The algorithm of canny and edge detection

The edge of image refers to the part where the brightness of the image is changed significantly, and the gray section of the region can be regarded as a step, which changes from one gray value to another in a very small buffer area to a gray scale with a large difference. The edge of the image is focused on the most information of the image, the determination and extraction of image edge is very important for the whole image scene recognition and understanding, and is also an important feature that the image segmentation relies on, the edge detection is mainly the measurement, detection and localization of the gray scale change of the image, since the 1959 edge detection has been proposed, After more than 50 years of development, there are many different methods of edge detection. The Laplace operator often produces double boundary in some of the operators we use for edge detection, while other operators, such as Sobel operators, often form unclosed regions. In this paper, we mainly discuss the algorithm of obtaining closed boundary area in edge detection.
Basic steps for Image edge detection
(1) filter. Edge detection is mainly based on derivative calculations, but is affected by noise. However, the filter reduces the noise and also leads to the loss of edge strength.
(2) enhanced. The enhancement algorithm highlights the points in the neighborhood that have a significant change in grayscale. It is generally done by calculating the gradient amplitude.
(3) detection. But in some images, the gradient amplitude is not the edge point. The simplest edge detection is the gradient amplitude threshold decision.
(4) Positioning. Precisely determine the position of the edge.

Basic steps of edge detection acid method

The method of the Hough transform is to use the global characteristics of the image to direct detection of the target contour, under the condition of the known region shape, the Hough transform can accurately capture the boundary of the target (continuous acquisition discontinuous), and finally in the form of continuous curve output transformation results, The transformation can extract the target of the known shape accurately from the strong noise environment.
The core idea of the Havelock transformation is: The duality of Point-line (duality). By transforming images from image control to parameter space, a line equation with an over point (x, y) in image space is y=px+q, and an algebraic transformation can be converted to another form p=-px+y, i.e. a straight line in the parametric space over the point (P,Q). If the slope of the line in the image space and the constant intercept, its parameter space must be too point (p, Q), which also shows that in the image space, the point corresponding to the parameter space of the line of common points. The change of the Havelock is based on the duality of the point-line to transform the linear detection problem in the image space into the point detection problem in the parameter space, the latter is much easier to deal with than the former, and the edge detection can be realized simply by accumulating statistics.
The Hough transform can detect not only the target of the first order curve, but also the high-order curve of the garden and ellipse.

The algorithm of canny and edge detection