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This project uses Artificial Neural Networks to classify playing cards by their suit and value through image analysis with Relaxation Labeling on color images. It introduces ANN training and classification mechanisms. The study concludes by discussing improvements such as using two ANNs, adjusting learning rates, and dedicating output neurons for each class.
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Pattern Recognition Using Artificial Neural Networks Ace of Hearts Eyal Ittah (60407301) Ittai Doron (53084489)
Introduction • Classify images of playing cards by their suit and value
Pattern Recognition • Sensor – existing images • Feature extraction mechanism – Relaxation Labeling on color images • Classification scheme –Artificial Neural Network used to classify suit and value
Relaxation Labeling • Objects – pixels • Labels – object, background • Initial confidence – degree of white
Artificial Neural Networks • Neurons as basic calculation units • Input layer - Each neuron a single input variable • Hidden layers • Output layer - Each neuron represents a single output variable.
Artificial Neural Networks • Training the network • Supervised learning • Back-propagation • Learning rate
Conclusions • Using two ANNs instead of one • Decreasing the learning rate of the ANN • Using an output neuron for each class