EE569_2010_Fall_HW2_v4

EE569_2010_Fall_HW2_v4 - EE569 Digital Image Processing...

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EE569 Digital Image Processing Version 4 (09/26/2010) 1 HOMEWORK #2 Edge Detection, Morphological Processing, and Digital Halftoning Issued: 09/17/2010 Due: 10/08/2010 Please refer to Homework Guidelines and MATLAB Function Guidelines for more information about how to complete and submit the homework. Also, refer to the USC policy on academic integrity and penalties for cheating and plagiarism - these rules will be strictly enforced. Problem 1: Edge Detection (30%) In this problem, an edge map is defined as a binary image, which is composed of pixel values of either 0 (edge) or 255 (background). Figure 1 : elaine.raw (a) Basic Edge Detection Algorithms (10%) First, implement the following two basic edge detection algorithms: (1) First-order Derivative Method (2) Second-order Derivative Method with Zero Crossing Apply these edge detection algorithms to the elaine.raw image (256x256) shown above in Figure 1 and discuss the edge map results in your report. Describe each edge detection method and compare your results for each method. Also, discuss how and why you choose any parameters that are used in these two algorithms.
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EE569 Digital Image Processing Version 4 (09/26/2010) 2 (b) Advanced Edge Detection Algorithms (10%) In this problem, you will examine some of the more advanced algorithms, which should also be applied to the elaine.raw image in Figure 1. (1) Adaptive Threshold Method - To improve the edge maps obtained in (a), we will apply locally different parameters to the image. Consider four sub-images of size 128x128, i.e., the top-left, top-right, bottom-left, and bottom-right portions of the original image. Decide the appropriate parameter for each region and apply each of the edge detection algorithms used in (a). Discuss how you choose these parameters. Show the edge maps and compare the results with those in (a). (2) Pre-Processing and Post-Processing - Implement any pre-processing and/or post-processing techniques you feel would improve the results of (a), then apply your revised algorithm to elaine.raw. Compare these results to those from part (a). State clearly the steps you used to implement these changes in your report and explain how these steps improve the edge detection algorithm in (a). (c) Canny Edge Detector (10%) (1) Convert the rope.raw image shown in Figure 2 into a grayscale image. Apply the 1 st and 2 nd order edge detection algorithms from part (a) to the grayscale image. Figure 2 : rope.raw (2) Apply the provided Canny Edge Detector program (MATLAB and C/C++ versions are provided) to elaine.raw and the grayscale version of rope.raw. Examine the source code and explain how this edge detector works in your report. (3)
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EE569_2010_Fall_HW2_v4 - EE569 Digital Image Processing...

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