Introduction to Computer Vision

Concepts

What is Computer Vision?

Computer Vision is the field of artificial intelligence that enables computers to understand and interpret visual information from the real world, similar to how humans use their eyes and brains. It involves capturing, processing, and analyzing digital images and videos to make decisions or extract meaningful information.

Key components of a computer vision system:

• Input: Digital images or videos from cameras, satellites, or medical scanners
• Processing: Mathematical operations that enhance, transform, or extract features
• Output: Descriptions, classifications, detections, or actions based on visual content

Image Representation

Digital images are represented as grids of pixels. Each pixel contains numerical values representing color and brightness.

• Grayscale images: Single channel with values 0-255 (black to white)
• Color images: Three channels (Red, Green, Blue) with values 0-255 each
• Shape: Height × Width × Channels (H×W×C) format

Example: A 224×224 RGB image has shape (224, 224, 3) with 150,528 total pixel values.

Basic Image Processing Operations

1. Resizing

Changing image dimensions while maintaining or altering aspect ratio. Used to standardize input size for neural networks.

2. Normalization

Scaling pixel values to a standard range, typically [0,1] or [-1,1], to help neural networks learn more effectively.

3. Data Augmentation

Creating modified versions of training images to increase dataset size and improve model generalization. Common techniques:

• Rotation, flipping, cropping
• Brightness/contrast adjustments
• Adding noise or blur

Core Computer Vision Tasks

Image Classification

Goal: Assign a single label to an entire image.

Example: Given a photograph, determine if it contains a "cat", "dog", or "car".

Analogy: Like sorting photos into labeled folders.

Object Detection

Goal: Identify and locate multiple objects within an image by drawing bounding boxes around them.

Example: Find all pedestrians and cars in a street scene and draw boxes around each with labels.

Analogy: Like using a highlighter to mark all instances of specific items in a photograph.

Image Segmentation

Goal: Classify each pixel in an image into categories, creating pixel-level outlines of objects.

Example: Identifying every pixel belonging to "road", "building", or "sky" in an aerial image.

Analogy: Like outlining distinct regions in a coloring map, where each region represents a specific category.

Deep Learning Approaches Overview

Convolutional Neural Networks (CNNs)

CNNs are specialized neural networks designed for processing grid-like data (images). They work by:

• Convolution: Scanning small regions to detect patterns like edges and textures
• Pooling: Reducing spatial dimensions while keeping important information
• Fully Connected Layers: Combining features to make predictions

Popular CNN architectures:

• ResNet: Uses "skip connections" to train very deep networks
• EfficientNet: Balances network depth, width, and resolution for optimal performance
• MobileNet: Lightweight architecture designed for mobile devices

Transformers in Vision

Vision Transformers (ViT) apply transformer architecture (originally for text) to images by treating them as sequences of patches. Compared to CNNs, which process pixels through local filters, ViTs use self-attention mechanisms to understand global relationships between different parts of the image from the start. They achieve state-of-the-art results but typically require more data and computation than CNNs.

Modern Approaches

Modern computer vision extends beyond standard architectures to address practical challenges:

• Transfer Learning: Using pre-trained models on large datasets (ImageNet, COCO) and adapting them to new tasks. This is the most practical starting point for most projects.
• Few-Shot Learning: Training models to recognize new categories with very few examples (sometimes just 1-5 images per class), mimicking how humans learn quickly.
• Self-Supervised Learning: Learning useful representations from unlabeled data by creating prediction tasks from the data itself (e.g., predicting masked portions of images), reducing reliance on expensive manual labeling.
• Real-Time Processing: Optimizing models for fast inference on edge devices using techniques like quantization and model pruning.

Evolution of Computer Vision Techniques

1. Traditional Methods (1960s-2010): Handcrafted features with machine learning classifiers. Key techniques included:

   • SIFT (Scale-Invariant Feature Transform): Detects and describes local features in images that remain stable despite changes in scale, rotation, or lighting. Used for image stitching and object recognition.
   • HOG (Histogram of Oriented Gradients): Describes object shapes by analyzing the distribution of gradient directions in localized portions of an image. Widely used for pedestrian detection.

