diff --git a/year4/semester1/CT404: Graphics & Image Processing/materials/week6/24_06_Image_Processing_Morphological.pdf b/year4/semester1/CT404: Graphics & Image Processing/materials/week6/24_06_Image_Processing_Morphological.pdf new file mode 100644 index 00000000..1bd34c6e Binary files /dev/null and b/year4/semester1/CT404: Graphics & Image Processing/materials/week6/24_06_Image_Processing_Morphological.pdf differ diff --git a/year4/semester1/CT404: Graphics & Image Processing/materials/week6/24_06_Image_Processing_Morphological.pptx b/year4/semester1/CT404: Graphics & Image Processing/materials/week6/24_06_Image_Processing_Morphological.pptx new file mode 100644 index 00000000..7421b7f3 Binary files /dev/null and b/year4/semester1/CT404: Graphics & Image Processing/materials/week6/24_06_Image_Processing_Morphological.pptx differ diff --git a/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.pdf b/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.pdf index 9f0f2163..bd0caf13 100644 Binary files a/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.pdf and b/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.pdf differ diff --git a/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.tex b/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.tex index 86fad842..8c518f81 100644 --- a/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.tex +++ b/year4/semester1/CT404: Graphics & Image Processing/notes/CT404-Notes.tex @@ -1575,7 +1575,43 @@ COME BACK TO LAPLACIAN OPERATOR \subsection{Image Filtering in the Frequency Domain} Any signal, discrete or continuous, periodic or non-periodic, can be represented as a sum of sinusoidal waves of different frequencies and phases which constitute the frequency domain representation of that signal. +\section{Morphological Image Processing} +The term \textbf{morphology} refers to shape. +Morphological image processing assumes that an image consists of structures that can be handled by mathematical set theory. +It is normally applied to binary (black \& white) images, e.g. after applying thresholding. +A \textbf{set} is a group of pixels; different set operations can be performed on this set of pixels. +Applications for morphological image analysis generally involve image analysis at a very small scale (i.e., small regions of pixels) e.g., medical image processing, scientific image processing, industrial inspection, etc. +\begin{figure}[H] + \centering + \includegraphics[width=\textwidth]{images/generalmorphologicalimageprocessingpipeline.png} + \caption{A General Morphological Image Processing Pipeline} +\end{figure} + +\begin{figure}[H] + \centering + \includegraphics[width=\textwidth]{images/basicsetoperators.png} + \caption{Basic Set Operators (From left to right: union, intersection, shift of $A$ by some amount $b$, reflection about the origin (assumed to be at the center), complement, \& set difference)} +\end{figure} + +We can describe morphological differences in terms of intersections with test sets called \textbf{structuring elements (SEs)}. +SEs are of a much smaller size than the original image. +Morphological operations transform an image, i.e. changing a pixel from black to white (or vice-versa) if a defined structuring element ``fits'' at that point. +The shape + size of the structuring element directly affect the information about the image obtained by the operation. + +\begin{tcolorbox}[colback=gray!10, colframe=black, title=\textbf{Structuring Elements Example}] +\begin{figure}[H] + \centering + \includegraphics[width=0.5\textwidth]{images/seexample.png} + \caption{Structuring Elements Example} +\end{figure} + +The three black objects $A$, $B$, \& $C$ are very similar in size (i.e., number of pixels) but different in morphology/shape. +E.g., several possible translations of $D$ will fit into A, but none will fit into $B$. +\end{tcolorbox} + +\subsection{Basic Morphological Operations} +Basic morphological operations are performed by set operations between a given image and a structuring element, e.g., erosion, dilation, opening, closing. \end{document} diff --git a/year4/semester1/CT404: Graphics & Image Processing/notes/images/basicsetoperators.png b/year4/semester1/CT404: Graphics & Image Processing/notes/images/basicsetoperators.png new file mode 100644 index 00000000..efffd393 Binary files /dev/null and b/year4/semester1/CT404: Graphics & Image Processing/notes/images/basicsetoperators.png differ diff --git a/year4/semester1/CT404: Graphics & Image Processing/notes/images/generalmorphologicalimageprocessingpipeline.png b/year4/semester1/CT404: Graphics & Image Processing/notes/images/generalmorphologicalimageprocessingpipeline.png new file mode 100644 index 00000000..153d2728 Binary files /dev/null and b/year4/semester1/CT404: Graphics & Image Processing/notes/images/generalmorphologicalimageprocessingpipeline.png differ diff --git a/year4/semester1/CT404: Graphics & Image Processing/notes/images/seexample.png b/year4/semester1/CT404: Graphics & Image Processing/notes/images/seexample.png new file mode 100644 index 00000000..dc53c48c Binary files /dev/null and b/year4/semester1/CT404: Graphics & Image Processing/notes/images/seexample.png differ