Digital Image Processing (Lab 05)
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This document provides an overview of using various MATLAB functions to analyze digital image data and pixel values, including histograms, contrast enhancement, and statistics. It discusses using imhist to display histograms, histeq and adapthisteq for contrast adjustment, impixel to view pixel values, improfile for intensity profiles along lines, and imcontour to create contour plots. Examples are given applying these functions to analyze and enhance grayscale and RGB images.
![Example
clc,clear all, close all;
I=imread('pout.tif');
figure;
subplot(1,2,1);imshow(I);
subplot(1,2,2);imhist(I);
imh=imadjust(I,[0.3;0.6],[0.0,1.0]);
imh1=histeq(I);
figure;
subplot(2,2,1);imshow(imh);title('Stretched Image');
subplot(2,2,2);imhist(imh);
subplot(2,2,3);imshow(imh1);title('Histeq Image');
subplot(2,2,4);imhist(imh1);](https://image.slidesharecdn.com/lab05-160824022245/85/Digital-Image-Processing-Lab-05-9-320.jpg)
![improfile
• The intensity profile of an image is the set of intensity values
taken from regularly spaced points along a line segment or
multi
I = fitsread('solarspectra.fts');
imshow(I,[]);
improfile
improfile displays a plot of the data along the line. Notice the
peaks and valleys and how they correspond to the light and dark
bands in the image.](https://image.slidesharecdn.com/lab05-160824022245/85/Digital-Image-Processing-Lab-05-12-320.jpg)
Digital Image Processing (Lab 05)
- 1. Presented by: Dr. Moe Moe Myint Information Technology Department Technological University (Kyaukse), Myanmar Digital Image Processing moemoemyint@moemyanmar.ml www.slideshare.net/MoeMoeMyint
- 2. • Only Original Owner has full rights reserved for copied images. • This PPT is only for fair academic use.
- 3. Pixel Value and Statistic (Lab 5) M. M. Myint Dr. Moe Moe Myint Information Technology Department Technological University (Kyaukse) Create histograms, contour plots, and get statistics on image regions
- 4. Objectives • To display histogram of image data and pixel color values Required Equipment • Computers with MATLAB software and Projector Practical Procedures • Read the image and display it • Use histeq for enhancing the contrast of images • Use impixel for pixel color values • Use improfile for pixel-value cross-sections along line segments • Use imcontour for create contour plot of image data
- 5. Pixel Values and Statistics corr2 2-D correlation coefficient imhist Display histogram of image data impixel Pixel color values improfile Pixel-value cross-sections along line segments imcontour Create contour plot of image data mean2 Average or mean of matrix elements regionprops Measure properties of image regions std2 Standard deviation of matrix elements
- 6. Histogram of Image Data • An image histogram is a chart that shows the distribution of intensities in an indexed or grayscale image. You can use the information in a histogram to choose an appropriate enhancement operation. For example, if an image histogram shows that the range of intensity values is small, you can use an intensity adjustment function to spread the values across a wider range. • imhist(I) displays a histogram for the image Examples Read image and display it. I = imread('rice.png'); imshow(I) figure, imhist(I)
- 7. adapthisteq • Contrast-limited adaptive histogram equalization (CLAHE) Syntax J = adapthisteq(I) J = adapthisteq(I,param1,val1,param2,val2...)
