Opencv erode binary image edge detection
Below example is done for a 9x9 kernel. Two basic morphological operators are Erosion and Dilation. Indeed, eroding an image twice is similar to eroding an image with a structuring element dilated with itself. When this relative variation reaches a local minimum, the region is identified as a MSER.
It is also possible to combine these two input modes. The basic idea of erosion is just like soil erosion only, it erodes away the boundaries of foreground object Always try to keep foreground in white. It is the best algorithm to use when you want to extract a foreground object in a still image for example, to cut and paste an object from one picture to another. A pixel in the original image either 1 or 0 will be considered 1 only if all the pixels under the kernel opencv erode binary image edge detection 1, otherwise it is eroded made to zero.
Dilation adds pixels to the boundaries of the object in an image, while erosion does exactly the opposite. When dilated with a cross-shaped structuring element, the square edges expand, except at the corner points where the cross shape does not hit the square. Closing is reverse of Opening, Dilation followed by Erosion.
Dilation has the exact opposite effect; that is, cliffs will gain terrain over the valleys. This marker image can be created as follows:. This is to remove the less interesting elongated shapes. Below example is done for a 9x9 kernel.
This rectangle is described by acv:: Goal In this tutorial you will learn how to: Meyer, Journal of Mathematical Imaging and Vision, volume 22, issueMaygives more information on the watershed transform.