One of the growing fields in computer science is that of Artificial Intelligence or AI. Many theories have evolved to make a computer intelligent and so far no one has succeeded (Dreyfus 1992). One of the methods used by the Shelley Project in the past has been to use a back propagation neural network that is the backbone of the GNU Neural Network Visualizer (GNNV). GNNV uses a neural network to try to identify known objects, like faces, in the field of view. A different method, that is the focus of this research, is to identify objects in the image. These objects could be squares, circles or even blobs. Neural networks can work through changes in environment without changing the code provided appropriate training. However it is tough to know what the neural network is actually learning. One advantage this research has over neural networks is that as the programmer you know exactly what it knows. Instead, the problems are of the form, "How do I tell it what a circle is?" or "How do I have it determine what is noise that should be ignored?" The goal of this project is to create a program capable of taking in an image from a digital camera and identifying the tic-tactoe game. This is inspired from past work done for the Shelley Project which included playing tic-tac-toe (without the vision component) and the Shelley Integrated Environment (SIB). Various problems arose during implementation. The majority of these were system and API related. For others like the perspective correction and game detection, stepping away from the computer with pencil and paper in hand was invaluable. Through it all the goal of playing a game of tic-tac-toe against Shelley has finally become a reality.
Zalokar '00, Michael, "Computer Vision: Object Recognition" (2000). Honors Projects. 10.