Matlab and Image Processing Workshop-SKERG


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a workshop i performed for SKERG at KSU, 29th Oct.

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  • In computer vision application the processed images output for use by a computer. In image processing applications the output images are for human consumption.-----Historically, the field of image processing grew from electrical engineering as an extension of the signal processing branch, whereas are the computer science discipline was largely responsible for developments in computer vision.
  • Image processing is low level comparing with image analysis 1. Preprocessing: Is used to remove noise and eliminate irrelevant, visually unnecessary information. Noise is unwanted information that can result from the image acquisition process, other preprocessing steps might include: Gray –level or spatial quantization (reducing the number of bits per pixel or the image size). Finding regions of interest for further processing. 2. Data Reduction: Involves either reducing the data in the spatial domain or transforming it into another domain called the frequency domain, and then extraction features for the analysis process. 3. Features Analysis: The features extracted by the data reduction process are examine and evaluated for their use in the application.
  • Here start developing
  • Here start developing
  • imhist(I) displays a histogram for the image I above a grayscalecolorbar. The number of bins in the histogram is specified by the image type. If I is a grayscale image, imhist uses a default value of 256 bins. If I is a binary image, imhist uses two bins.BW = im2bw(I, level) converts the grayscale image I to a binary image. J = imnoise(I,'salt & pepper',d)adds salt and pepper noise to the image I, where d is the noise density. This affects approximately d*numel(I) pixels. The default for d is 0.05.IM2 = imcomplement(IM) computes the complement of the image IM. IM can be a binary, grayscale, or RGB image. IM2 has the same class and size as IM.In the complement of a binary image, zeros become ones and ones become zeros; black and white are reversed. In the complement of an intensity or RGB image, each pixel value is subtracted from the maximum pixel value supported by the class (or 1.0 for double-precision images) and the difference is used as the pixel value in the output image. In the output image, dark areas become lighter and light areas become darker.SE = strel('square', W) creates a square structuring element whose width is W pixels. W must be a nonnegative IM2 = imdilate(IM,SE) dilates the grayscale, binary, or packed binary image IM, returning the dilated image, IM2. The argument SE is a structuring element object, or array of structuring element objects, returned by the strel function.IM2 = imerode(IM,SE) erodes the grayscale, binary, or packed binary image IM, returning the eroded image IM2. The argument SE is a structuring element object or array of structuring element objects returned by the strel function.*** al function descriptions are from the Mathwork website ***
  • imtool opens a new Image Tool in an empty state. Use the File menu options Open or Import from Workspace to choose an image for display.
  • Lunch GUIDE tool: *Create a GUI * handle input and outputDeploy:mcc -mv filename.m -a topo.mat
  • Matlab and Image Processing Workshop-SKERG

    1. 1. Introduction to MATLAB and Image Processing By :Sulaf alMagooshi
    2. 2. Purpose and Objectives • • • • • Learn about Image Processing. Discover MATLAB environment. Learn about MATLAB features. Discover Image processing toolbox at MATLAB. Develop a beginner level MATLAB application. Sulaf Almagooshi, 2013 2
    3. 3. Outline • • • • • What is Image Processing? MATLAB tour. Image Processing in MATLAB. Develop Image Processing Application. Summary. Sulaf Almagooshi, 2013 3
    4. 4. What Is Image Processing?
    5. 5. What Is Image Processing? Computer imaging can be separate into two primary categories: 1. Computer Vision. Sulaf Almagooshi, 2013 2. Image Processing. 5
    6. 6. Image Processing topics The major topics within the field of image processing include: 1. Image restoration. 2. Image enhancement. 3. Image compression. 

 Sulaf Almagooshi, 2013 6
    7. 7. Image restoration Sulaf Almagooshi, 2013 7
    8. 8. Image enhancement Sulaf Almagooshi, 2013 8
    9. 9. Image compression Sulaf Almagooshi, 2013 9
    10. 10. Image Analysis Process The image analysis process can be broken down into three primary stages: 1.Preprocessing. Sulaf Almagooshi, 2013 2.Data Reduction. 10 3. Features Analysis.
    11. 11. Applications of Image Processing 1.Midicine . Sulaf Almagooshi, 2013 2.Security. 11 3. Astronomy.
    12. 12. MATLAB Tour
    13. 13. MATLAB Tour Sulaf Almagooshi, 2013 13
    14. 14. MATLAB Tour - some tricks ! • To know if you already used a variable name • Use “ which”. • To clear Command Window • Use “clc” • To know your variables • Use “ who” • To know your variable's info • Use “ whos” • To know your files • Use “ what” Sulaf Almagooshi, 2013 14
    15. 15. MATLAB Tour If you needed Help: Type help in Command window Sulaf Almagooshi, 2013 15
    16. 16. MATLAB Tour Sulaf Almagooshi, 2013 16
    17. 17. MATLAB Tour M-files • To store the code and execute later. • The file name will become a function, when we call it it will execute the file. • To open a new m-file , In the Command window , type edit Sulaf Almagooshi, 2013 17
    18. 18. MATLAB Tour Editor Or.. Sulaf Almagooshi, 2013 18
    19. 19. MATLAB Tour GUI • MATLAB offers ‘ GUIDE’ tool to design graphic interface. • In the Command window , type guide Sulaf Almagooshi, 2013 19
    20. 20. MATLAB Tour GUI GUIDE tool Sulaf Almagooshi, 2013 20
    21. 21. Image Processing in MATLAB.
    22. 22. Image Processing : Basic functions Function Description Imread to read an image into Matlab. imshow To show image in a figure. Figure To create an independent figure. size(x) To know the min and max for an object. imwrite(image, 'filename.type') To save the image. rgb2gray To convert a colored image to gray one. Sulaf Almagooshi, 2013 22
    23. 23. Image Processing : Basic functions Function Description imhist(x) Create a histogram. BW = im2bw(x) Convert to Binary image. J = imnoise(a,'salt & pepper',d); Add noise of type “ salt and pepper”. IM2 = imcomplement(IM) computes the complement of the image IM. SE = strel('square', 5); Create a structure. IM2 = imdilate(a,SE); To dilates an image. IM2= imerode(a,SE); To erode an image. Sulaf Almagooshi, 2013 23
    24. 24. MATLAB Image Processing toolbox imtool(f) Sulaf Almagooshi, 2013 24
    25. 25. Activity : Develop Image Processing Application
    26. 26. Connect! @theSulaf Sulaf Almagooshi, 2013 26