OBTAIN CHESS MOVES WITH IMAGE PROCESSING

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1 INTERNATIONAL SCIENTIFIC CONFERENCE November 2013, GABROVO OBTAIN CHESS MOVES WITH IMAGE PROCESSING Hilmi KUŞÇU Mustafa ARDA Emir ÖZTÜRK Abstract Over the years, with the development of the artificial intelligence, chess became a game that is experienced a struggle between human and artificial intelligence. Computers could detect and make counter moves to opponent's move. At this point, chess moves detection has been have great importance. Also, chess games notation is important for later analyzes. In this study, image processing method was used to detect chess moves in a chess match. Image processing is carried out with the help of MATLAB Software. Keywords: Chess Notation, Image Processing, MATLAB. INTRODUCTION In 1996, computer program Deep Blue, which was built by the IBM Corporation, won against the World Champion chess player Garry Kasparov, it became a huge event. Defeat of the Human intelligence in front of the artificial intelligence caused to asking of some questions like: Human intelligence can be beaten by production of a human intelligence? In 1968, David Levy claimed that a computer have couldn t win himself in chess match for the coming 10 years. In 1978, he defeated the most powerful computer opponent in chess, but he admitted that this situation continued no longer. In 1996, first victory of the artificial intelligence gained by the IBM s computer Deep Blue. The world champion chess player Garry Kasparov defeated by Deep Blue in two matches, but final score was 4-2 in favor of Kasparov. One year later, Deep Blue won the rematch against Kasparov. When choosing chess moves, Human and artificial intelligent have different ways. Humans can calculate next 50 moves maximumly. However, computers can calculate millions of chess moves in the same situation. Fig. 1. Deep Blue vs Kasparov Chess Game Invention of the computer was a milestone in chess playing machine history. Alan Turing, who is founder of the computer science, developed an algorithm that computer could prefer chess moves according to opponents chess moves. Detecting a chess move is important as deciding a chess move. Before decide a chess move, a computer must detect the opponents move. There are different methods for detecting of chess moves. In this study, chess moves detected with image processing techniques by using MATLAB Program.

2 EXPOSITION The Experimental setup is shown in Figure 2. We used a chess board which has 50cm x 52cm dimensions, becomes red and white squares and also made from the plastic material. Chess pieces are selected black and white colours and sticked black and white tapes around the them to obtain contrast difference in webcam. In this study, we got the snapshots in 640x480 pixel resolution. Thanks to higher resolution images, we can use more detail pictures and analysis more data. Fig. 3. Webcam Chess board consists 64 squares and when look at the whole picture, it looks like a square. We must crop the unuseful part of taking snapshot images, because they are in rectangular form. For this purpose, we use the ROI(Region of Interest) position command which belongs to Image Acquasition Toolbox. Fig. 2. Experimental Setup To perceive the chess moves, we used a webcam which has 640x480 pixel resolution. Webcam can seen in Figure 3. The connection of the webcam with computer is established on USB Port. Webcam is positioned on a wooden bracket which has 103 cm length. Captured images from the webcam is analyzed in notebook computer with the help of MATLAB Program. Image acquisition process is established with the MATLAB Image Acquisition Toolbox. Using this toolbox, we can adjust the resolution, colour scale, crop and trigger of snapshot of the taking image. Fig. 4. Cropped Snapshot Image There are 3 types of taking snapshot images colour scale: Grayscale, RGB, YCbCr. Image Acquaisition Toolbox defines the taking snapshot images as a matrix in MATLAB Workspace. This matrix has a 3 layer in RGB and YCbCr format and 1 layer in Grayscale format. In Grayscale format, every pixel has a colour value between the value means black and white. Thus, taking snapshot images converted to the digital variables. In this study, we used the Grayscale color format.

