Computer Vision

Computer Vision Fundamentals

• What you Need to Know

  • Image Processing Basics

    • Digital image representation and color spaces
    • Image filtering, enhancement, and transformation
    • Geometric transformations and image alignment
    • Resources:
      • OpenCV Python Tutorial - Comprehensive computer vision library guide
      • Digital Image Processing - Gonzalez - Classic textbook with free resources
      • Computer Vision Basics - Practical computer vision tutorials

  • Feature Detection and Description

    • Edge detection and corner detection algorithms
    • Feature descriptors (SIFT, SURF, ORB)
    • Image matching and object recognition techniques
    • Resources:
      • Feature Detection Tutorial - OpenCV feature detection guide
      • SIFT Algorithm Explained - Scale-Invariant Feature Transform paper
      • Feature Matching Examples - Practical feature matching

Deep Learning for Computer Vision

• What you Need to Know

  • Convolutional Neural Networks (CNNs)

    • CNN architecture and convolution operations
    • Pooling layers and feature map interpretation
    • Popular architectures (LeNet, AlexNet, VGG, ResNet)
    • Resources:
      • CNN Explained - Stanford CS231n CNN guide
      • Deep Learning for Computer Vision - Practical deep learning book
      • CNN Architectures - Visual guide to CNN architectures

  • Transfer Learning and Pre-trained Models

    • Using pre-trained models for image classification
    • Fine-tuning strategies and layer freezing
    • Domain adaptation and feature extraction
    • Resources:
      • Transfer Learning Tutorial - PyTorch transfer learning guide
      • TensorFlow Transfer Learning - TensorFlow implementation
      • Timm Library - PyTorch image models collection

  • Object Detection and Segmentation

    • Object detection algorithms (YOLO, R-CNN, SSD)
    • Instance and semantic segmentation techniques
    • Real-time detection and performance optimization
    • Resources:
      • YOLO Object Detection - You Only Look Once algorithm
      • Detectron2 - Facebook's object detection library
      • Object Detection Tutorial - TensorFlow object detection API

Cloud Vision APIs and Services

• What you Need to Know

  • Google Cloud Vision API

    • Image labeling and object detection
    • Optical Character Recognition (OCR)
    • Face detection and landmark recognition
    • Resources:
      • Cloud Vision API Documentation - Complete API reference and guides
      • Vision API Python Client - Official Python client library
      • Vision API Tutorials - Step-by-step implementation guides

  • AWS Rekognition

    • Image and video analysis capabilities
    • Celebrity and face recognition
    • Content moderation and safety detection
    • Resources:
      • Amazon Rekognition Documentation - AWS computer vision service
      • Rekognition Python SDK - Boto3 Rekognition client
      • Rekognition Examples - Practical implementation examples

  • Azure Computer Vision

    • Image analysis and description generation
    • Read API for text extraction
    • Spatial analysis and custom models
    • Resources:
      • Azure Computer Vision Documentation - Microsoft vision services
      • Computer Vision SDK - Python SDK reference
      • Vision Service Samples - Azure cognitive services examples

Image Classification and Recognition

• What you Need to Know

  • Building Image Classifiers

    • Dataset preparation and augmentation techniques
    • Model training and validation strategies
    • Handling class imbalance and overfitting
    • Resources:
      • Image Classification with PyTorch - Complete classification tutorial
      • Data Augmentation Techniques - Albumentations library for image augmentation
      • Image Classification Best Practices - TensorFlow classification guide

  • Multi-class and Multi-label Classification

    • Handling multiple classes and hierarchical labels
    • Evaluation metrics for complex classification tasks
    • Dealing with large-scale image datasets
    • Resources:
      • Multi-label Classification - Scikit-learn multi-label strategies
      • Hierarchical Classification - Hierarchical labeling approaches
      • Large Scale Image Recognition - ImageNet competition insights

Optical Character Recognition (OCR)

• What you Need to Know

  • Text Detection and Recognition

    • Text detection in natural scenes
    • Character recognition and text extraction
    • Handling different fonts, languages, and orientations
    • Resources:
      • Tesseract OCR - Open-source OCR engine
      • EasyOCR - Ready-to-use OCR with 80+ languages
      • PaddleOCR - Multilingual OCR toolkit

