Image Processing with Matlab (R)

This course deals with using MATLAB® Image Processing toolbox for image processing, analysis, visualization, and algorithm development. The training covers various topics such as importing and exporting images, pre-and post-processing of images, analysis and visualization of images, and spatial transformations and image registration.

Course Content:

• Introduction    
• A quick overview of MATLAB(R) computing environment
• Overview of MATLAB(R) Image Processing toolbox
• Course content and material discussion
• Acquiring and handling images in MATLAB(R)    
• Connecting the hardware
• Retrieving hardware information
• Acquiring and viewing the image data
• Image file I/O
• Exploring image types (RGB, binary, intensity, and
• indexed images)
• Image type conversions
• The concept of color space and image color space conversions
• Finding pixel value information
• Computing mean and standard deviation of images
• Measuring properties of image regions
• Image enhancement techniques    
• Adjusting image intensity
• Image histogram operations: adjustment, equalization,and stretching
• Multidimensional arrays
• Image arithmetic operations
• Cropping and resizing images
• Image alignment correction: rotating images
• Image filtering    
• Neighborhood and block processing of images
• Distinct block operations
• Sliding neighborhood operations
• Performing image convolution and correlation
• Averaging filters
• Region of interest processing
• Introduction to spatial and frequency domain filtering
• Image restoration techniques    
• Reducing noise from images
• De-blurring images
• Correcting background illumination
• Edge detection related operations    
• Edges in an image
• Detecting edges with various methods: Sobel, Prewitt,
• Roberts, Laplacian of Gaussian, zero cross and Canny.
• Computing edge directive histogram
• Image morphological operations    
• Bridging unconnected pixels, cleaning, closing, and opening
• Dilation and erosion
• Identifying and labeling connected components
• Image transforms    
• Forward and inverse Discrete cosine transform
• Forward and inverse Fast Fourier transform
• Forward and inverse Radon transform
• Applying wavelet transform to images