👁️ Computer Vision

Master the art and science of teaching machines to see and interpret visual information

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Computer Vision Curriculum

12
Specialized Units
~100
Vision Concepts
30+
Algorithms & Models
45+
Practical Projects
1

Image Fundamentals

Learn the basics of digital images, pixel representation, and fundamental image operations.

  • Digital image representation
  • Color spaces and models
  • Image file formats
  • Pixel operations
  • Spatial and frequency domains
  • Image quality metrics
  • Noise and artifacts
  • Image acquisition systems
2

Image Processing Techniques

Master essential image processing operations including filtering, enhancement, and transformations.

  • Linear and nonlinear filtering
  • Convolution operations
  • Edge detection algorithms
  • Morphological operations
  • Histogram processing
  • Image enhancement
  • Geometric transformations
  • Multi-scale processing
3

Feature Detection and Description

Explore algorithms for detecting and describing distinctive features in images.

  • Corner detection
  • Blob detection
  • Scale-invariant features
  • SIFT and SURF
  • ORB and FAST
  • Feature descriptors
  • Feature matching
  • Keypoint evaluation
4

Image Classification

Build systems to categorize images into predefined classes using traditional and deep learning methods.

  • Traditional classification pipelines
  • Bag of visual words
  • Convolutional Neural Networks
  • Popular CNN architectures
  • Transfer learning
  • Data augmentation
  • Multi-class vs multi-label
  • Performance evaluation
5

Object Detection

Learn to locate and identify multiple objects within images using state-of-the-art detection frameworks.

  • Sliding window approaches
  • Two-stage detectors
  • One-stage detectors
  • YOLO family
  • R-CNN variants
  • Anchor-based vs anchor-free
  • Non-maximum suppression
  • Evaluation metrics
6

Image Segmentation

Partition images into meaningful regions and segments using classical and deep learning approaches.

  • Thresholding techniques
  • Region growing
  • Watershed algorithm
  • Graph-based segmentation
  • Semantic segmentation
  • Instance segmentation
  • Panoptic segmentation
  • Medical image segmentation
7

Facial Recognition and Analysis

Develop systems for face detection, recognition, and facial attribute analysis.

  • Face detection algorithms
  • Facial landmark detection
  • Face recognition methods
  • Eigenfaces and Fisherfaces
  • Deep face recognition
  • Facial expression analysis
  • Age and gender estimation
  • Anti-spoofing techniques
8

3D Computer Vision

Understand depth perception, 3D reconstruction, and spatial understanding from 2D images.

  • Camera geometry
  • Stereo vision
  • Depth estimation
  • Structure from motion
  • 3D reconstruction
  • Point cloud processing
  • SLAM algorithms
  • Neural radiance fields
9

Video Analysis

Process temporal sequences of images for motion analysis, tracking, and video understanding.

  • Optical flow estimation
  • Motion detection
  • Object tracking
  • Action recognition
  • Video classification
  • Temporal segmentation
  • Video stabilization
  • Real-time processing
10

Medical Image Analysis

Apply computer vision techniques to medical imaging for diagnosis and treatment planning.

  • Medical imaging modalities
  • DICOM standard
  • Image registration
  • Medical image segmentation
  • Computer-aided diagnosis
  • Radiomics
  • Deep learning in radiology
  • Regulatory considerations
11

Advanced Deep Learning for Vision

Explore cutting-edge deep learning architectures and techniques for computer vision.

  • Vision transformers
  • Attention mechanisms
  • Generative adversarial networks
  • Self-supervised learning
  • Few-shot learning
  • Neural architecture search
  • Efficient architectures
  • Multimodal learning
12

Applications and Deployment

Build real-world computer vision applications and deploy them in production environments.

  • Autonomous vehicles
  • Surveillance systems
  • Augmented reality
  • Industrial inspection
  • Mobile applications
  • Edge deployment
  • Performance optimization
  • Ethical considerations

Unit 1: Image Fundamentals

Learn the basics of digital images, pixel representation, and fundamental image operations.

Digital Image Representation

Understand how images are stored and represented in digital form, including sampling and quantization.

Pixels Sampling Quantization
A digital image is a 2D array of pixels, where each pixel represents the intensity or color at a specific location. The process involves spatial sampling (determining pixel locations) and amplitude quantization (determining intensity levels).
# Digital Image Representation
image_representation = {
  "definition": "2D function f(x,y) where x,y are spatial coordinates",
  "digitization": {
    "sampling": "Convert continuous spatial coordinates to discrete grid",
    "quantization": "Convert continuous intensity values to discrete levels",
    "result": "M x N array of integer values"
  },
  "pixel_properties": {
    "grayscale": "Single intensity value (0-255 for 8-bit)",
    "color": "Multiple channels (RGB, HSV, etc.)",
    "bit_depth": "Number of bits per pixel (1, 8, 16, 32)",
    "dynamic_range": "Ratio between maximum and minimum intensity"
  },
  "resolution_types": {
    "spatial": "Number of pixels in image (width x height)",
    "intensity": "Number of gray levels (2^bit_depth)",
    "temporal": "Frame rate for video sequences"
  }
}

Color Spaces and Models

Explore different ways to represent and manipulate color information in digital images.

Common Color Spaces:
• RGB: Red, Green, Blue - additive color model
• HSV: Hue, Saturation, Value - intuitive for humans
• LAB: Lightness, A, B - perceptually uniform
• YUV: Luminance and chrominance - used in video
• CMYK: Cyan, Magenta, Yellow, Black - printing
Color Space Conversion:
Converting between color spaces involves mathematical transformations. RGB to grayscale: Gray = 0.299*R + 0.587*G + 0.114*B (weighted sum based on human perception).
# Color Space Analysis
color_spaces = {
  "rgb": {
    "description": "Red, Green, Blue channels",
    "range": "0-255 for each channel (8-bit)",
    "advantages": ["Hardware-oriented", "Simple operations"],
    "disadvantages": ["Not perceptually uniform", "Poor for color-based segmentation"]
  },
  "hsv": {
    "description": "Hue (0-360°), Saturation (0-100%), Value (0-100%)",
    "advantages": ["Intuitive", "Good for color filtering", "Separates color from intensity"],
    "applications": ["Color-based object detection", "Skin detection", "Image enhancement"]
  },
  "lab": {
    "description": "L* (lightness), a* (green-red), b* (blue-yellow)",
    "advantages": ["Perceptually uniform", "Device-independent"],
    "applications": ["Color correction", "Quality assessment", "Medical imaging"]
  }
}

Pixel Operations

Learn fundamental operations that can be performed on individual pixels and their neighborhoods.

Types of Pixel Operations:
• Point operations: Transform individual pixels independently
• Local operations: Use neighborhood information
• Global operations: Use entire image statistics
• Geometric operations: Change spatial relationships
Operation Complexity:
Point operations are O(N) where N is number of pixels. Local operations are O(N×k) where k is neighborhood size. Choose efficiently based on requirements.
# Pixel Operations Categories
pixel_operations = {
  "point_operations": {
    "definition": "Output pixel depends only on input pixel at same location",
    "examples": {
      "brightness": "g(x,y) = f(x,y) + c",
      "contrast": "g(x,y) = a * f(x,y)",
      "gamma_correction": "g(x,y) = c * f(x,y)^γ",
      "thresholding": "g(x,y) = 255 if f(x,y) > T else 0"
    },
    "complexity": "O(width × height)"
  },
  "neighborhood_operations": {
    "definition": "Output depends on input pixel and its neighbors",
    "examples": ["Convolution",