By the end of the course, the students will be able to
- understand and apply a few spatial/frequency domain, linear/non-linear and adaptive image enhancement methods to a given gray-level images
- interpret and visualize image subtraction (and other image arithmetic/logic) operations
- calculate convolution operation in spatial/frequency domain to find image features like gradient, Laplacian
- analytically define the Fourier transform and its inverse
- understand the meaning of continuous and discrete Fourier transform
- analytically compute and draw the Fourier transform of simple functions
- understand the Nyquist (sampling) theorem and aliasing
- interpret and write a few highpass and lowpass filter kernels
- prove basic Fourier transform properties like shifting, scaling, separable functions,
- do automatic segmentation of an image (2D or 3D) using fundamental image processing techniques (intensity-based, edge-based, watershed, graph-theoretic, global or locally adaptive, region-based, active-countour) on MATLAB using basic programming commands and built-in functions
- implement basic Hough transform methods (for line and circle detection)
- understand a few semi-automatic segmentation techniques (anisotropic, live-wire, dragging, etc)
- calculate fundamental shape and texture quantification features
- can formulate (mathematically) and iteratively solve (on MATLAB) image alignment (registration) problem using landmark, boundary or intensity information