Object Localization In Computer Vision

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Outline:

Chapter 0 Acknowledgement
Chapter 0 Content
1 Introduction
2 Introduction to object localization methods
2.1 Introduction to computer vision
2.2 Challenges
2.3 Low-level image processing
2.3.1 Image Enhancement
2.3.2 Image Restoration
2.4 Medium-Level Image Processing
2.4.1 Image segmentation
2.5 High-Level Image Processing
2.6 Some techniques for object localization
2.6.1 Haar cascade
2.6.2 Histogram of Oriented Gradients
2.7 conclusion
3 Deep learning
3.1 Introduction
3.2 An artificial neuron
3.2.1 Structure
3.2.2 behaviour
3.3 Activation function
3.3.1 The Sigmoid function
3.3.2 Tanh (Tangent Hyperbolic function) function
3.3.3 Rectified Linear Unit ReLU
3.4 Artificial neural network
3.5 Loss function
3.6 Gradient Descent
3.7 Back propagation algorithm
3.8 Regularization.
3.8.1 Early stopping
3.8.2 L2&L1.
3.8.3 Dropout
3.9 Convolutional Neural Network CNN
3.9.1 convolutional layer
3.9.2 Pooling layer
3.9.3 CNN’s architectures LeNet-5
3.10 Conclusion.
4 Contribution
4.1 Introduction
4.2 Data Set
4.3 Development tools
4.3.1 Software tools
4.3.2 Hardware tools
4.4 Building the model
4.4.1 Model selection
4.4.2 Evaluation metric&Loss function
4.5 Development process
4.6 Results and Discussion
4.7 Conclusion.
General conclusion

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