Classification of banknote fitness by artificial neural networks

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Glossary of Abbreviations and Acronyms
General Introduction
Chapter I Artificial vision
Introduction
1. Digital image
2. Digital Image types
2.1. Raster image
2.2. Vector image
3. image characteristics
3.1 Pixel
3.2. Color of image
3.2.1. Typeof color Image. Binary Images Grayscale images
Color images
3.3. Histograms
3.4. Resolution
3.5. Noise
3.6. Luminance
3.7. Contour
3.8. The contrast
3.9. Dimension
4. Digital image processing
4.1. Fundamental Steps of Digital Image Processing
4.1.1. Image Acquisition
4.1.2. Image Enhancement
4.1.3. Image Restoration
4.1.4. Color Image Processing
4.1.5. Image Compression
4.1.6. Morphological Image Processing
4.1.7. Segmentation
4.1.8. Representation and Description
4.1.9. Object recognition
4. Computer vision
5. Image processing Vs Computer Vision
Conclusion
Introduction
1. Artificial Intelligence
2. Machine Learning
Chapter II Artificial Intelligence
2.1. Types of Systems of Machine Learning
2.1.1. Supervised learning
2.1.1.1. Type of supervised machine learning algorithms
2.1.1.2. Common supervised Learning Algorithms
2.1.2. Unsupervised Learning
2.1.2.1. Type of supervised machine learning algorithms
2.1.2.2. Common unsupervised Learning Algorithms
3. Deep learning
4. Artificial Neural Networks
4.1. The biological neuron
4.2. The Perceptron model
4.3. Component of artificial neural network
4.3.1. Input layer
4.3.1. Hidden layers
4.3.2. Output layers
4.3.3. Weight
4.3.4. Biases
4.3.5. Activation Functions
4.3.6. Cost function
4.3.7. Hyperparameters
4.3.8. Optimizers
4.3.9. ANN Models
4.3.9.1. Single Layer Perceptron Model (SLP)
4.3.9.2. Multilayer Perceptron Model (MLP)
4.3.10. Type of ANN
4.3.10.1. Recurrent Neural Networks (RNNs)
4.3.10.2. Convolution Neural Network (CNNs)
4.3.10.2.1. Types of layers in a convolutional network
4.3.10.2.2 Convolutional Neural Network Models
4.3.10.2.3. Transfer Learning
Conclusion
Chapter III State of Art
Introduction
Related work
Tuyen, Dat Tien, Wan, Sung Ho and Kang Ryoung papers
First study
Second study
Third study
Comparison between proposed methods
Banknote counting machine
Nancy and Halimah
Weizhong, Zhenyu and Ai Gu paper
Nishant and Manish Paper
Conclusion
Introduction
Chapter IV Experiments and realization
1. Tools and Libraries
1.1.To Anaconda
1.1. Python
1.2. TensorFlow
1.3. Anaconda
1.4. Jupyter notebook
1.5. Google Colab
1.6. Keras
1.7. Flask
2. Experiments
2.1. Our datasets
2.2. Architecture of our network
2.2.1. Model 1
2.2.2. Model 2
2.2.2. Model 3
2.3. Implementation
3. Result and discussion
3.1. Results obtained for model 1
3.2. Results obtained for model 2
3.3. Results obtained for model 3
3.4. Results comparison table
Conclusion
General Conclusion
Bibliography

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