Chapter 1: Introduction to Computer Vision
- Lesson 1: What is Computer Vision?
- Lesson 2: Applications and Use Cases of Computer Vision
- Lesson 3: Evolution of Computer Vision as a Field
- Lesson 4: Overview of Key Concepts (Images, Pixels, etc.)
- Lesson 5: Course Structure and Goals
Chapter 2: Fundamentals of Digital Imaging
- Lesson 1: Image Formation and Representation
- Lesson 2: Color Spaces (RGB, Grayscale, HSV, etc.)
- Lesson 3: Image Resolution, Size, and Aspect Ratio
- Lesson 4: Image Formats and Compression (JPEG, PNG, etc.)
- Lesson 5: Introduction to Image Metadata
Chapter 3: Image Processing Basics
- Lesson 1: Introduction to Image Filters
- Lesson 2: Convolution and Kernels
- Lesson 3: Edge Detection (Sobel, Canny, etc.)
- Lesson 4: Thresholding Techniques
- Lesson 5: Noise Reduction (Gaussian Blur, Median Filter)
Chapter 4: Geometric Transformations
- Lesson 1: Image Translation and Rotation
- Lesson 2: Scaling and Resizing
- Lesson 3: Affine Transformations
- Lesson 4: Perspective Transformations
- Lesson 5: Applications of Transformations
Chapter 5: Feature Detection and Description
- Lesson 1: Understanding Features in Images
- Lesson 2: Corner Detection (Harris, Shi-Tomasi)
- Lesson 3: SIFT, SURF, and ORB Descriptors
- Lesson 4: Matching Features Across Images
- Lesson 5: Applications in Object Tracking
Chapter 6: Image Segmentation
- Lesson 1: Introduction to Segmentation
- Lesson 2: Threshold-Based Segmentation
- Lesson 3: Region-Based Segmentation
- Lesson 4: Edge-Based Segmentation
- Lesson 5: Watershed Algorithm
Chapter 7: Object Detection Basics
- Lesson 1: What is Object Detection?
- Lesson 2: Sliding Window Method
- Lesson 3: Image Pyramids
- Lesson 4: Non-Maximum Suppression
- Lesson 5: Evaluation Metrics (Precision, Recall, IoU)
Chapter 8: Image Classification Basics
- Lesson 1: What is Image Classification?
- Lesson 2: Overview of Traditional Methods (KNN, SVM)
- Lesson 3: Introduction to Deep Learning-Based Classification
- Lesson 4: Datasets for Classification (MNIST, CIFAR-10)
- Lesson 5: Performance Evaluation Metrics
Chapter 9: Video Processing Basics
- Lesson 1: Reading and Writing Videos
- Lesson 2: Frame-by-Frame Analysis
- Lesson 3: Background Subtraction Techniques
- Lesson 4: Object Tracking Basics
- Lesson 5: Motion Detection
Chapter 10: Image Filtering in Computer Vision
- Lesson 1: Introduction to Image Filtering
- Lesson 2: Linear Filters
- Lesson 3: Non-Linear Filters
- Lesson 4: Frequency Domain Filtering
- Lesson 5: Applications and Case Studies
Chapter 11: Histograms in Computer Vision
- Lesson 1: Understanding Histograms
- Lesson 2: Histogram Equalization
- Lesson 3: Histogram Matching
- Lesson 4: Practical Applications with Libraries
- Lesson 5: Project-Based Learning
Chapter 12: Basic Supervised Learning Techniques for Computer Vision
- Lesson 1: Introduction to Supervised Learning in Computer Vision
- Lesson 2: Common Algorithms for Vision Tasks
- Lesson 3: Feature Engineering in Vision Tasks
- Lesson 4: Training and Evaluating Models
- Lesson 5: Hands-On Projects
Chapter 13: Working with OpenCV Library in Python
- Lesson 1: Introduction to OpenCV
- Lesson 2: Image Basics: Reading, Writing, and Manipulation
- Lesson 3: Drawing and Annotating on Images
- Lesson 4: Image Transformation Techniques
- Lesson 5: Image Filtering and Smoothing
- Lesson 6: Edge Detection and Contour Detection
- Lesson 7: Thresholding and Binarization
- Lesson 8: Working with Videos and Real-Time Processing
- Lesson 9: Feature Detection and Matching
- Lesson 10: Object Detection and Face Recognition
Chapter 14: Working with OpenCV Library in C++
- Lesson 1: Setting Up OpenCV for C++ Development
- Lesson 2: Core Functionalities of OpenCV in C++
- Lesson 3: Advanced Image Processing Techniques in C++
- Lesson 4: Feature Detection and Tracking in C++
- Lesson 5: Building Applications with OpenCV in C++
Chapter 15: Working with OpenCV in Java
- Lesson 1: Setting Up OpenCV for Java Development
- Lesson 2: Core Functionalities of OpenCV in Java
- Lesson 3: Image Processing Techniques in Java
- Lesson 4: Real-Time Video Processing in Java
- Lesson 5: Developing Projects with OpenCV in Java
Chapter 16: Image Processing and Computer Vision with MATLAB
- Lesson 1: Introduction to MATLAB for Image Processing and Computer Vision
