Preface (Image Processing Toolbox)

Image Processing Toolbox

Image Processing Demos

The Image Processing Toolbox is supported by a full complement of demo applications. These are very useful as templates for your own end-user applications, or for seeing how to use and combine your toolbox functions for powerful image analysis and enhancement. The toolbox demos are located under the subdirectory,

```
matlabroot\toolbox\images\imdemos
```

where matlabroot represents your MATLAB installation directory.

The table below lists the demos available.

The easiest way to run an individual demo is to enter its name at the MATLAB command prompt. You can also launch MATLAB demos from the MATLAB Demo Window. To evoke this window select Demos from the Help menu of the main MATLAB window, or simply type demo at the command prompt. To see the list of available image processing demos, double-click on Toolboxes from the list on the left, then select Image Processing. Select the desired demo and press the Run button.

If you want to know whether there is a demo that uses a particular function, check the function name in the index. If there is a demo that demonstrates this function, a subentry of "See also demoname" will appear, where demoname is the name of the demo.

Demo Name
Brief Description

dctdemo
DCT image compression: you choose the number of coefficients and it shows you a reconstructed image and an error image.

edgedemo
Edge detection: all supported types with optional manual control over threshold, direction, and sigma, as appropriate to the method used.

firdemo
2-D Finite Impulse Response (FIR) filters: design your own filter by changing the cut-off frequency and filter order.

imadjdemo
Contrast Adjustment and Histogram Equalization: adjust intensity values using brightness, contrast, and gamma correction, or by using histogram equalization.

ipexconformal
Explore a Conformal Mapping: illustrates how to use spatial- and image-transformation functions to perform a conformal mapping.

ipexdeconvblind
Deblurring Images Using the Blind Deconvolution Algorithm: illustrates use of the deconvblind function.

ipexdeconvlucy
Deblurring Images Using the Lucy-Richardson Algorithm: illustrates use of the deconvlucy function.

ipexdeconvreg
Deblurring Images Using a Regularized Filter: illustrates use of the deconvreg function.

ipexdeconvwnr
Deblurring Images Using the Wiener Filter: illustrates use of the deconvwnr function.

ipexgranulometry
Finding the Granulometry of Stars in an Image: illustrates how to use morphology functions to perform granulometry.

ipexmri
Extracting Slices from a 3-Dimensional MRI Data Set: illustrates how to use the image transformation functions to interpolate and reslice a three-dimensional MRI data set, providing a convenient way to view a volume of data.

ipexnormxcorr2
Registering an Image Using Normalized Cross-correlation: illustrates how to use translation to align two images.

ipexregaerial
Registering an Aerial Photo to an Orthophoto: illustrates how to use the Control Point Selection Tool to align two images.

ipexrotate
Finding the Rotation and Scale of a Distorted Image: illustrates how to use the cp2tform function to get the rotation angle and scale factor of a distorted image.

ipexsegcell
Detecting a Cell Using Image Segmentation: illustrates how to use dilation and erosion to perform edge detection.

ipexsegmicro
Detecting Microstructures Using Image Segmentation: illustrates how to use morphological opening and closing to extract large objects from an image.

ipexsegwatershed
Detecting Touching Objects Using Watershed Segmentation: illustrates use of morphology functions to perform marker-control watershed segmentation.

ipexshear
Padding and Shearing an Image Simultaneously: illustrates how to use the padding options of the image transformation functions.

ipextform
Creating a Gallery of Transformed Images: illustrates how to use the imtransform function to perform many types of image transformations.

ipss001
Connected Components Labelling: includes double thresholding, feature-based logic, and binary morphology. All operations are performed on one image.

ipss002
Feature-based Logic: Shows object selection using AND operations on the `on' pixels in two binary images and shows filtering and thresholding on a single image.

ipss003
Correction of Nonuniform Illumination: creates a coarse approximation of the background, subtracts it from the image, and then adjusts the pixel intensity values to fill the entire range.

nrfiltdemo
Noise Reduction Using Linear and Non-linear Filters: allows you to add different types of noise with variable densities, and choose a filter neighborhood size.

qtdemo
Quadtree Decomposition: select a threshold and see a representation of the sparse matrix and a reconstruction of the original image.

roidemo
Region of Interest (ROI) Selection: select an ROI and apply operations such as unsharp and fill. Also displays the binary mask of the ROI.

