Computer Vision Corporate Training Course

1. Definition and scope of computer vision
   • What is Computer vision?
   • Goals of Computer Vision
   • Applications of Computer Vision
2. Key Milestones in Computer Vision Development
   • Early Work in Computer Vision
   • The Development of Digital Cameras
   • The Rise of Artificial Intelligence and Machine Learning
   • Modern Applications of Computer Vision
3. Applications and Use Cases in Industry
   • Medical Imaging and Diagnosis
   • Robotics and Autonomous Systems
   • Surveillance and Security
   • Self-driving Cars
   • Augmented Reality and Virtual Reality

1. Basic components of a vision system
   • Image acquisition device
   • Image processing unit
   • Feature extraction module
   • Pattern recognition module
   • Output interface
2. Data flow in vision systems
   • Image acquisition
   • Pre-processing
   • Feature extraction
   • Pattern recognition
   • Output generation
3. Hardware and software requirements
4. Hardware requirements
   • Processing power
   • Memory
   • Storage
5. Software requirements
   • Operating system
   • Programming languages
   • Libraries and frameworks

1. Pixel structure and image data types
   • Pixel structure
   • Color representation
   • Image resolution
2. Image data types
   • Integer images
   • Floating-point images
   • Compressed images
3. Color spaces and transformations (RGB, HSV, grayscale)
   • RGB color space
   • HSV color space
   • Grayscale images
   • Color space transformations
4. Image file formats (JPEG, PNG, BMP, TIFF)
   • JPEG format
   • PNG format
   • BMP format
   • TIFF format

1. Image acquisition techniques
   • Cameras
   • Scanners
   • Sensors
2. Pre-processing steps
   • Noise reduction
   • Image enhancement
   • Registration
   • Segmentation
3. Feature extraction and pattern recognition
4. Feature extraction techniques
   • Corners
   • Edges
   • Blobs
5. Pattern recognition techniques
   • Template matching
   • Statistical methods
   • Machine learning techniques

1. The role of convolution in feature detection
   • Convolutional filters
   • Feature maps
2. Types of filters (Sobel, Scharr, Laplacian)
   • Sobel filter
   • Scharr filter
   • Laplacian filter
3. Pooling concepts (max, average)
   • Max pooling
   • Average pooling

1. Sampling theory and aliasing
   • Nyquist theorem
   • Aliasing
2. Spatial and temporal resolution
   • Spatial resolution
   • Temporal resolution
3. Downsampling and upsampling strategies
4. Downsampling strategies
   • Averaging
   • Interpolation
5. Upsampling strategies
   • Nearest neighbor
   • Bilinear interpolation
   • Bicubic interpolation

1. Image enhancement techniques (histogram equalization, normalization)
   • Histogram equalization
   • Normalization
2. Spatial domain filtering
   • Linear filters
   • Non-linear filters
3. Frequency domain filtering (Fourier transform, band-pass filters)
   • Fourier transform
   • Band-pass filters

1. Understanding the structure of a convolutional layer
   • Convolution operation
   • Activation functions
   • Pooling layers
2. Role of non-linearity (activation functions)
   • Sigmoid function
   • Tanh function
   • ReLU function
3. Training CNNs: Loss functions and backpropagation
4. Loss functions
   • Cross-entropy loss
   • Mean squared error
   • Backpropagation

1. Padding techniques and their impact on layer output size
   • Zero padding
   • Same padding
   • Valid padding
2. Detailed pooling strategies (global average pooling, RoI pooling)
   • Global average pooling
   • RoI pooling
3. Advanced filtering and feature map interpretation
   • Dilated convolution
   • Atrous convolutions
   • Feature map visualization

1. LeNet, AlexNet, and VGG
2. LeNet architecture
   • Introduction to LeNet
   • Architecture of LeNet
3. AlexNet architecture
   • Introduction to AlexNet
   • Architecture of AlexNet
4. VGG architecture
   • Introduction to VGG
   • Architecture of VGG
5. GoogLeNet and ResNets
6. GoogLeNet architecture
   • Introduction to GoogLeNet
   • Architecture of GoogLeNet
7. ResNet architecture
   • Introduction to ResNet
   • Architecture of ResNet
8. Modern architectures: EfficientNet and capsule networks
9. EfficientNet architecture
   • Introduction to EfficientNet
   • Architecture of EfficientNet
10. Capsule networks
    • Introduction to capsule networks
    • Applications of capsule networks

1. Concepts and benefits of transfer learning
   • Definition of transfer learning
   • Benefits of transfer learning
2. Source and target tasks in transfer learning
   • Source task
   • Target task
3. Pre-trained models and fine-tuning
   • Pre-trained models
   • Fine-tuning

1. Transfer learning strategies (feature extraction, fine-tuning)

• Feature extraction
• Fine-tuning

2. Managing data imbalance with transfer learning

• Dealing with data imbalance
• Using transfer learning to address data imbalance

3. Transfer learning in different domains (NLP, computer vision, etc.)

• Transfer learning in NLP
• Transfer learning in computer vision
• Transfer learning in other domains