The early stages of vision processing identify features in images that are relevant to estimating the structure and properties of objects in a scene. Edges are one such feature. Edges are significant local changes in the image and are important features for analyzing images. Edges typically occur on the boundary between two different regions in an image. Edge detection is frequently the first step in recovering information from images. Due to its importance, edge detection continues to be an active research area. Here we discussed about canny edge detection statement:
Canny Edge Detection: Overview
Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Classical methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. There are an extremely large number of edge detection operators available, each designed to be sensitive to certain types of edges. Variables involved in the selection of an edge detection operator include:
- Edge Orientation: The geometry of the operator determines a characteristic direction in which it is most sensitive to edges. Operators can be optimized to look for horizontal, vertical, or diagonal edges.
- Noise Environment: Edge detection is difficult in noisy images, since both the noise and the edges contain high-frequency content. Attempts to reduce the noise result in blurred and distorted edges. Operators used on noisy images are typically larger in scope, so they can average enough data to discount localized noisy pixels. This results in less accurate localization of the detected edges.
- Edge Structure: Not all edges involve a step change in intensity. Effects such as refraction or poor focus can result in objects with boundaries defined by a gradual change in intensity. The operator needs to be chosen to be responsive to such a gradual change in those cases. Newer wavelet-based techniques actually characterize the nature of the transition for each edge in order to distinguish, for example, edges associated with hair from edges associated with a face.
Canny Edge Detection: Definition
In a digital image, an edge is a point in the image where the brightness changes sharply. The canny edge detector was developed by John F. Canny in 1986. It is used to detect a wide range of edges in images.
The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images.
Canny’s aim was to discover the optimal edge detection algorithm. Below are some of the attributes of the Canny Edge detector:
- Good Detection: In determining a true or false edge, thresholds are required. The Canny edge detector can be fine-tuned with the right threshold to provide good edges on average.
- Noise Sensitivity: The Canny edge detector eliminates or reduces noise that could corrupt results.
- Orientation Sensitivity: The Canny edge detector accurately detects not just the edge magnitude, but also the edge orientation, which can be used in post processing to connect edge segments and in turn suppress non-maximum edge magnitude.
- Speed and Efficiency: The Canny edge detector allows for recursive implementation which improves efficiency.
Canny Edge Detection Algorithm
The algorithm runs in 5 separate steps:
- Smoothing: Blurring of the image to remove noise.
- Finding gradients: The edges should be marked where the gradients of the image has large magnitudes.
- Non-maximum suppression: Only local maxima should be marked as edges.
- Double Thresholding: Potential edges are determined by Thresholding.
- Edge tracking by hysteresis: Final edges are determined by suppressing all edges that are not connected to a very certain (strong) edge.
There are many ways to perform edge detection. However, the majority of different methods may be grouped into two categories:
- Gradient: The gradient method detects the edges by looking for the maximum and minimum in the first derivative of the image.
- Laplacian: The Laplacian method searches for zero crossings in the second derivative of the image to find edges. An edge has the one-dimensional shape of a ramp and calculating the derivative of the image can highlight its location.
An Example of Canny Edge Detection (Gradient Method)
Figure 1: Original Image
Figure 2: Edge Detected By Canny (Gradient Method)
 Andrew Enughwure, “A Combination Approach to Face Recognition”, Master Thesis, 2015
 “A Combination Approach to Face Recognition”, available online at: https://ena.etsmtl.ca/pluginfile.php/59678/mod_resource/content/0/Canny%20Wikipedia.pdf
 John Canny, “A Computational Approach too Edge Detection”, IEEE Transaction Pattern Analysis and Machine Intelligence, 8(6):679698, 1986.
 “Canny Edge Detection”, available online at: http://fourier.eng.hmc.edu/e161/lectures/canny/node1.html