 ## Tutorial 6 Useful Image Processing Techniques Matlab For-Free PDF

• Date:22 Feb 2020
• Views:27
• Pages:14
• Size:807.26 KB

Share Pdf : Tutorial 6 Useful Image Processing Techniques Matlab For

Download and Preview : Tutorial 6 Useful Image Processing Techniques Matlab For

Report CopyRight/DMCA Form For : Tutorial 6 Useful Image Processing Techniques Matlab For

Transcription:

determined by the combination of the red green and blue intensities stored in each color plane at the. pixel s location, To determine the color of the pixel at 2 3 you would look at the RGB triplet stored in 2 3 1 3 Suppose. 2 3 1 contains the value 0 5176 2 3 2 contains 0 1608 and 2 3 3 contains 0 0627 The color for the. pixel at 2 3 is,0 5176 0 1608 0 0627,Image Coordinate Systems. MATLAB stores most images as two dimensional arrays i e matrices in which each element of the matrix. corresponds to a single pixel in the displayed image. Often the most convenient method for expressing locations in an image is to use pixel indices The image is. treated as a grid of discrete elements ordered from top to bottom and left to right as illustrated by the. following figure, For pixel indices the row increases downward while the column increases to the right Pixel indices are. integer values and range from 1 to the length of the row or column. There is a one to one correspondence between pixel indices and subscripts for the first two matrix. dimensions in MATLAB For example the data for the pixel in the fifth row second column is stored in the. matrix element 5 2 You use normal MATLAB matrix subscripting to access values of individual pixels For. example the MATLAB code, returns the value of the pixel at row 2 column 15 of the image I. Instructions for the exercises In this tutorial the code blocks are provided in sequential order Every code. block is explained When you go through this tutorial add code blocks sequentially to your MATLAB script. and run it Add the code blocks one by one Try to understand the first code block and see its output. workspace values and figures Then add the second code block and understand it and so on If you run. the entire script at once you will not understand the individual steps. Exercise 1,Basic Image Import Processing and Export.
Open a new MATLAB script and save it as Tute 6 1 m Include the following commands in your script and. run them lowlight 1 jpg file is given for you,Step 1 Read and Display an Image. Read an image into the workspace using the imread command. I imread lowlight 1 jpg,Display the image using the imshow function. Step 2 Check How the Image Appears in the Workspace. Check how the imread function stores the image data in the workspace using the whos command You can. also check the variable in the Workspace Browser,Step 3 Improve Image Contrast. View the distribution of image pixel intensities The image lowlight 1 jpg is a low contrast image To see the. distribution of intensities in the image create a histogram by calling the imhist function Precede the call. to imhist with the figure command so that the histogram does not overwrite the display of the image I in. the current figure window Notice how the histogram indicates that the intensity range of the image is. rather narrow The range does not cover the potential range of 0 255 and is missing the high values that. would result in good contrast, Improve the contrast in an image using the histeq function Histogram equalization spreads the intensity. values over the full range of the image Display the image. I2 histeq I, Call the imhist function again to create a histogram of the equalized image I2 If you compare the two.
histograms you can see that the histogram of I2 is more spread out over the entire range than the. histogram of I,Step 4 Write the Adjusted Image to a Disk File. Write the newly adjusted image I2 to a disk file using the imwrite function This example includes the. filename extension png in the file name so the imwrite function writes the image to a file in Portable. Network Graphics PNG format but you can specify other formats. imwrite I2 lowlight 1 png,Exercise 2,Convert Between Image Types Resizing Cropping. The Image Processing toolbox includes many functions that you can use to convert an image from one type. to another, Create a new MATLAB script and save it as Tute 6 2 m. Step 1 Convert a truecolor image to a grayscale image. RGB imread peppers png,imshow RGB, Step 2 Convert the RGB image to a grayscale image and display it. I rgb2gray RGB,Resize an Image with imresize Function.
Step 3 Specify the Magnification Value, Resize the image I using the imresize function and assign the resized image to J Specify the magnifying. factor as 0 75 Google and find out how to do this,J write your command here. Display the original image next to the reduced version. imshowpair I J montage,Step 4 Specify the Size of the Output Image. Resize the image again this time specifying the desired size of the output image rather than a. magnification value Pass imresize a vector 200 250 that contains the number of rows and columns in the. output image If the specified size does not produce the same aspect ratio as the input image the output. image will be distorted,K write your command here,figure imshow K. Crop an Image, To extract a rectangular portion of an image use the imcrop function Using imcrop you can specify the.