- Lesson 2: Image Preprocessing Techniques
- Lesson 3: Feature Detection and Extraction
- Lesson 4: Image Segmentation
- Lesson 5: Object Detection and Recognition
- Lesson 6: Image Transformation and Registration
- Lesson 7: Video Processing with MATLAB
- Lesson 8: 3D Vision and Depth Estimation
- Lesson 9: Advanced Applications of Computer Vision in MATLAB
- Lesson 10: Projects and Case Studies
Chapter 1: Advanced Image Processing Techniques
- Lesson 1: Fourier Transform in Image Processing
- Lesson 2: Wavelet Transform
- Lesson 3: Image Inpainting
- Lesson 4: Super-Resolution Techniques
- Lesson 5: Advanced Image Denoising
Chapter 2: Convolutional Neural Networks (CNNs) for Image Classification
- Lesson 1: Introduction to Convolutional Neural Networks
- Lesson 2: The Convolution Operation and Filters
- Lesson 3: Pooling and Architecture of CNNs
- Lesson 4: CNN Architectures (LeNet, AlexNet, VGG, ResNet)
- Lesson 5: Training CNNs for Image Classification
- Lesson 6: Transfer Learning with CNNs
- Lesson 7: Evaluating and Deploying Image Classification Models
- Lesson 8: Applications of CNNs in Real-World Classification Tasks
Chapter 3: Vision Transformers (ViTs)
- Lesson 1: Introduction to Vision Transformers
- Lesson 2: Architecture of ViTs
- Lesson 3: Training Vision Transformers
- Lesson 4: Applications of ViTs in Object Detection
- Lesson 5: Comparison of ViTs with CNNs
Chapter 4: Generative AI in Computer Vision
- Lesson 1: Introduction to Generative AI
- Lesson 2: GANs (Generative Adversarial Networks)
- Lesson 3: Variational Autoencoders (VAEs)
- Lesson 4: Image-to-Image Translation
- Lesson 5: Style Transfer
Chapter 5: 3D Computer Vision
- Lesson 1: Basics of 3D Vision
- Lesson 2: Point Clouds and 3D Reconstruction
- Lesson 3: Stereo Vision Techniques
- Lesson 4: Structure from Motion (SfM)
- Lesson 5: Applications in AR/VR
Chapter 6: Multimodal AI Integration
- Lesson 1: Understanding Multimodal AI
- Lesson 2: Combining Vision with NLP (Text and Image)
- Lesson 3: Multimodal Learning Architectures
- Lesson 4: Applications in Real-Time Systems
- Lesson 5: Challenges and Future Directions
Chapter 7: Augmented Reality (AR) for Computer Vision
- Lesson 1: Introduction to Augmented Reality and Computer Vision
- Lesson 2: Basics of AR Hardware and Software
- Lesson 3: Marker-Based AR
- Lesson 4: Markerless AR
- Lesson 5: Object Recognition and AR
- Lesson 6: Image and Video Processing for AR
- Lesson 7: 3D Pose Estimation and AR
- Lesson 8: Hands Tracking and Gesture Recognition for AR
- Lesson 9: Building AR Applications with OpenCV
- Lesson 10: Advanced Topics and AR Projects
Chapter 8: Working with Keras and TensorFlow for Computer Vision
- Lesson 1: Introduction to Keras and TensorFlow for Computer Vision
- Lesson 2: Loading and Preprocessing Image Data
- Lesson 3: Building a Simple Image Classifier
- Lesson 4: Transfer Learning with Pre-Trained Models
- Lesson 5: Object Detection with TensorFlow
- Lesson 6: Image Segmentation with TensorFlow
- Lesson 7: Visualizing Model Performance
- Lesson 8: Working with GANs for Image Generation
- Lesson 9: Real-Time Applications with TensorFlow
- Lesson 10: Hands-On Projects
Chapter 9: Working with PyTorch for Computer Vision
- Lesson 1: Introduction to PyTorch for Computer Vision
- Lesson 2: Loading and Preprocessing Image Data
- Lesson 3: Building a Simple CNN Model
- Lesson 4: Transfer Learning with PyTorch
- Lesson 5: Image Classification with PyTorch
- Lesson 6: Object Detection Using PyTorch
- Lesson 7: Image Segmentation with PyTorch
- Lesson 8: Visualizing Models and Results
- Lesson 9: Deploying PyTorch Models
- Lesson 10: Hands-On Projects
Chapter 10: Edge AI for Computer Vision
- Lesson 1: Introduction to Edge AI Devices
- Lesson 2: Optimization for Low-Power Devices
- Lesson 3: Applications of Edge AI (Surveillance, Drones)
- Lesson 4: Edge AI vs Cloud AI
- Lesson 5: Challenges in Edge Deployment
Chapter 11: Ethical Considerations in AI for Vision
- Lesson 1: Ethical Challenges in Vision AI
- Lesson 2: Bias in Datasets and Models
- Lesson 3: Privacy Concerns in Computer Vision
- Lesson 4: Responsible Deployment of Vision Systems
- Lesson 5: Case Studies in Ethical Vision AI
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