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Image_Processing.md

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Image Processing ⚡

> Why convert to grayscale?

  • When loaded into memory, a grayscale image occupies a third of the space required for an RGB image.
  • Because a grayscale image has a third of the data, it requires less computational power to process and can reduce computation time.
  • A grayscale image is conceptually simpler than an RGB image, so developing an image processing algorithm can be more straightforward when working with grayscale.

> Intensity Thresholding

  • Show the image : 
imshow(image_name)
  • Plot the intensity histogram : 
imhist(image_name)
  • Logical operators to threshold the intensity values of a grayscale image, creating a binary image : 
B = g > thresh;

> Automatic Thresholding

  • To automate the threshold selection process, you can use the imbinarize function, which calculates the "best" threshold for the image : 
 gBinary = imbinarize(g);
  • You can have imbinarize look at smaller portions of the image and pick the best threshold for that region by passing the adaptive option as the second argument to imbinarize:
 gBinary = imbinarize(g,"adaptive");
  • To display images in pair : 
imshowpair(image1,imagw2,"Montage")
  • In imbinarize, you can designate whether the foreground is bright or dark by setting the ForegroundPolarity option : 
gBinary = imbinarize(g,"adaptive",...
           "ForegroundPolarity","dark")

> Working With Binary Image

  • You can compute the sum across rows of an array using the sum function : 
rSum = sum(BW,2)
  • Plot the data : 
plot(<data>)

> Pre- and Postprocessing Techniques

  • Use the fspecial function to create an n-by-n averaging filter : 
F = fspecial("average",n)
  • Apply a filter F to an image I by using the imfilter function.
Ifltr = imfilter(I,F);
  • Structuring elements are created using the strel function.
SE = strel("diamond",5)
  • To perform a closing operation on an image I with structuring element SE, use the imclose function.
Iclosed = imclose(I,SE);
  • In general, you can remove one image from another by subtracting it.
Isub = I2 - I1;
  • You can create a rectangular structuring element using strel with a size array in the format [height width].
SE = strel("rectangle",[10 20]);
  • To isolate the background, you wanted to eliminate the dark text by emphasizing the bright regions. To do this, you used imclose to close the image.
Iclosed = imclose(I,SE);
  • To emphasize the dark text instead, so you need to perform the opposite operation and open the image using imopen.
Iopened = imopen(I,SE);

> Pre- and Postprocessing to Improve Segmentation

  • Segmentation can be improved in two ways: by preprocessing the image before binarizing and by postprocessing the binary image itself.
  1. Noise Removal

Smooth pixel intensity values to reduce the impact of variation on binarization.

2.Background Isolation and Subtraction

Isolate and remove the background of an image before binarizing.

3.Binary Morphology

Emphasize particular patterns or shapes in a binary image.One way to remove text from an image is to use morphological operations.A morphological closing operation emphasizes the bright paper and removes the dark text.

4.Inverting a binary image

A binary image is composed of 0s and 1s. In logical terms, 0 = false and 1 = true. The logical NOT operator (~) reverses the logical state (true becomes false and vice versa). You can use the ~ operator to invert a binary image, which changes all 1s to 0s, and vice versa.

> Classification and Batch Processing

  • Counts the number of nonzero entries in an array.
nnz([8 0 7 5 3 0])

image datastore

  • The imageDatastore function creates an image datastore for all the image files in a folder, but won't load them into memory until they are requested.
ds = imageDatastore("localFolder")
  • can access the datastore's properties by using a period (.).
ds.Folders
  • can use the numel function to find the total number of elements in an array.
nFiles = numel(dataFilenames)
  • can import all the images using the readall function.
img = readall(<value>)
  • The readimage function loads the nth image from an image datastore into the MATLAB workspace.
img = readimage(ds,n);
  • function processes and classifies an image using the algorithm you've developed in previous activities.
isReceipt = classifyImage(I)

  • can load each image using readimage and classify it using the local function classifyImage.

  • can use the logical array isReceipt as an index to extract the file names of the images classified as receipts.

ds.Files(isReceipt)