- 8. • histeq enhances the contrast of images by transforming the values in an intensity image, or the values in the colormap of an indexed image, so that the histogram of the output image approximately matches a specified histogram. Examples • Enhance the contrast of an intensity image using histogram equalization. I = imread('tire.tif'); J1 = histeq(I); J2 = adapthisteq(I); imshow(I), title('Original Image'); figure, imshow(J1); title('Histogram Equalization'); figure, imshow(J2); title('Adaptive Histogram Equalization');
- 9. Example clc,clear all, close all; I=imread('pout.tif'); figure; subplot(1,2,1);imshow(I); subplot(1,2,2);imhist(I); imh=imadjust(I,[0.3;0.6],[0.0,1.0]); imh1=histeq(I); figure; subplot(2,2,1);imshow(imh);title('Stretched Image'); subplot(2,2,2);imhist(imh); subplot(2,2,3);imshow(imh1);title('Histeq Image'); subplot(2,2,4);imhist(imh1);
- 10. subplot • Create axes in tiled positions
- 11. • impixel returns the red, green, and blue color values of specified image pixels. In the syntax below, impixel displays the input image and waits for you to specify the pixels with the mouse. 1. Display an image. imshow canoe.tif 2. Call impixel. When called with no input arguments, impixel associates itself with the image in the current axes. impixel 3. Select the points you want to examine in the image by clicking the mouse. impixel places a star at each point you select. 4. When you are finished selecting points, press Return. impixel returns the pixel values in an n-by-3 array, where n is the number of points you selected. The stars used to indicate selected points disappear from the image. pixel_values = 0.1294 0.1294 0.1294 0.5176 0 0 0.7765 0.6118 0.4196
- 12. improfile • The intensity profile of an image is the set of intensity values taken from regularly spaced points along a line segment or multi I = fitsread('solarspectra.fts'); imshow(I,[]); improfile improfile displays a plot of the data along the line. Notice the peaks and valleys and how they correspond to the light and dark bands in the image.
- 13. The example below shows how improfile works with an RGB image. Use imshow to display the image in a figure window. Call improfile without any arguments and trace a line segment in the image interactively. In the figure, the black line indicates a line segment drawn from top to bottom. Double-click to end the line segment. imshow peppers.png improfile The improfile function displays a plot of the intensity values along the line segment. The plot includes separate lines for the red, green, and blue intensities. In the plot, notice how low the blue values are at the beginning of the plot where the line traverses the orange pepper.
- 14. imcontour • You can use the toolbox function imcontour to display a contour plot of the data in a grayscale image. • This example displays a grayscale image of grains of rice and a contour plot of the image data: 1. Read a grayscale image and display it. I = imread('rice.png'); imshow(I) 2. Display a contour plot of the grayscale image. figure, imcontour(I,3)
- 15. Questions?
Editor's Notes
- #4: A spatial transformation (also known as a geometric operation) modifies the spatial relationship between pixels in an image, mapping pixel locations in an input image to new locations in an output image. The toolbox includes functions that perform certain specialized spatial transformations, such as resizing and rotating an image. In addition, the toolbox includes functions that you can use to perform many types of 2-D and N-D spatial transformations, including custom transformations. Resizing an Image Rotating an Image Cropping an Image Performing General 2-D Spatial Transformations Performing N-Dimensional Spatial Transformations Example: Performing Image Registration
- #12: impixel returns the red, green, and blue color values of specified image pixels. In the syntax below, impixel displays the input image and waits for you to specify the pixels with the mouse. P = impixel(I) P = impixel(X,map) P = impixel(RGB) If you omit the input arguments, impixel operates on the image in the current axes. Use normal button clicks to select pixels. Press Backspace or Delete to remove the previously selected pixel. A shift-click, right-click, or double-click adds a final pixel and ends the selection; pressing Return finishes the selection without adding a pixel. When you finish selecting pixels, impixel returns an m-by-3 matrix of RGB values in the supplied output argument. If you do not supply an output argument, impixel returns the matrix in ans. You can also specify the pixels noninteractively, using these syntax. P = impixel(I,c,r) P = impixel(X,map,c,r) P = impixel(RGB,c,r) r and c are equal-length vectors specifying the coordinates of the pixels whose RGB values are returned in P. The kth row of P contains the RGB values for the pixel (r(k),c(k)). If you supply three output arguments, impixel returns the coordinates of the selected pixels. For example, [c,r,P] = impixel(...) To specify a nondefault spatial coordinate system for the input image, use these syntax. P = impixel(x,y,I,xi,yi) P = impixel(x,y,X,map,xi,yi) P = impixel(x,y,RGB,xi,yi) x and y are two-element vectors specifying the image XData and YData. xi and yi are equal-length vectors specifying the spatial coordinates of the pixels whose RGB values are returned in P. If you supply three output arguments, impixel returns the coordinates of the selected pixels. [xi,yi,P] = impixel(x,y,...)