Image Processing with Matlab(R)







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.

  • 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

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