Скачать или смотреть Edge Detection - Python

Edge detection is a fundamental process in image processing and computer vision aimed at identifying boundaries within images. The goal of edge detection algorithms is to find the most relevant edges in an image or scene. These edges should then be connected into meaningful lines and boundaries, resulting in a segmented image containing two or more regions. These boundaries delineate regions with distinct visual features, such as changes in intensity, color, or texture.

Intensity Gradient: Edges typically occur where there is a significant change in intensity within an image.
Derivative Operations: Derivatives measure how pixel values vary across neighboring pixels.
Thresholding: After detecting edges, a thresholding step is commonly used to segment the image into regions of interest based on the detected edges.

First Order Derivative Edge Detection:
First-order derivative edge detectors compute the gradient of intensity in the image.
1) Sobel Operator
2) Prewitt Operator

Second Order Derivative Edge Detection:
Second-order derivative edge detectors compute the Laplacian of the image to identify regions of rapid intensity change.
1) Laplacian Operator

Canny Edge Detector
The Canny edge detector is a multi-stage algorithm that combines various techniques to provide high-quality edge detection with low error rates.
1. Gaussian Smoothing
2. Gradient Calculation
3. Non-maximum Suppression
4. Edge Tracking by Hysteresis

Example including all the edge detection techniques is included in Tutorial

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