Morphological transformations on images

Morphological operations are based on image shapes, and they work best on binary images. We can use these to get away with a lot of unwanted information, such as noise in an image. Any morphological operation requires two inputs: image and kernel. In this section, we will explore the erosion, dilation, and gradient of an image. Since binary images are most suitable for explaining this concept, we will use a binary image (black and white) to study the concepts.

Erosion removes the boundaries in the image and slims it. In a binary image, white is the foreground and black is the background. All the pixels at the boundary of the white foreground image are made zero, thus slimming the image and eroding away the boundary. Dilation is exactly opposite of erosion; it expands the foreground image boundary and flattens it. The extent of to erosion and dilation depends on the kernel and the number of iterations. The morphological gradient of an image is the difference...