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Edge_Detection_in digital image processing
- 1. Edge Detection inImage Processing An Overview
- 2. Introduction • • Edgedetection is a fundamental step in image processing and computer vision. • • It identifies points in a digital image where brightness changes sharply. • • These points often represent object boundaries.
- 3. Importance of EdgeDetection • • Helps in identifying object boundaries. • • Useful in image segmentation. • • Assists in object recognition. • • Facilitates feature extraction for further processing.
- 4. Categories of EdgeDetection • • Gradient-based Methods: Detect edges by looking for maximum and minimum values in the first derivative. • • Laplacian-based Methods: Detect edges by looking for zero crossings in the second derivative.
- 5. Common Edge Detection Techniques •• Sobel Operator • • Prewitt Operator • • Roberts Cross Operator • • Canny Edge Detector • • Laplacian of Gaussian (LoG)
- 6. Canny Edge Detection •Steps: • 1. Noise reduction using Gaussian filter. • 2. Calculate intensity gradients. • 3. Apply non-maximum suppression. • 4. Use double thresholding. • 5. Edge tracking by hysteresis.
- 7. Applications of EdgeDetection • • Medical Imaging (tumor boundary detection) • • Face Detection • • Object Tracking in Videos • • Industrial Inspection • • Autonomous Vehicles