system (LEDall).
• Project work (LEDall).

3
Interactive optimization by GA

• In Interactive Evolutionary Computation (IEC),
  the computational fitness function is replaced by
  a human evaluator.
• In other aspects the genetic algorithm may be as
  usual.

4
Application domains of IEC

• In cases, where the favorable output can be
  usually evaluated only subjectively, IEC is
  applied.
• Such domains are e.g. music, graphics and image
  enhancement.

5
Constraints

• The human intervention is a bottleneck of IEC
  system it is time consuming, difficult and even
  boring to evaluate outputs of a system.
• The time constraint and patience of the user can
  be overcome by limiting the number of fitness
  evaluations.
• The attention has to be paid to the problem
  complexity and the algorithm so that search
  strategy can be guided gradually from a global
  phase into a fine-tune search by the user.

6
IEC strategies

• A few strategies for the subjective fitness
  evaluation have been reported evaluation score
  points with n levels, selection of elite, which
  actually corresponds given score points from 2
  levels, and pair-wise tournament.
• Others?
• How to compare n outputs in parallel/in sequence?

7
IEC strategy suggestions

• In addition to a fitness function, the user can
  be used to select the genetic operators that
  should be applied
• Requires more expertise
• Alternatively, GA could have a pool of different
  operators and a mechanism to learn, which
  operators are efficient in different cases.

8
Image enhancement

• People use image processing tools increasingly as
  the costs of digital cameras have decreased.
• However, image processing tools contain nowadays
  dozens of filters with a few parameters each.
• An inexperienced user is barely capable of
  deciding, which filters and parameters to use.
  Presumable the method of trial and error is
  applied in such cases, which is time consuming.
• Moreover, rarely one single filter is enough for
  the desired output but a sequence of filters and
  integrations of filtered images are required.

9
Objective

• Image enhancement and image restoration are
  usually applied to improve the quality of the
  pictures or to emphasize certain features and
  details.
• The result is another image that meets better the
  requirements set for the image in a specific
  application.
• The difference, by definition, of image
  enhancement and image restoration is in the
  output evaluation.
• While image enhancement is evaluated
  subjectively, the objective of restoration is to
  recover the original image subjected to e.g.
  noise or other degradation.

10
Objective

• Image enhancement and image restoration are
  usually applied to improve the quality of the
  pictures or to emphasize certain features and
  details.
• The result is another image that meets better the
  requirements set for the image in a specific
  application.
• The difference, by definition, of image
  enhancement and image restoration is in the
  output evaluation.
• While image enhancement is evaluated
  subjectively, the objective of restoration is to
  recover the original image subjected to e.g.
  noise or other degradation.

11
Objective

• The objective of image pre-processing may be e.g.
  to remove noise from the image, to sharpen the
  image, to adjust color/gray scale intensities, or
  to highlight e.g. edges or other features that
  can be used in segmentation and pattern
  recognition stages of image analysis.
• Complex image enhancement and analysis tasks are
  difficult even for experts. Hence, a method to
  boost the search for an image processing sequence
  would be advantageous both for uninitiated and
  experts.

12
Applications

• Evolutionary computing or algorithms (EC/EA) have
  be applied to partially automate image
  enhancement, whose output may be subjected to
  visual inspection or act as the input for further
  image analysis and pattern recognition stages.
• Usually, the principle is to combine basic image
  processing operations drawn from a finite set and
  to optimize the relations between the operations
  and the internal parameters of to the operations.

13
Applications

• Interactive image enhancement optimization
  methods have been applied e.g. to magnetic
  resonance (MR) image pseudo-colorization using
  genetic programming.
• It has also been suggested that the user can be
  modeled to decrease the need for human
  intervention.
• Visual image enhancement with a desired output
  image has been studied by Nagao et al.
• the objective was to search for, with a GA, an
  approximation of the transformation sequence
  leading to the given output.
• The desired output can also be defined by
  objective criteria by the user.
• Pre-processing optimization as a part of pattern
  recognition optimization has also been reported
  in the literature
• experiments were done with radar signals.

14
LEDall

• Koljonen et al. (2004) have developed an
  interactive LED lighting system to optimize
  illumination pattern in close range optical
  imaging.
• An I/O board with digital voltage outputs
  controls 90 LEDs that are set around the object
  to be imaged.
• Different lighting patterns can be searched for
  to enhance different features of the image.
• Shadows, illumination levels, etc.

15
(No Transcript)
16
PWM control

• Since cameras (and human eye) have a relative
  long expose time (time resolution),
  pulse-width-modulation (PWM) can be used to
  increase the number of luminance levels of the
  LEDs.
• In LEDall 4 levels are used.
• Totally 490 lighting combinations allowed!
• Q How to optimize? A With GA!

17
Interactive GA

• Initial population of 9 random lighting patters.
• Resulting images shown as a 3x3 grid of images.
• User selects 0-8 images that contain favorable
  features (parents).
• Illuminations of the parents are operated by
  crossover and mutation to create offspring of
  potentially more favorable illumination.
• Occationally, new random offspring are created to
  retain diversity of the population.

18
(No Transcript)
19
(No Transcript)
20
Example
Random lightings
After 3 GA genetations
21
Applications

• LEDall or a similar device can be utilized in
  many places and applications

22
Improvements/project work

• More images, score points?
• New (user controlled?) genetic operators?
• More LEDs (LEDall2, PIC, Toni Harju)?
• Better camera?
• New applications?
• Semi-automatic fitness funcition?
• Deterministic criteria.
• I/O and frame grapper routines exist
• Native Java functions.

23
Questions?