Chess Segmentation

by Statis Grigoropoulos

© 2012

Chess Segmentation

• The Problem• Ways of Approach• Implementation• Results• Suggestions for Improvement• Conclusion

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The Problem

• Detect chessboard segmentation in an image

• classify the chess pieces found in the segmented chessboard.

• provide a correct FEN notation for the detected chessboard(s)

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The Algorithm• Step 1: Find internal borders of the

chessboard(s)( i.e detect chessboard)

• Step 2: Spit the squares for each chessboard

• Step 3: Detect the piece inside the square

• Step 4: Produce FEN notation

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Step 1: Find the Borders• Ways of Approach:

-Hough Transform-Norm. Cross Correlation

Template :

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Step 1: Find the Borders

• Hough Transform :MATLAB(single case)

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Step 1: Find the Borders

• Hough Transform :MATLAB(real case)

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Step 1: Find the Borders• Norm. Cross Correlation :MATLAB

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Step 2: Splitting the Squares• Step 1: Compute the histogram of the

chessboard• Step 2: Detect the dominant values of

the histogram• Step 3: Classify the dominant values

with a confidence interval as the white and

• black square background• Step 4: Iterate on the x and y axis and

catch the interchange of the values

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Step 2: Splitting the Squares

Confidence interval = 10%= 25

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Step 3: Classify Square• Empty Squares-Standard Deviation

A way to measure the distance of values from the mean

Expected low value of standard deviation in empty squares : s.d. < 25(10%)??

-Histogram bins© 2012Chess SegmentationPage 11

Step 3: Classify Square

s.d. =~ 20

s.d. =~ 11

s.d. =~ 30(!)

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Step 3: Classify Square• Square containing a Piece

- Rich Feature Space- Simple Mean Classifier The Method:

Shrink-to-fit Compute Characteristics

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Step 3: Classify Square• Square Containing a Piece -The Features:1. Normalized Histogram (dropped)2. Compute the ratio of dark pixels in

square(1)3. Spit the Square in a 3x3 grid4. Compute the relative ratio of dark

pixels in each region(9)

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Step 3: Classify Square• Square Containing a Piece -The Features(cont.):5. Spit the square in a 5x5 grid 6. Compute the Relative Position of the

border of the shape (chess piece) along the lines of the grid, in each direction(20)

7. Compute the relative ratio of dark pixels along each line of the grid(10)

Result : 40-dimensional feature space© 2012Chess SegmentationPage 15

Step 3: Classify Square• Square Containing a Piece

Shape Context

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Some Results

Accuracy = 100%

Accuracy 55%

Accuracy 80%

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Towards a complete Program• Minimize assumptions• Implement Step 1• Refine Spitting Squares Method• Extend Feature Space( go Log

Polar?)• Add more training sets• Optimize Code and Algorithms• Test, Test, Test…

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Conclusion• Chess Detection and Segmentation

a very hard problem in a general case

• We managed to solve a small part of it

• Each Step poses challenges

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Thank you!

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Questions?

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Classify that! :p

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