In the previous blog, we discussed erosion operation. In this blog, we will cover another morphological operation – Dilation which is the dual of erosion. Dual in the sense that dilating the object region is equivalent to eroding the background region and vice versa. So, let’s get started.
Dilation
As clear from the name, this operation dilates or expands the object region. This is just opposite of erosion. In this, we ask the simple question of whether the structuring element hits the object or not? (“hits” here means that at least one of the image pixels underlying the structuring element (SE) should have the same value as that of the corresponding SE). If it hits, that pixel is set to 1 else set to 0. This is all the concept behind the dilation operation. Now, let’s formulate this in terms of the set operation.
In general, the dilation of the binary image A by some SE B is defined as
That is the set of all values of z such that the intersection of B (translated by z and reflected about its origin) and A is non-empty. In other words, we place the SE over the image so that the origin of the SE coincides with the input pixel position and compare the underlying image pixels with the pixels of the corresponding SE. If the SE shares at least one common element with its underlying image pixels, then the central image pixel is set to 1 else 0.
Thus this increases the size of the object. If some holes or pepper noise is present in the object, this results in bridging the gaps or removing the noise similar to what we discussed in the low pass filtering. The extent of thickening is controlled by the shape and size of the SE.
For binary images, this can be simply done by taking the maximum of the neighborhood defined by the SE. Now, let’s see how to do this using OpenCV-Python. OpenCV provides a builtin function for this as shown below.
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cv2.dilate(src, kernel[, anchor[, iterations[, borderType[, borderValue]]]]) |
Here, src is the input image with any number of channels( all will be processed independently) and the kernel is the structuring element whose origin is defined by the anchor (default (-1,-1) i.e at the center of the SE). You can create the SE using cv2.getStructuringElement() or simply using numpy. Iterations specify how many times to repeat the dilation process. It is sometimes useful to pad the image to account for the boundary pixels or if the image is of non-regular shape and this can be done using the “borderType” and “borderValue” arguments. Below is an example where we dilate the image with the rectangular SE.
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import cv2 import numpy as np # load the image img = cv2.imread('kang.png', 0) # Following are the two methods by which we can construct # the structuring element # Method-1: using numpy kernel_1 = np.ones((3,1), dtype='uint8') # Method-2: using opencv getStructuringElement() function kernel_2 = cv2.getStructuringElement(cv2.MORPH_RECT,(1,3)) # Apply the dilation function dilation = cv2.dilate(b, kernel_2, iterations = 1) |
Similar to erosion, this can also be used to remove noise, detect the object boundary, etc. Although neither erosion nor dilation alone is effective in reducing noise. A more efficient approach is erosion followed by dilation or opening operation in general. Most of the morphological algorithms which we will discuss in the next blogs are also based on dilation and erosion. Hope you enjoy reading.
If you have any doubt/suggestion please feel free to ask and I will do my best to help or improve myself. Good-bye until next time.