Matt Cabot Rory Taca QR CODES

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2 Figure 1: An image of a large QR code, found on Google images The essential idea behind the projective transformation is taken by comparing points on two images through a theoretical lens. In order to do this, we needed data points corresponding to the corners of the QR code to transform onto a theoretical 200 x 200 blank image. The original plan was to use SIFT to find the corners of the original QR code. This was mostly successful, but ultimately proved useless, as we were unsuccessful in finding a way to take the feature points from the SIFT and turn those into usable corner points. Our solution to this issue was less than elegant the user manually inputs the corner points. While this is not ideal, it does not seem like a completely unreasonable way to handle this process with a program in its early iterations. After the corner points are found, we use helpful commands in MATLAB to build a transform and then apply it to the original image, which, when applied to figure 1, results in the image seen in figure 2.

3 Figure 2: Figure 1, after transformation The image is then cropped by manually selecting the upper left corner. This results in the QR code being a 200 x 200 image that is then passed over to a function that helps make it more readable. The function in our reader, toblackwhite, takes an image and produces a black and white version. A number of tools were used to do this. First, the RGB image is simply converted to the grayscale version. In grayscale, every pixel is defined by its luminosity, a number from 0 to 1. Closer to 0 is darker, and closer to 1 is lighter. The next thing that is done is an adjustment for contrast via the imadjust function, which makes the image higher contrast (raising the whites, and lowering the blacks). Then, the weiner2 function is used, which helps to remove noise from the image. A filter is applied, the unsharp filter, that actually sharpens the image, to improve the border between black and white. Finally, the function runs through the remaining pixel and raises it to 1 (white) if it is above.65 and 0 (black) if it is below. The threshold was obtained

4 through trial and error. The results are fairly good, as you can see from the transition from figure 3 to figure 4. Figure 3: The unprocessed QR code from figure 1 Figure 4: The QR code from figure 3, after being run through the toblackwhite function

5 The results are nice, and as we found out later in the project, this processing was essential to turning getting accurate results during the decoding. The process is not perfect, as there is some fuzzing in certain areas, and it does not handle the part of the code that was brightened by lights (which could be commonplace with flash on phones). From that point, the code was ready to be analyzed. In our QR code, we eliminated code between the corner markers, leaving a 6 x 6 of characters available to be worked with, as we decided that the 200 x 200 would be cut 8 x 8, and each corner marker took up 1 of those grid spots. The algorithm is fairly simple it would go into each of the 6 x 6 squares, and break that square up into a 5 x 5 square. It would average each one of the squares in the inner grid, and put a 1 into a matrix if the value was above a threshold, or 0 if the value was below. That matrix would then be compared to predetermined matrices to determine the value of the character, and that character would be placed in a matrix to be deciphered later. Figure 5: A photo example of our QR Code, reading HELLO CS374 HELLO CS374 The encoding scheme was fairly simple. Each character was a 5x5 matrix, so we essentially used binary representations of the characters. A = 1, B = 2 Z = 26. The binary was on the top row, with white = 0 and black = 1. On the third row, we put numerals, 1 9, 0, also using binary (but with 0 encoded as 10). Blank spaces are treated as such. The overall results from this project were a mixed bag. With the image in Figure 5, we got quality results about 50% of the time, with the errors usually being garbled text. In other cases, with darker, more blurry images, the reader consistently would fail to pick up any of the text. One of the biggest boosts to accuracy is picking the corner points well cutting off too much often ended in a failed run. Our encoding proved successful, but it might be better to shrink the 5x5 matrix to something smaller, because it would allow for more characters to be stored, and the 5x5 matrix we use is overkill for the 36 alphanumeric characters we are using.

6 Figure 6: Successful output of figure 5 There are a number of ways to improve the quality of the reader and to take the project to the next logical step. Some of the obvious ways are to automatically get the corner points using SIFT. This eliminates user error in corner selection, which proved to be one of the main factors in the success of the program. Another couple of ways to improve accuracy is to make the toblackwhite function better, as well as creating something of a predictive algorithm to fill in any holes. Another logical step is to make the algorithm more robust with placement, as well as using the standard QR code encoding, to read standard QR codes instead of just the custom ones that we employed. With this as a starting point, a full-featured accurate QR code reader could be created.

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