Development of Image Processing Based Feedback Systems for

Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers Adam Hedji Mantas Pulinas Philip San III. Viktorious SSIP 2009, Debrecen, Hungary Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Full Body Motion Detection Project Natal X-Box 360 Microsoft Corporation New project that promises to use two cameras for full-body motion detection Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

CHECKERS! Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Overview of Code Board Plane Geometry Detection Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Overview of Code Board Plane Geometry Detection Segmentation of Individual Game Squares Identification of Square Status by Colour Classification Relaying of Data with Checkers A. I. Feedback This is blue! Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection • Find the corners • 2 approaches were used: • Edge detection and region growing • Hough transforms Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Edge Detection and Region Growing Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Edge Detection and Region Growing • Edge detection with Sobel operator • Dilate the image to fill the gaps in the border Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Edge Detection and Region Growing • Edge detection with Sobel operator • Dilate the image to fill the gaps in the border • Thin the image to get the true border Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Edge Detection and Region Growing • Edge detection with Sobel operator • Dilate the image to fill the gaps in the border • Thin the image to get the true border • Dilate the image several times to remove useless edges Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Edge Detection and Region Growing • Edge detection with Sobel operator • Dilate the image to fill the gaps in the border • Thin the image to get the true border • Dilate the image several times to remove useless edges • Region growing • Determine the corner tiles • Track corners in real-time Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Projective Correction • Edge detection with Sobel operator • Dilate the image to fill the gaps in the border • Thin the image to get the true border • Dilate the image several times to remove useless edges • Region growing • Determine the corner tiles • Track corners in real-time Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Projective Correction Calculate homography using corner coordinates Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Geometry Detection – Hough Transform • Hough transform – We had time so we developed a better solution – Based on Hough transformations – Better real-time line detection Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification How can the computer classify the colours? (32, 61, 105) (171, 154, 158) Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification Use image processing algorithms to make the RGB values only 0 or 255 (0, 0, 255) (255, 255) Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification (255, 255) (255, 0) (0, 0, 255) • Select individual tile • Analyse the predominant colour inside to classify the square state (white, black, blue, yellow) • Sample of pixels used as opposed to whole square Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification • How to determine if each R, G and B values are 0 or 255? -Need to choose threshold value e. g. A given pixel of value (15, 19, 250) A threshold of 126 Output is (0, 0, 255) Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification - But how to choose threshold? • Webcams can be of relatively poor quality and provide poor contrast. – For example, blue pieces used were relatively hard to distinguish from the black tiles. Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

But how to choose threshold? • Can instead normalise the image and use a threshold of 127, given by (256/2)-1. – Select a white tile and take an average of the colour values. – Do the same for a black tile. – Use these averages to normalise the image p 1=(241, 209, 210) p 2=(232, 204, 214) p 3=(240, 211, 205) Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification - Normalisation Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification - Normalisation • Then reduce the image to absolute values of 0 and 255 • Use a threshold of 126 (half of full intensity value) Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Colour Classification - Normalisation Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Summary • Main results – Successfully used 2 approaches to chess board detection • Edge detection and region growing • Hough transform – Removing perspective distortion – Identification of individual tiles and pieces, including classification – Connection to engine interface with feedback system Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Improvements • Aim towards real-time 60 fps processing • Use a more efficient programming language such as C++ • Use of GPU using CUDA or Open CL programming language • More complex algorithms – Motion detection of hand • Use of overlay of 3 D structures onto camera image. • Virtual humans. . . Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

Unused Virtual Human Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers

The Team Adam Hedji University of Zagreb, Croatia adam. hedi@fer. hr Mantas Paulinas Vilnius Gediminas Technical University, Lithuania mantas. paulinas@el. vgut. lt Philip San University College London, England p. san@ucl. ac. uk Viktor Blaskovics University of Szeged, Hungary blaskovics. viktor@stud. u-szeged. hu Development of Image Processing Based Feedback Systems for Interactive Gaming Using Non-Traditional Controllers