# Hou

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```					Chessmen Position Recognition Using
Artificial Neural Networks

Jun Hou
Dec. 8, 2003.
Scenario
  Augmented Reality chess                      Hidden pieces
game – detect the position
of all black chess pieces
 Problem – moving camera,
hidden pieces
Constraints: 1-2 piece
moves each time;
Initial position known

Problem reduced to:
whether there is a piece on
a given square
Fig 1. Illustration of hidden pieces
synthesized images, train
ANN, test recognition rate
Feature Selection
   Normalized chessboard squares
In 2D: find region of interest – the chessboard,
calculate 3D positions.
In 3D: divide each chessboard square into m*m
smaller squares (64*m*m)
Map 3D square positions onto 2D

Use average of each square as input

   Camera angles and chessboard square positions
   To compensate for the black ratio difference

Prior probability of each square occupied
by a chess piece
Neural Network Design

ANN applied:
m*m                Square       Camera       Prior
(2) Gradient Descent   Image data         position     parameters   probability

with variable
of     the
m*m input          2 input      6 input      square
learning rate;                                                  1 input

with momentum                   Fig 2. Neural Network Design
Experimental Results – Data
generation and preprocessing
Use Blender Version 2.30 to model the pieces and
use Python scripts to generate the synthesized
images
Change the chessboard state – play one random move
each time, and observe the chessboard from different
viewpoints
Image properties – clean, high contrast, little noise, no
distortion, no lighting variation.
Generate 1500 images with 640*480 resolution

Data filtering

Use threshold to select the squares that have at least a
portion of black, exclude completely blank squares for
training
Experimental Results – Data
generation and preprocessing (cont.)
(a)                                             (b)

Fig.3 (a) Original synthesized 640 * 480 3D image and (b) converted 64*64 2D
image. Only none white squares are used for training. Trying to restore the 3D
image to 2D image. The 2D chessboard looks as if rotated 45.
Experimental Results – Train ANN

Fig.4 Training using GD with         Fig.5 Training using GD
variable learning rate               with momentum

Recognition rate: 72%, GD with variable learning rate 1.05
No significant recognition rate difference between the GD, GD-VLR, GD-M
GD-VLR and GD-M are faster than GD
Experimental Results
The more training samples,
the more similar are the
training and test sets
As the number of training
samples increases, the
training set accuracy and
test set accuracy merge
Fig.6 Training set accuracy and test
set accuracy. GD with variable
learning rate
Conclusion and Future Work
   Conclusion
Variations of Feed Forward ANNs are used to
recognize the positions of chess pieces.

   Future work
Size of square divisions, effect of randomizing the training
data, learning rate, momentum, etc.
Use Confidence measure
Use constraints to help recognition
Look at successive frames to find out the piece moved;
use frames of different viewpoints for one chessboard state,
etc.
Thank you! *^_^*

```
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 views: 3 posted: 4/27/2011 language: English pages: 10