2. Deep Learning Revolution (2012-2018): CNNs with large labeled datasets (ImageNet)

3. Modern Era (2018-Present): Transformers, attention mechanisms, and self-supervised learning

Examples

Example 1: Image Classification Workflow

A wildlife conservation organization wants to automatically identify species in camera trap images:

1. Input: Raw image (1280×720 pixels) of an animal
2. Preprocessing: Resize to 224×224, normalize pixel values
3. Model: Pre-trained ResNet50 network fine-tuned on local animal species
4. Output: Label "Cheetah" with 94% confidence
5. Action: Automatically sort images into folders for research analysis

Example 2: Object Detection for Retail

A retail store uses cameras to monitor shelf inventory:

1. Input: Shelf image (1920×1080)
2. Processing: Object detection model (YOLO) identifies products
3. Detection Results: Bounding boxes around 15 different products
4. Analysis: Counts products per category and identifies low stock
5. Output: Alert message "Restock milk - 2 units remaining"

Example 3: Medical Image Segmentation

Hospital uses AI to help radiologists identify tumors:

1. Input: MRI scan image slice
2. Segmentation Model: U-Net architecture segments brain tissue
3. Pixel-level output: Tumor area marked in red (all tumor pixels classified)
4. Doctor review: AI overlay helps radiologist measure tumor size and location
5. Benefit: Faster diagnosis with consistent measurements

Example 4: Real-Time Traffic Monitoring

City traffic department monitors congestion:

1. Input: Live video feed from intersection camera (30 frames per second)
2. Model: Lightweight MobileNet-based detection running on edge device
3. Processing: Counts cars, buses, trucks per lane each second
4. Output: Traffic density score and alerts when congestion threshold exceeded
5. Action: Adjusts traffic light timing dynamically

Example 5: Face Recognition System

Secure building access control:

1. Input: Camera captures face at entrance
2. Detection: Face detector locates and crops face region
3. Recognition: Face embedding model compares against authorized personnel database
4. Matching: Finds 98% similarity with employee "John Doe"
5. Access: Door unlocks automatically

Example 6: Satellite Image Analysis

Agricultural monitoring system:

1. Input: Satellite image of farmland (multi-spectral)
2. Segmentation: Classifies each pixel as "healthy crop", "dry soil", or "water"
3. Analysis: Calculates percentage of stressed crops per field
4. Output: Heat map showing crop health for 10,000 acres
5. Farmer alert: "Field 7 shows 40% stress - recommend irrigation"

Example 7: Quality Control in Manufacturing

Factory inspection system:

1. Input: Product photo on assembly line
2. Anomaly Detection: Model trained on perfect products only
3. Comparison: Flags products deviating from normal pattern
4. Detection: Identifies specific defects (scratch, dent, misalignment)
5. Action: Automatically rejects defective units

Example 8: Document Processing

Bank check scanning system:

1. Input: Photograph of check
2. OCR: Text recognition for amount and account numbers
3. Segmentation: Identifies signature field, amount field, date field
4. Verification: Cross-validates handwritten and printed amounts
5. Output: Structured data for transaction processing

Key Notes

Important Concepts for Beginners

• Image quality matters: Low resolution or blurry images significantly reduce model performance
• Data is critical: The quality and quantity of training data determine success more than model architecture
• Start simple: Begin with basic classification before tackling complex detection or segmentation tasks
• Compute requirements: Training deep networks requires GPUs; inference can often run on CPUs or mobile devices

Common Pitfalls

• Overfitting: Model memorizes training data but fails on new images
  • Signs: Training accuracy much higher than validation accuracy
  • Solutions: Use data augmentation, add dropout layers, increase training data, use early stopping
• Class imbalance: Having many more examples of one category than others
  • Signs: High accuracy but poor performance on minority classes
  • Solutions: Use class weights, oversample minority classes, or undersample majority classes
• Domain mismatch: Training on different types of images than deployment
  • Signs: Model works well in testing but poorly in real-world use
  • Solutions: Ensure training data reflects real-world conditions, collect data from deployment environment
• Evaluation metrics: Accuracy alone is misleading
  • Better metrics: Use precision, recall, and F1-score for imbalanced tasks; consider confusion matrices

