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# CA08 - neighbor classifier You can use 2 images for...

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University of Tehran Department of Electrical and Computer Engineering Prof. H. Soltanian-Zadeh Fall 2009 Digital Image Processing Assignment #8- Representation and Object Recognition Problem 1 – Read rice.png from MATLAB demo images. i. Convert this image to a binary image that background is 0. Show the result. Can you separate all grains? ii. Put a label to each object (grain) and count number of grains in image. iii. Compute perimeter, area and diameter of each grain and plot their histogram. iv. Our goal is a quality control task. You must separate small grains from other using arbitrary criteria. You can do this task by using area and/or perimeter and/or diameter of grains. Please explain your arbitrary criteria. How many grains satisfy your criteria (high quality grains) and how many grains have to be rejected? Problem 2 – You can see 10 handwritten signature classes in "signature" folder. There are 3 samples per class. Our goal is handwritten signature identification based on nearest
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Unformatted text preview: neighbor classifier. You can use 2 images for training samples and 1 image for test. i. Compute center of gravity for each signature image. Could you classify different classes using this feature without any misclassification? ii. Compute width and height of signatures. First, classify different classes based on only width of signatures. Then do classification only based on height of signatures. Compute accuracy of classification for each feature. iii. Which feature is the most important? Why? iv. Now, classify signature classes using 3 features (center of gravity, width and height of signature)? How much is accuracy of this approach? v. Could you propose some other features for above problem? Apply your proposed features and compute accuracy of classification. . Hints: You can use some beneficial MATLAB functions: • graythresh • im2bw • bwlabel • bwboundaries • bwperim...
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