Puzzle Image Processing

1
Puzzle Image Processing

• Sam Bair
• (Group Leader)
• Nick Halliday
• Nathan Malkin
• Joe Wang

2
Scramble Squares

• The object of the puzzle is to arrange the nine
  pieces into a square so that the graphics on the
  pieces' edges match perfectly to form a completed
  design in every direction.

Scramble Squares are made by b.dazzle, inc.
3
Our Goal

• Goal
• Create program(s) that use 9 pre-scanned image
  files to produce a solution to the puzzle
• Use the solution to create a visualization of the
  work accomplished by the project

4
Resources

• Engine
• MATLAB programming language

The MathWorks is the creator of MATLAB and a
sponsor of SI

• Computers
• The program ran primarily on OSCs Pentium 4
  Cluster
• Four gigabytes of RAM
• Two 2.4 GHz Intel P4 Xeon processors

5
Structure of the Programs

• Our groups programs
• Processed image files to determine unique objects
• Matched the images to find a solution to the
  puzzle
• Created a visualization displaying an animation
  of the algorithm and the results it produced

6
Integrating the Programs
Image Processing
Visualization
Output To User
7
Two Interfaces

• Program allows for 2 ways of input

Images

• Automatic
• Program detects unique objects and categorizes
  them

• Manual
• User categorizes the edges

Solve the puzzle
Image Processing
8
Two Interfaces (Contd.)

• Both input methods use a system of upper/lower
  case letters assigned to each unique object

9
Image Processing

• Purpose to match two edges of two puzzle pieces
• Edge detection used in comparison
• Black and white pixel matching

10
Image Processing (Contd.)

• Outputs a matching percentage 100 is perfectly
  matching, 0 is no matching at all
• Difficulties image quality interferes with
  recognition, preventing accurate results

11
Matching Algorithm

• 9x4 matrix holds letters assigned to unique
  objects
• Program checks 1269 combinations of sides and
  stores the information (matches/doesnt match) in
  a 4-dimensional matrix

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Matching Algorithm / Visualization

• Algorithm
• Picks starting piece and side (e.g. 1,1)
• Selects succession of pieces and checks
  appropriate matches on the go
• The possible combinations are tried in turn
• Some combinations are skipped as soon it is
  determined that they cannot yield a solution

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Visualization

• Takes solution file produced by the main program
  and loads images ..
• Simulates the algorithm used by the main program
  to show the steps it went through
• Images are moved into the position dictated by
  the simulation (see above)
• Movement is saved as many image files
• All the image files are then combined into a
  movie/animation created by a MATLAB program

14
The Results

• Our group solved the puzzle by combining the
  effort of humans and computers.
• While the fully automated program was not
  completed, an understanding of the concepts its
  development required was reached.
• More time spent on the project would have likely
  yielded more and better results.

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Conclusion

• Can you solve my puzzle?
• Technical limitations prevented our programs from
  producing final solutions
• If we had more time, we would
• Optimize image processing code for speed and
  efficiency
• Develop a better image matching algorithm as
  explained in 5/6
• Improve interaction with user
• Develop more complex visualizations
• MAKE IT WORK!

16

• Our group would like to thank everyone who helped
  us with our project, including
• Sam Bair
• b.dazzle, inc
• The staff of OSC
• Alan Chalker

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