  • Document Processing and Layout Analysis

    • Document structure understanding
    • Table detection and extraction
    • Form processing and information extraction
    • Resources:
      • LayoutLM - Microsoft document understanding model
      • Document AI - Google Cloud document processing
      • Azure Form Recognizer - Microsoft document processing service

Face Recognition and Analysis

• What you Need to Know

  • Face Detection and Landmark Recognition

    • Face detection algorithms and bounding boxes
    • Facial landmark detection and alignment
    • Age, gender, and emotion recognition
    • Resources:
      • OpenCV Face Detection - Haar cascade classifiers
      • MediaPipe Face Detection - Google's face detection solution
      • MTCNN Face Detection - Multi-task CNN for face detection

  • Face Recognition and Verification

    • Face encoding and similarity computation
    • Face verification and identification systems
    • Privacy considerations and ethical implications
    • Resources:
      • Face Recognition Library - Simple face recognition library
      • DeepFace - Lightweight face recognition framework
      • FaceNet Paper - Deep face recognition research

Image Generation and Manipulation

• What you Need to Know

  • Generative Adversarial Networks (GANs)

    • GAN architecture and training dynamics
    • Style transfer and image-to-image translation
    • Conditional generation and controllable synthesis
    • Resources:
      • GAN Tutorial - PyTorch GAN implementation
      • StyleGAN - NVIDIA's high-quality image generation
      • CycleGAN - Unpaired image-to-image translation

  • Diffusion Models and Modern Generation

    • Stable Diffusion and DALL-E integration
    • Text-to-image generation workflows
    • Image editing and inpainting techniques
    • Resources:
      • Diffusers Library - Hugging Face diffusion models
      • Stable Diffusion - Open-source text-to-image model
      • DALL-E API - OpenAI image generation API

Video Processing and Analysis

• What you Need to Know

  • Video Understanding and Analysis

    • Video frame extraction and preprocessing
    • Action recognition and activity detection
    • Video summarization and key frame extraction
    • Resources:
      • OpenCV Video Processing - Video capture and processing
      • PyTorch Video - Video understanding library
      • Video Analysis Techniques - Practical video processing

  • Real-time Video Processing

    • Live video stream processing
    • Real-time object tracking
    • Performance optimization for video applications
    • Resources:
      • Real-time Object Detection - YOLO with OpenCV
      • Video Streaming with Flask - Web-based video streaming
      • WebRTC Integration - Real-time communication protocols

Medical and Scientific Imaging

• What you Need to Know

  • Medical Image Analysis

    • DICOM format handling and visualization
    • Medical image segmentation techniques
    • Radiological image interpretation
    • Resources:
      • SimpleITK - Medical image analysis toolkit
      • PyDicom - DICOM file handling in Python
      • Medical Image Analysis - Medical imaging AI framework

  • Scientific Image Processing

    • Microscopy image analysis
    • Satellite and aerial image processing
    • Scientific visualization techniques
    • Resources:
      • scikit-image - Image processing in Python
      • ImageJ - Scientific image analysis software
      • Satellite Image Analysis - Remote sensing techniques

Performance Optimization and Edge Deployment

• What you Need to Know

  • Model Optimization for Vision Tasks

    • Model quantization and pruning techniques
    • Mobile and edge deployment strategies
    • Hardware acceleration (GPU, TPU, specialized chips)
    • Resources:
      • TensorFlow Lite - Mobile and embedded deployment
      • ONNX Runtime - Cross-platform inference optimization
      • OpenVINO - Intel's optimization toolkit

  • Real-time Processing and Streaming

    • Optimizing inference speed and memory usage
    • Batch processing and pipeline optimization
    • Distributed processing for large-scale applications
    • Resources:
      • TensorRT - NVIDIA GPU optimization
      • Apache Kafka for ML - Streaming data processing
      • Ray for Computer Vision - Distributed computing framework

Ready to Build? Continue to Module 4: AI Application Development to master full-stack AI application development, user interface design, and system integration.