Computer Vision Fundamentals

Study Notes

Computer Vision

Definition: Computer Vision is a subfield of Artificial Intelligence that focuses on enabling computers to interpret and understand visual information from the world.
Applications:
- Image and Object Recognition
- Facial Recognition
- Optical Character Recognition (OCR)
- Image Segmentation
- Object Detection and Tracking
Techniques:
- Convolutional Neural Networks (CNNs)
- Edge Detection
- Feature Extraction
- Image Filtering
Challenges:
- Variability in Lighting and Viewpoint
- Occlusion and Clutter
- Intra-Class Variation
- Limited Training Data

Neural Networks

Definition: Neural Networks are a type of Machine Learning model inspired by the structure and function of the human brain.
Types:
- Feedforward Networks
- Recurrent Neural Networks (RNNs)
- Convolutional Neural Networks (CNNs)
- Autoencoders
Components:
- Artificial Neurons (Nodes)
- Connections (Edges)
- Activation Functions
- Hidden Layers
Training:
- Supervised Learning
- Unsupervised Learning
- Backpropagation Algorithm
- Gradient Descent Optimization

Computer Vision

Computer Vision enables computers to interpret and understand visual information from the world.
Applications of Computer Vision include:
- Recognizing images and objects
- Identifying faces
- Translating images of text into editable text (Optical Character Recognition)
- Dividing images into their constituent parts (Image Segmentation)
- Detecting and tracking objects across images
Techniques used in Computer Vision include:
- Convolutional Neural Networks (CNNs) to analyze images
- Detecting edges in images to identify boundaries
- Extracting relevant features from images
- Filtering images to enhance or remove certain features

Neural Networks

Neural Networks are Machine Learning models inspired by the human brain.
Types of Neural Networks include:
- Feedforward Networks, which process information in one direction
- Recurrent Neural Networks (RNNs), which process sequential data
- Convolutional Neural Networks (CNNs), which analyze images
- Autoencoders, which compress and reconstruct data
Components of Neural Networks include:
- Artificial neurons (nodes) that process information
- Connections (edges) between nodes
- Activation functions that introduce non-linearity
- Hidden layers that allow for complex representations
Neural Networks are trained using:
- Supervised Learning, where labeled data is used to train the model
- Unsupervised Learning, where unlabeled data is used to discover patterns
- The Backpropagation Algorithm, which updates model parameters based on errors
- Gradient Descent Optimization, which minimizes the loss function

Description

Learn about the basics of Computer Vision, a subfield of Artificial Intelligence that enables computers to interpret and understand visual information. Explore applications, techniques, and challenges in the field.