Computer Vision

Computer vision enables machines to understand and process visual information from the world, mimicking human visual perception. This field combines image processing, pattern recognition, and deep learning to extract meaningful information from images and videos.

Image Processing

Image Preprocessing

Definition: Image preprocessing involves transforming raw images into a format more suitable for analysis through various cleaning and enhancement techniques.

Key Concepts:

• Normalization: Scaling pixel values to a standard range
• Augmentation: Creating variations of training images
• Filtering: Removing noise and enhancing features
• Resizing: Standardizing image dimensions

Common Applications:

• Medical image analysis
• Satellite imagery processing
• Quality control in manufacturing
• Security system feeds

Example:

Medical Image Enhancement

Preparing X-ray images for automated diagnosis.

Steps:

1. Noise reduction using Gaussian filtering
2. Contrast enhancement
3. Standardization to 512x512 pixels
4. Intensity normalization to [0,1] range

Key Points:

• Improves feature visibility
• Reduces processing artifacts
• Ensures consistent input format

Feature Extraction

Definition: Feature extraction identifies distinctive characteristics in images that are useful for classification or detection tasks.

Key Components:

• Edge detection algorithms
• Corner detection
• Texture analysis
• Shape descriptors

Vision Applications

Object Detection

Definition: Object detection combines localization and classification to identify and locate specific objects within images.

Key Technologies:

• YOLO (You Only Look Once)
• R-CNN family
• SSD (Single Shot Detector)
• RetinaNet

Important

Real-time object detection requires careful balance between accuracy and processing speed.

Image Segmentation

Definition: Image segmentation is the process of partitioning an image into distinct regions or segments, often for the purpose of simplifying analysis.

Key Concepts:

• Semantic Segmentation: Classifying each pixel into a category
• Instance Segmentation: Differentiating between separate objects of the same class
• Panoptic Segmentation: Combining semantic and instance segmentation

Common Applications:

• Autonomous driving (road, vehicles, pedestrians)
• Medical image analysis (tumor detection, organ segmentation)
• Image editing and manipulation
• Object tracking in videos

Example:

Autonomous Vehicle Navigation

Using segmentation to identify drivable areas, obstacles, and lane markings.

Process:

1. Capture video stream from cameras
2. Apply real-time segmentation
3. Detect and track objects (cars, pedestrians)
4. Navigate based on segmented output

Key Points:

• Critical for safe and efficient navigation
• Requires high accuracy and low latency
• Combines with other sensors (LiDAR, radar) for robustness

Facial Recognition

Definition: Facial recognition is a technology that identifies or verifies a person's identity using their facial features.

Key Components:

• Face detection
• Feature extraction (landmarks, embeddings)
• Matching and recognition

Common Applications:

• Security and surveillance
• User authentication
• Emotion recognition
• Human-computer interaction

Example:

Smartphone Unlocking

Using facial recognition to unlock a phone.

Process:

1. Capture face image using front camera
2. Extract facial features (eyes, nose, mouth)
3. Compare with stored features
4. Unlock if match is found

Key Points:

• Must be fast and accurate
• Needs to work in various lighting conditions
• Should preserve user privacy and data security

---

Back to AI & Machine Learning