Automatic commemorative coin recognition system by using image processing techniques Runhou Chen，Danpeng Cheng, Jian Guo, Yawen Shen, Caokun Yang Advisor: Professor B.D. Barkana 1Department of Electrical Engineering University of Bridgeport Introduction Coin collection is a hobby that has many benefits. There is a tremendous number of foreign students and tourists visiting the United States every year. Some people would like to buy souvenirs. A set of collectible US coins is one the most popular souvenirs items. In 1990, the 50 State Quarters was released as a series of circulating commemorative coins by the United States Mint began. This set of quarter not only includes all the 50 states of the United States, but also includes the District of Columbia and the five inhabited U.S territories: Puerto Rico, Guam, America Samoa, the United States Virgin Islands and the Commonwealth of the North Mariana Islands. In this work, we design a commemorative coin recognition system, which can be used in parking meters as well as vending machines. Our system may lower the cost of quarter collection and alleviate the human work to identify different quarters. We prepared an image database for commemorative US coins that represent different states and national parks. A recognition system is designed by employing image processing techniques and supervised classifier. Preprocessing, classification are the two major stages in our system. In the preprocessing stage, coin image is being reconstructed and if there is any, artifacts are removed from the images. In the classification stage, subtraction, cross correlation, and tophat filter techniques are used. Applied Procedure Database: We have 102 quarter reverses including fifty state program and the American beautiful sightseeing program, 52 dollar reverses including 40 presidential dollars and 12 native American dollars, 6 penny reverses, 5 nickel reverses, 1 nickel reverse. Also, we have correspondent obverse for each coin. These coins not only show the vast territories, but also represents the unique diversification and multi variation of the United States. Figure 2. Connecticut quarter obverse and reverse Image reconstruction: Two cases are considered: (1) The input image is non-centered. (2) The size of the input image is smaller than the size of the images in our database. The proposed image reconstruction method address these two cases. There are five steps: Find the four points r1, r2, c1, and c2. (2) in the database image. Find the four points r3, r4, c3, and c4 in the input image. Take out the input image we need. The size should be (r4-r3)*(c4-c3). Reconstruct the input image which is taken out at step 3. The new size is (r2-r1)*(c1-c2). Reconstruct the input_new_image. Figure 3. Image Reconstruction Confronted to the noises and edges that are not clear enough to detect in input images, we employ Low-pass (LPF) and high-pass filters (HPF). LPF: Noise and small artifacts are removed by using a LPF. An image is smoothed by decreasing the disparity between pixel values by averaging nearby pixels. A low-pass filter tends to retain the low frequency information within an image while reducing the high frequency information. HPF: A high-pass filter can be used to make an image appear sharper by highlighting the edges. HPF filters emphasize fine details in the image – exactly the opposite of the low-pass filter. Moreover, High-pass filtering works in exactly the same way as low-pass filtering, just by using a different convolution kernel. We filtered the input and database images by the designed LPF and HPF in order to make sure that the images after the pre-processing are affected in the same manner. Figure 1. Coin Detection Cross-correlation： Cross-correlation is a measure of similarity of two series as a function of the displacement of one relative to the other. normxcorr2: Normalized 2-D cross-correlation. The similarity depends on the location of the peak in the cross-correlation matrix. If two pictures are exactly the same, the peak value in figure is about one. Subtraction: Image subtraction (pixel subtraction) takes two images as input and generates an output image whose pixel values are simply those of the first image minus the corresponding pixel values from the second image. Figure 4. Cross correlation Tophat filter: Top-hat transform is an operation that extracts small elements and details from given images. The size or width of the elements that are extracted by the top-hat transforms can be controlled by the choice of the structuring element. The bigger the latter, the larger the elements extracted. Figure 5. The decision by the system Conclusions Our project focused on the detection of different coins. The input image is matched with the image in the database. which proves the success of this project. Image reconstruction, low pass filtering, high pass filtering, and various methodologies are the essential steps of our coin recognition system. Subtraction method works well but only when two images are highly similar in pixel distribution. Cross – correlation is more flexible compared to the restrain of subtraction method. Tophat filter works well along with the subtraction method.