Best Practices

• Understand your data: Always visualize samples, check distributions, and understand limitations
• Start with transfer learning: Use pre-trained models rather than training from scratch
• Use appropriate architectures:
  • CNNs for most image tasks
  • EfficientNet/MobileNet for resource-constrained environments
  • ViT for large-scale, high-accuracy requirements
• Monitor training: Use validation sets to detect overfitting early
• Benchmark baselines: Establish simple baselines to measure improvement against

When to Use Each Task Type

• Classification: Single object, simple decision (what is in the image?)
• Detection: Multiple objects, location needed (where are the objects?)
• Segmentation: Precise boundaries required (exactly what pixels belong to object?)

Development Environment

For learning and prototyping:

Recommended Setup for Beginners:

• Frameworks: PyTorch (torchvision) or TensorFlow (Keras)
• Libraries: OpenCV for image processing, NumPy for array operations
• Tools: Jupyter notebooks for interactive experimentation
• Cloud: Google Colab offers free GPU access (essential for training deep models)

Initial Setup Steps:

1. Install Python 3.8+
2. Install PyTorch or TensorFlow with GPU support if available
3. Install OpenCV: pip install opencv-python
4. Create a free Google Colab account for GPU-accelerated training
5. Download sample datasets: Kaggle, ImageNet, or COCO

Resources for Beginners:

• Official PyTorch tutorials: pytorch.org/tutorials
• TensorFlow tutorials: tensorflow.org/tutorials
• OpenCV documentation: docs.opencv.org
• Kaggle Learn: kaggle.com/learn/computer-vision

Ethics and Considerations

Privacy Concerns: Facial recognition systems raise surveillance and tracking concerns. A notable case: Amazon's Rekognition system faced criticism from civil rights groups for potential misuse by law enforcement. Always ask: "Would I be comfortable being analyzed by this system?"

Bias and Fairness: Models trained on limited demographics perform poorly on underrepresented groups. Example: Gender classification systems have shown error rates up to 34% for dark-skinned women versus <1% for light-skinned men. To mitigate: audit training data diversity, test on diverse populations, use fairness metrics.

Transparency: In medical or legal applications, "black box" decisions are unacceptable. Doctors need to understand why an AI flagged a tumor, and defendants have a right to understand algorithmic evidence. Solutions: use interpretable models, provide confidence scores, include human oversight.

Safety: Autonomous systems must fail safely. Example: A self-driving car must recognize pedestrians even in unusual poses or weather. Strategies: rigorous testing, redundancy, clear failure protocols, human-in-the-loop for critical decisions.

Best Practices for Ethical Development:

• Document training data sources and demographics
• Test models across different populations and conditions
• Implement human oversight for high-stakes decisions
• Be transparent about system limitations
• Consider societal impact before deployment

Learning Path Forward

Immediate Next Steps:

1. Set up your environment: Install PyTorch or TensorFlow and OpenCV
2. Run your first model: Load a pre-trained ResNet and classify an image
3. Experiment with augmentation: Apply rotations and flips to see effects
4. Visualize features: Use tools to see what the network learns

Coming in This Book:

• Chapter 3: Environment setup and first classification model
• Chapter 4: Data preparation, labeling, and augmentation strategies
• Chapter 5: Fine-tuning pre-trained models for custom tasks
• Chapter 6: Object detection with YOLO and Faster R-CNN
• Chapter 7: Segmentation and advanced architectures
• Chapter 8: Model optimization and deployment to edge devices

Practice Exercise to Try Now: Use Google Colab and the torchvision library to load a pre-trained ResNet model and classify any image from your computer. This will confirm your environment is ready and give you hands-on experience with the basic workflow.