Experiment 6
Morphological Operations
The word Morphology denotes a branch of biology that deals with the form and structure of animals and plants. Here, we use the same word in the context of Mathematical Morphology, which means as a tool for extracting image components that are useful in the representation and description of region shape, such as boundaries, skeletons etc. We use Morphology for shape analysis & shape study. Mathematical Morphology is used to extract image components that are useful in the representation and description of region shape.
Mathematical morphology involves a convolution-like process using various shaped kernels, called structuring elements. Every Operation has two elements are present – Input Image (almost Binary) and Structuring element. The operation’s results depend upon the structuring element that is chosen. The structuring elements are mostly symmetric: squares, rectangles, and circles. Most common morphological operations are – Dilation and Erosion. The operations can be applied iteratively in selected order to affect a powerful process - Opening and Closing.
Let A be the image undergoing analysis, B be the structuring element, then Dilation is described by:
Erosion is defined as
Opening is defined as
Closing is defined as
Problem Objective
Write Python functions to perform the following operations on the given test image, ricegrains_mono.bmp. All functions must support binary images.
1. Make separate functions for erosion, dilation, opening, and closing of binary images
a. ErodeBinary, DilateBinary
Input: Binary image, structuring element
Output: Eroded/dilated image
b. OpenBinary, CloseBinary
Input: Binary image, structuring element
Output: Opened/closed image
Use structuring elements:
1 1
1 1 1 0 1 0
1 1 1 1 1 1
1 1 1 0 1 0
and 9 × 9, 15 × 15 kernels of grayvalue = 1 (reference point -centre pixel).
Note
1. Do not hardcode the filenames and/or image size into the code.
2. Show structuring element, input, and morphed images together.
3. Use proper code commenting and documentation.
4. Use self-explanatory identifiers for variables/functions etc.
References
2. R. C. Gonzalez and R. Woods, Digital Image Processing, Reading, MA: Addison-Wesley, 1992.
3. http://www.mmorph.com/mmtutor1.0/html/mmtutor/mm030gray.html
