In this blog, we will discuss Hit-or-Miss transformation. This is basically used for shape detection or finding particular patterns in the given image. The shape or pattern to match has to be provided via the structuring element. This transformation can be easily implemented using the erosion operation. So, let’s get started.
Here, we use two structuring elements (say B1 and B2). In this, we ask a simple question of does B1 fits the object while, simultaneously, B2 misses the object, i.e. fits the background? In other words, we are interested only in those pixels whose neighborhood exactly matches B1 while not matching B2 at the same time. As we already discussed that erosion answers the question of whether the SE fits or not. Thus, Hit-or-Miss operation can be expressed in terms of erosion as
Here, we assume that both SE B1 and B2 don’t intersect otherwise this operation cannot be performed. Because both B1 and B2 are disjoint sets, we can express both in terms of a single structuring element. Doing so makes the operation more interpretable. Let’s see how.
Suppose we want to find a T-shaped pattern in the image defined by the SE B1 such that it does not contain the pattern defined by B2 in its right neighborhood. 0 below represents that we don’t care about these positions. Below figure shows how to combine two SE into one.
Now, just compare the underlying pixels values with the combined SE. If it matches exactly then the pixel underneath the origin of SE is set to 1 else 0. Let’s take an example. Suppose we want to find the above combined SE in the image shown below. Clearly, the one on the lower right matches the pattern defined by the combined SE. The result is shown on the right side.
Below is the code for this. The hit-or-miss can be implemented using the OpenCV cv2.morphologyEx() function by passing the flag cv2.MORPH_HITMISS as shown below.
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import cv2 import numpy as np # Create the input image input_image = np.array(( [0, 0, 0, 0, 0, 0, 0, 0], [0, 255, 255, 255, 0, 0, 0, 255], [0, 0, 255, 255, 0, 0, 0, 0], [0, 0, 255, 255, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 255, 255, 0, 0, 255, 255, 255], [0,255, 0, 0, 0, 0, 255, 0], [0, 255, 0, 0, 0, 0, 255, 0]), dtype="uint8") # Construct the structuring element kernel = np.array(( [1, 1, 1], [0, 1, -1], [0, 1, -1]), dtype="int") # Apply hit-or-miss transformation output_image = cv.morphologyEx(input_image, cv2.MORPH_HITMISS, kernel) |
This way you can find any patterns in the input image. In the next blog, we will discuss other applications of hit-or-miss operation such as thinning, thickening, convex Hull, etc. 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.