Demo Name	Brief Description
`dctdemo`	DCT image compression: you choose the number of coefficients and it shows you a reconstructed image and an error image.
`edgedemo`	Edge detection: all supported types with optional manual control over threshold, direction, and sigma, as appropriate to the method used.
`firdemo`	2-D Finite Impulse Response (FIR) filters: design your own filter by changing the cut-off frequency and filter order.
`imadjdemo`	Contrast Adjustment and Histogram Equalization: adjust intensity values using brightness, contrast, and gamma correction, or by using histogram equalization.
`ipexconformal`	Explore a Conformal Mapping: illustrates how to use spatial- and image-transformation functions to perform a conformal mapping.
`ipexdeconvblind`	Deblurring Images Using the Blind Deconvolution Algorithm: illustrates use of the `deconvblind` function.
`ipexdeconvlucy`	Deblurring Images Using the Lucy-Richardson Algorithm: illustrates use of the `deconvlucy` function.
`ipexdeconvreg`	Deblurring Images Using a Regularized Filter: illustrates use of the `deconvreg` function.
`ipexdeconvwnr`	Deblurring Images Using the Wiener Filter: illustrates use of the `deconvwnr` function.
`ipexgranulometry`	Finding the Granulometry of Stars in an Image: illustrates how to use morphology functions to perform granulometry.
`ipexmri`	Extracting Slices from a 3-Dimensional MRI Data Set: illustrates how to use the image transformation functions to interpolate and reslice a three-dimensional MRI data set, providing a convenient way to view a volume of data.
`ipexnormxcorr2`	Registering an Image Using Normalized Cross-correlation: illustrates how to use translation to align two images.
`ipexregaerial`	Registering an Aerial Photo to an Orthophoto: illustrates how to use the Control Point Selection Tool to align two images.
`ipexrotate`	Finding the Rotation and Scale of a Distorted Image: illustrates how to use the `cp2tform` function to get the rotation angle and scale factor of a distorted image.
`ipexsegcell`	Detecting a Cell Using Image Segmentation: illustrates how to use dilation and erosion to perform edge detection.
`ipexsegmicro`	Detecting Microstructures Using Image Segmentation: illustrates how to use morphological opening and closing to extract large objects from an image.
`ipexsegwatershed`	Detecting Touching Objects Using Watershed Segmentation: illustrates use of morphology functions to perform marker-control watershed segmentation.
`ipexshear`	Padding and Shearing an Image Simultaneously: illustrates how to use the padding options of the image transformation functions.
`ipextform`	Creating a Gallery of Transformed Images: illustrates how to use the `imtransform` function to perform many types of image transformations.
`ipss001`	Connected Components Labelling: includes double thresholding, feature-based logic, and binary morphology. All operations are performed on one image.
`ipss002`	Feature-based Logic: Shows object selection using `AND` operations on the `on' pixels in two binary images and shows filtering and thresholding on a single image.
`ipss003`	Correction of Nonuniform Illumination: creates a coarse approximation of the background, subtracts it from the image, and then adjusts the pixel intensity values to fill the entire range.
`nrfiltdemo`	Noise Reduction Using Linear and Non-linear Filters: allows you to add different types of noise with variable densities, and choose a filter neighborhood size.
`qtdemo`	Quadtree Decomposition: select a threshold and see a representation of the sparse matrix and a reconstruction of the original image.
`roidemo`	Region of Interest (ROI) Selection: select an ROI and apply operations such as unsharp and fill. Also displays the binary mask of the ROI.

Typographical Conventions Getting Started