crop region interactively using the mouse or programmatically by specifying the size and position of the. crop region, Call imcrop specifying the image to crop I and the crop rectangle imcrop returns the cropped image in J. The image coordinates are given in pixels Write your code to crop the onion image Assign the cropped. image to L,L write your command here,figure imshow L. Exercise 3,Morphological Operations, Morphology is a broad set of image processing operations that process images based on shapes In a. morphological operation each pixel in the image is adjusted based on the value of other pixels in its. neighborhood By choosing the size and shape of the neighborhood you can construct a morphological. operation that is sensitive to specific shapes in the input image. Morphological Dilation and Erosion, The most basic morphological operations are dilation and erosion Dilation adds pixels to the boundaries of. objects in an image while erosion removes pixels on object boundaries. Morphological dilation makes objects more visible and fills in small holes in objects. Morphological erosion removes islands and small objects so that only substantive objects remain. Use Morphological Opening to Extract Large Image Features. You can use morphological opening to remove small objects from an image while preserving the shape and. size of larger objects in the image, In this example you use morphological opening on an image of a circuitboard to remove all the circuit lines.
from the image The output image contains only the rectangular shapes of the microchips. Create a new MATLAB script and save it as Tute 6 3 m. Open an Image In One Step, You can use the imopen function to perform erosion and dilation in one step. Step 1 Read the image into the workspace and display it. BW1 imread circbw tif,figure imshow BW1,Step 2 Create a structuring element. The structuring element should be large enough to remove the lines when you erode the image but not. large enough to remove the rectangles It should consist of all 1s so it removes everything but large. contiguous patches of foreground pixels,SE strel rectangle 40 30. Step 3 Open the image,BW2 imopen BW1 SE,figure imshow BW2. Open an Image By Performing Erosion Then Dilation, Step 4 You can also perform erosion and dilation sequentially.
Erode the image with the structuring element This removes all the lines but also shrinks the rectangles. BW3 imerode BW1 SE,figure imshow BW3, To restore the rectangles to their original sizes dilate the eroded image using the same structuring. element SE,BW4 imdilate BW3 SE,figure imshow BW4, By performing the operations sequentially you have the flexibility to change the structuring element. Create a different structuring element and dilate the eroded image using the new structuring element. SE strel diamond 15,BW5 imdilate BW3 SE,figure imshow BW5. Exercise 4,Image Region Properties, Image regions also called objects connected components or blobs can be contiguous or discontiguous. The following figure shows a binary image with two contiguous regions. A region in an image can have properties such as an area center of mass orientation and bounding box. To calculate these properties for regions and many more in an image you can use. the regionprops function, Estimate Center and Radii of Circular Objects and Plot Circles.
Estimate the center and radii of circular objects in an image and use this information to plot circles on the. image In this example regionprops returns the measured region properties in a table. Create a new MATLAB script and save it as Tute 6 4 m. Step 1 Read an image into workspace,a imread circlesBrightDark png. figure imshow a,Step 2 Turn the input image into a binary image. figure imshow bw,title Image with Circles, Step 3 Calculate properties of regions in the image and return the data in a table. stats regionprops table bw Centroid,MajorAxisLength MinorAxisLength. Step 4 Get centers and radii of the circles,centers stats Centroid.
diameters mean stats MajorAxisLength stats MinorAxisLength 2. radii diameters 2,Plot the circles,viscircles centers radii. Exercise 5,Identifying Round Objects, Create a new MATLAB script and save it as Tute 6 5 m. This example shows how to classify objects based on their roundness using bwboundaries a boundary. tracing routine,Step 1 Read Image,Read in pills etc png. RGB imread pillsetc png,Figure imshow RGB,Step 2 Threshold the Image. Convert the image to black and white in order to prepare for boundary tracing using bwboundaries. I rgb2gray RGB,bw imbinarize I,figure imshow bw,Step 3 Remove the Noise.
Using morphology functions remove pixels which do not belong to the objects of interest. Remove all object containing fewer than 30 pixels,bw bwareaopen bw 30. figure imshow bw,Fill a gap in the pen s cap,se strel disk 2. bw imclose bw se,figure imshow bw, Fill any holes so that regionprops can be used to estimate the area enclosed by each of the boundaries. bw imfill bw holes,figure imshow bw,Step 4 Find the Boundaries. Concentrate only on the exterior boundaries Option noholes will accelerate the processing by. preventing bwboundaries from searching for inner contours. B L bwboundaries bw noholes,Display the label matrix and draw each boundary.
figure imshow label2rgb L jet 5 5 5,for k 1 length B. boundary B k,plot boundary 2 boundary 1 w LineWidth 2. Step 5 Determine which Objects are Round, Estimate each object s area and perimeter Use these results to form a simple metric indicating the. roundness of an object, This metric is equal to 1 only for a circle and it is less than one for any other shape The discrimination. process can be controlled by setting an appropriate threshold In this example use a threshold of 0 94 so. that only the pills will be classified as round, Use regionprops to obtain estimates of the area for all of the objects Notice that the label matrix returned.
by bwboundaries can be reused by regionprops, Reference Mathworks online tutorials Relevant web pages are linked to the blue color titles 03 May. Tutorial 6 Useful Image Processing Techniques MATLAB for Your TurtleBot3 Practical Project Images in MATLAB The basic data structure in MATLAB is the array an ordered set of real or complex elements This object is