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MATLAB has been a cornerstone in my exploration of image processing, providing a robust platform to implement and analyze various techniques. I have worked extensively on fundamental and advanced methods such as edge detection, thresholding, histogram analysis, and logical operations like OR and XOR for image enhancement and feature extraction. Leveraging MATLAB's image processing toolbox, I have performed skeletonization to simplify image structures and extract critical features, enabling detailed analysis and pattern recognition. These experiences have not only enhanced my understanding of digital image processing principles but also empowered me to address practical challenges, making MATLAB an integral part of my journey in robotics and machine vision applications
basic of image processing
basic of image processing
Print the image size in the command mode saying ‘no. of rows’ and ‘no. of columns’ in a given image. Find the pixel value from the center of image using the output obtained from ‘size’ command
Separate three-layer (RGB) matrices into separate single layer matrix and display three images using subplot command in a single window. Give title as name of the components
Using imresize command ,reduce the size of the image by 50% , increase the size by 150 % and using subplot command display three images in a single window. write your inference by looking at the image. Get the size of resized image and verify the same
Convert the given RGB image into gray scale image using rgb2grey and save it as ‘grayball.jpg’. Display both the images in the same window.
Display the frequency of each pixel value in gray scale image using imhist command. Graphically find the frequency of occurrence of pixel value ‘127’.
Convert the gray ball into binary image using im2bw, display three images (RGB, gray and binary) in a single window. Change the threshold value to 0.2, 0.5 and 0.7. observe the result and write your inference
Rotate the image by 45deg using imrotate and observe result.
crop the rectangular image from the pixel location with starting point 30,50 for a width , height of 135 , 220 using imcrop command and display original image with the cropped image in a single window
ex01.pdfbanner of even and odd rows of image
banner of even and odd rows of image
Create a digital image with your initials & first letter on the banner size 50* 100.
Sub-sample any mages with ODD/EVEN lines and show your output.
ex02.pdfCreation of Digital Images, Arithmetic operations of it
Creation of Digital Images, Arithmetic operations of it
Read any two images and do the following
Arithmetic operations (Addition, Subtraction, Multiplication and division)
Logical operation (AND,OR, EX-OR)
Create an image with random nos generated from 0-255 in a matrix of size 500* 500
ex03 arthimatic_and_logic.pdfMean & Median Filtering
Mean & Median Filtering
Read an gray scale image, do the average
filtering with subset of 3x3,5x5 and 11x11…observe the changes. Repeat the same averaging exercise in for loop with the count of 5,10,15.. Observe the changes
For the same image, apply median filtering and write the best filter based on clarity.
ex04 noise_VIKNESSHWAR A B.pdfHistogram equalization
Histogram equalization
i) Download any image. Using imaging software convert the image as dark, bright, low contrast, and high contrast. Use the images modified for plotting histogram plotting, equalize it and compare its histogram with original and equalized one.
ii) Use histogram specifications to convert dark image to bright image
ex05 histogram.pdfEdge Detection
Edge Detection
Find the edge detection of the given image using different operators and write the conclusion about it
ex06 diffrent_operation.pdfDynamic Thresholding, Plotting edge along x, y axis separately
Dynamic Thresholding, Plotting edge along x, y axis separately
Pl. implement dynamic thresholding & compare its performance with Global thresholding
ex07 Dynamic Thresholding, Plotting edge along x, y axis separately.pdfTrace region boundaries in binary image
Trace region boundaries in binary image
Try with B = bwboundaries(BW) on on Binary images and compute the length of boundaries. Display the image with the boundaries overlaid. Add the region number next to every boundary (based on the label matrix). Use the zoom tool to read individual labels.
ex08 binary_image.pdfFeature extraction
Feature extraction
Pl. draw any one of geometrical size( Rectangle, right angle triangle) in varying size and orientation in MS-Paint. Use that binary images for the experiment.
Extract min. 5 features from each of it and compare and conclude the features which is invariant to size and rotation
ex09 feature_extraction.pdfSkeleton
Skeleton
Pl. draw any one of geometrical size( Rectangle, right angle triangle) in varying size and orientation in MS-Paint. . Try to find the skeleton of that images
ex 10 Skeleton.pdfPage updated
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