Learning OpenCV, 2nd Edition

Figure 5-22 and those of a dilation operation on the same image are shown in

Figure 5-20. The erode operation is often used to eliminate “speckle” noise in an image. The idea here is

that the speckles are eroded to nothing while larger regions that contain visually significant content are not

affected. The dilate operation is often used when attempting to find connected components (i.e., large

discrete regions of similar pixel color or intensity). The utility of dilation arises because in many cases a

large region might otherwise be broken apart into multiple components as a result of noise, shadows, or

some other similar effect. A small dilation will cause such components to “melt” together into one.

To recap: when OpenCV processes the cv::erode() function, what happens beneath the hood is that

the value of some point p is set to the minimum value of all of the points covered by the kernel when

aligned at p; for the dilation operator, the equation is the same except that max is considered rather than

min:

Figure 5-22: Results of the erosion, or “min,” operator: bright regions are isolated and shrunk

𝑒𝑟𝑜𝑑𝑒 𝑥, 𝑦 = min

! , ! ∈!"#$"%

𝑠𝑟𝑐 𝑥 + 𝑖, 𝑦 + 𝑗

𝑑𝑖𝑙𝑎𝑡𝑒 𝑥, 𝑦 = max

(!,!)∈!"#$"%

𝑠𝑟𝑐 (𝑥 + 𝑖, 𝑦 + 𝑗)

You might be wondering why we need a complicated formula when the earlier heuristic description was

perfectly sufficient. Some ...