3 At this point, colors of chess board and chess piecess have a really important role. To detect chess board square and chess pieces is very difficult when has same colour in grayscale. For this reason, choosing chess squares and chess pieces different colors gives advantage to us. After the crop process, snapshot images are not in square matrix form. Because of that snapshot image matrix is resized to 480x480 pixel with imresize command. Then, snapshot image matrix is divided 64 equal square matrix, which are consisted 60x60 square matrix, with mat2cell command. Fig. 6. After "canny" Filtering of Snapshot Image Figure 7 is shown the canny values in Figure 6 snapshot image. Fig. 7. canny Values of Snapshot Image Fig. 5. Cropped Snapshot Image in Grayscale Format Every one of the 64 sub-matrix element s arithmatic mean is calculated and in this way, snapshot image matrix is reduced from 480x480 elements to 8x8 elements. Every element in 8x8 reduced matrix correspond to one of the chess board s 64 squares. To determine the which chess square has a chess pieces, canny command will be used. When canny command is used, transition between black and white colors are occured a line. Aim of using black and white stripes on the chess pieces is helped detecting of them on the chess board. canny value must be between 0-1. It describes the precision of line detection. In this study, canny treshold value is accepted as 0,3. After the canny filtering, snapshot image is seen in Figure 6. White lines density treshold values in reduced 8x8 matrix s is accepted as 0,06 according to lighting and environmental condition. After the reducing process, matrix form shown in Figure 8. Fig. 8. Reduced Form of Snapshot Image Matrix Perception of chess moves obtain difference between repeatedly taking 2 snapshot image matrix. Before and after chess move, difference between the taking snapshot image matrix results a difference matrix. In difference matrix, -1 element describes the chess move begining point, 1 element describes the chess move ending point. In Figure 9, a chess move is shown which is a pawn move forward 2 square.

4 Fig. 9. Difference Matrix of First Move Recognizing of the chess pieces are obtain by the algorithm. Before the chess match began, we knew all the chess pieces starting position. Every chess pieces identfy by numbers from 1 to 32. In Figure 10, game matrix is shown. When chess move is made, the movement in the difference matrix is also happening in the game matrix. Change in the game matrix stored in the MATLAB / Workspace and updated in every chess move. Fig. 11. Chessboard Notation According to Figure 11, a notation matrix is established and shown in Figure 12. Fig. 12. Notation Matrix Fig. 10. Game Matrix In chess notation, chess pieces named with first letter as a capital letter. Pawns have not letter. Also a letter matrix is created and all the capital letters of chess pieces are stored in there. In this way, we can use letter matrix instead of numbers from the game matrix in chess notation. Here is the letter matrix is shown; Letter Matrix = {'R';'K';'B';'K';'Q';'B';'K';'R';'';'';'';'';'';'';'';'';'';'';'';''; '';'';'';'';'R';'K';'B';'Q';'K';'B';'K';'R'}; In chess notation, columns named letters from a to h, rows named numbers from 1 to 8. Chessboard notation is shown in Figure 11. GUI (Graphical User Interface), is a subprogram of MATLAB program which contains commands and functions are generated in MATLAB. GUI provide user-friendly interface. From the beginning to this point, all image process is summed up in GUI interface. When push the Start button, program will start the webcam and when push the Stop button, program will stop the webcam. After the white chess pieces are played, White Move button must be pushed and the black chess pieces are played, Black Move button must be pushed. These buttons take snapshot images from the webcam after chess moves are made. There are two listbox on the right of display. When white chess pieces are played, chess notation is seen in listbox which White written on the top of the listbox. Samely, when black chess pieces are played, chess notation is seen in listbox which Black written on the top of the listbox. Generating GUI for this study is seen in Figure 13.

5 [5] Timothée COUR, Autonomous Chess- Playing Robot, Ecole Polytechnique, Page 1-27, Fig. 13. Image of the Generating GUI CONCLUSION. [6] G.D.Illeperuma, Using Image Processing Techniques to Automate Chess Game Recording, Proceedings of the Technical Sessions Institute of Physics Sri Lanka, No: 27, Page 76-83, [7] Şükrü Ozan, A Case Study on Logging Visual Activities: Chess Game, Master Thesis, İzmir Yüksek Teknoloji Enstitüsü, In this study, obtaining of the chess piece s move notation with image processing techniques is aimed and realized successfully. Using MATLAB Program with this purpose gives us rapid outcomes in image acquisition and image processing steps. Light intensity of the environment has an influence on the recognizing of the chess pieces. So that, changing of light intensity directly affect the program. Because of that, after the experimental setup position change, the ROI Position and canny value is had to regulate. REFERENCE [1] Mustafa ARDA, Görüntü İşleme Yöntemiyle Satranç Hamlelerinin Yazılması, Doctorate Seminar, Trakya Üniversitesi Fen Bilimleri Enstitüsü, Edirne, [2] , Access Date: [3] Franciscus C. A. Groen, A Chess-Playing Robot: Lab Course in Robot Sensor Integration, IEEE Transactions on Instrumentation and Measurement, Vol. 41, No: 6, Page , [4] Emir Sokic, Simple Computer Vision System for Chess Playing Robot Manipulator as a Project-based Learning Example, IEEE, Page 75-79, 2008.