lab7 - Image Processing II: Filtering Images using...

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Image Processing II: Filtering Images using Convolution Pre-Lab: Read this handout before going to your assigned lab section. Download the matlab file containing the images from WebCT. Verification: Show the TA your written definitions of the key terms at the beginning of the lab. The noise reduction and edge filtering must be completed during your assigned lab time and demonstrated to your TA for verification. Lab Report: Your report should include the results from Sections 2.1,2.2, and 2.3 with images and explanations. You need to label the axes of your plots and include a title for every plot. In order to keep track of plots, include your plot in-lined within your report. In your report, describe what you did and what your output sounded like. Include your Matlab code in an appendix. 1. Introduction Image signals can be filtered in a manner similar to one-dimensional signals. Images vary in intensity across the image just as an audio signal varies in time. In images we use the term "spatial frequency". A high spatial frequency means that the image is changing quickly in intensity (e.g. a striped shirt). An object with a low spatial frequency only changes slightly over many pixels. A low pass filter will blur the image by removing the high frequency details. A high pass filter will enhance the edges and other areas that change quickly in intensity. One way of filtering is to first filter the rows with a one dimensional filter and then filter the columns with a one dimensional filter. Two dimensional filters can also be used that use a neighborhood of pixels to filter the signal. Filtering can help remove noise from an image by averaging out the effects of random noise fluctuations. High pass filters help detect edges and sudden spatial changes in an image. 2. Image Filtering This lab will start by filtering a single row of an image using the conv.m function in Matlab. The next step will be to extend this procedure to the entire image. Read in the file of image data for this lab and display the image in matrix cicada in grayscale. This can be done by loading the jpg images and converting them to grayscale: >cicada=imread(‘cicada.jpg’);%read in image >gray_cicada = rgb2gray(cicada);%convert to grayscale image >gray_cicada = double(gray_cicada);% convert image to a type suitable for filtering You may also find the images in the .MAT file in ip2_images which is available on WebCT. Load the file using the command: > load ip2_images -mat
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Extract the 50 th row of the image and plot the intensity values. Filter this row using an FIR averaging filter that averages seven samples. Plot the result on the same plot. Is the filtered output rougher or smoother than the input? >row = cicada(50,:); %extracts 50th row
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lab7 - Image Processing II: Filtering Images using...

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