TL;DR

Computer vision teaches machines to interpret images and video. Core tasks include classification (what's in the image), detection (where are objects), and segmentation (pixel-level understanding). Deep learning has made these capabilities remarkably powerful and accessible.

Why it matters

Computer vision enables AI to understand the visual world: recognizing faces, analyzing medical scans, enabling self-driving cars, moderating content, and more. Understanding the basics helps you evaluate what's possible and realistic for visual AI applications.

How computers see images

Images as numbers

To a computer, images are arrays of numbers:

Grayscale: Each pixel is 0-255 (brightness)
Color (RGB): Each pixel has 3 values (red, green, blue)
Resolution: 1920x1080 image = 2 million pixels × 3 colors = 6 million numbers

From pixels to understanding

Raw pixels don't mean much alone. Computer vision finds patterns:

  • Edges and textures (low-level)
  • Shapes and parts (mid-level)
  • Objects and scenes (high-level)

Core computer vision tasks

Image classification

Task: What is in this image?
Output: Category label(s)

Examples:

  • Photo: "cat" or "dog"
  • X-ray: "normal" or "abnormal"
  • Product: "type A" or "type B"

Use cases:

  • Content organization
  • Medical diagnosis
  • Quality control

Object detection

Task: What objects are where?
Output: Bounding boxes + labels

Examples:

  • Find all people in a photo
  • Locate defects on a product
  • Identify vehicles in traffic

Use cases:

  • Security and surveillance
  • Inventory management
  • Autonomous vehicles

Image segmentation

Task: Label every pixel
Output: Pixel-level mask

Types:

  • Semantic: Label by class (all "person" pixels)
  • Instance: Separate each object

Use cases:

  • Medical image analysis
  • Photo editing (background removal)
  • Robotics (scene understanding)

Other tasks

Task Description
Face recognition Identify specific people
OCR Read text in images
Pose estimation Detect human body position
Depth estimation Infer 3D structure

How it works

Convolutional Neural Networks (CNNs)

The foundation of modern computer vision:

Key idea: Detect local patterns that combine into larger features

Process:

  1. Convolutional layers find patterns (edges, textures)
  2. Pooling layers reduce size
  3. Deeper layers find complex features
  4. Final layers make predictions

Vision Transformers

Newer approach using attention:

Key idea: Divide image into patches, attend to all parts

Benefits:

  • Captures global relationships
  • Often better performance
  • More scalable

Pre-trained models

Don't start from scratch:

  • Models trained on millions of images
  • Transfer learning adapts to your task
  • Much less data needed

Practical considerations

Data requirements

Task complexity Data needed
Simple classification Hundreds per class
Complex classification Thousands per class
Object detection Thousands with annotations
Segmentation Hundreds with pixel labels

Common challenges

Variability:

  • Lighting changes
  • Viewing angle
  • Occlusion (objects blocked)
  • Image quality

Solutions:

Performance factors

Speed vs accuracy:

  • Larger models = more accurate but slower
  • Real-time applications need optimization
  • Edge devices have constraints

What's possible today

Very reliable:

  • Simple classification
  • Face detection
  • OCR on clear text
  • Object detection in good conditions

Good but imperfect:

  • Complex scene understanding
  • Fine-grained recognition
  • Unusual viewpoints
  • Adversarial robustness

Still challenging:

  • Understanding context/intent
  • Novel object categories
  • Precise measurements
  • Complete scene reasoning

Common mistakes

Mistake Problem Prevention
Not enough data Poor generalization More data or transfer learning
Testing on training data False confidence Proper train/test split
Ignoring edge cases Production failures Test diverse scenarios
Wrong task formulation Building wrong thing Clearly define requirements

What's next

Explore more AI technology: