🔍 Edge Detection & Filters
Master convolution kernels and feature extraction techniques
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Image Preprocessing Pipeline
Introduction to Edge Detection
🎯 What are Image Filters?
Image filters are mathematical operations that transform images by emphasizing certain features. Edge detection filters identify boundaries where pixel intensity changes rapidly, which is fundamental for computer vision.
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Why Edge Detection?
Edges contain most of the important structural information in an image. They help identify objects, shapes, and boundaries.
📐
Object Detection
Identify boundaries and shapes of objects in images
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Feature Extraction
Extract meaningful features for machine learning models
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Image Enhancement
Sharpen, blur, or stylize images for various purposes
🔬 How Filters Work
1
Kernel/Filter Matrix
Small matrix (usually 3×3 or 5×5) with specific values
2
Convolution Operation
Slide kernel over image, multiply and sum
3
Output Image
New image with filtered/enhanced features
📍 Common Applications
- •Medical imaging (tumor detection)
- •Autonomous vehicles (lane detection)
- •Document scanning (text recognition)
- •Facial recognition systems
🎯 Key Concepts
- •Gradient: Rate of intensity change
- •Kernel: Small weight matrix
- •Convolution: Sliding window operation
- •Edge: Sharp intensity transition