Canny Edge Detection

Advanced Algorithms

O(w×h) time, O(w×h) space

Advanced

Multi-stage edge detection algorithm. Gaussian blur → gradient calculation → non-maximum suppression → hysteresis thresholding. Industry standard for edge detection.

Prerequisites:

Image processing basics

Convolution

Gaussian filters

Gradient computation

Visualization

Interactive visualization for Canny Edge Detection

Canny Edge:

• Multi-stage algorithm

Interactive visualization with step-by-step execution

Implementation

Language:

1// Simplified Canny Edge Detection
2function cannyEdgeDetection(image: number[][], lowThreshold: number, highThreshold: number): number[][] {
3  const height = image.length;
4  const width = image[0].length;
5  
6  // 1. Gaussian blur (simplified)
7  const blurred = gaussianBlur(image, 5);
8  
9  // 2. Calculate gradients
10  const { magnitude, direction } = calculateGradients(blurred);
11  
12  // 3. Non-maximum suppression
13  const suppressed = nonMaxSuppression(magnitude, direction);
14  
15  // 4. Double threshold
16  const edges = doubleThreshold(suppressed, lowThreshold, highThreshold);
17  
18  // 5. Edge tracking
19  return edgeTrackingByHysteresis(edges);
20}

Deep Dive

Theoretical Foundation

5 stages: 1) Gaussian blur (noise reduction), 2) Gradient intensity/direction (Sobel), 3) Non-maximum suppression (thin edges), 4) Double threshold (strong/weak edges), 5) Edge tracking by hysteresis.

Complexity

Time

Best

O(w×h)

Average

O(w×h)

Worst

O(w×h)

Space

Required

O(w×h)

Applications

Industry Use

Autonomous vehicle lane detection

Medical image analysis (tumor boundaries)

Industrial quality control and inspection

Optical character recognition (OCR)

Robotics navigation and obstacle detection

Satellite image analysis

Augmented reality marker detection

Use Cases

Object detection

Image segmentation

Feature extraction

Computer vision preprocessing

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