A ROBUST SOFTWARE BARCODE READER USING THE HOUGH TRANSFORM PDF

Be sure about what you are trying to detect. In more detail: 1: As noted in other answers, converting straight to grayscale discards too much information - any circles with a similar brightness to the background will be lost. Much better to consider the colour channels either in isolation or in a different colour space. There are pretty much two ways to go here: perform HoughCircles on each pre-processed channel in isolation, then combine results, or, process the channels, then combine them, then operate HoughCircles.

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Be sure about what you are trying to detect. In more detail: 1: As noted in other answers, converting straight to grayscale discards too much information - any circles with a similar brightness to the background will be lost. Much better to consider the colour channels either in isolation or in a different colour space. There are pretty much two ways to go here: perform HoughCircles on each pre-processed channel in isolation, then combine results, or, process the channels, then combine them, then operate HoughCircles.

Be wary of over saturating the image when combining, I use cv. This is also useful as it reduces some noise. Trouble is the sweet spot is going to be different for every image. I think the best approach here is to make set a condition and do a search through different param2 values until this condition is met.

Your images show non-overlapping circles, and when param2 is too low we typically get loads of overlapping circles. So I suggest searching for the: maximum number of non-overlapping, and non-contained circles So we keep calling HoughCircles with different values of param2 until this is met.

I do this in my example below, just by incrementing param2 until it reaches the threshold assumption. It would be way faster and fairly easy to do if you perform a binary search to find when this is met, but you need to be careful with exception handling as opencv often throws a errors for innocent looking values of param2 at least on my installation. A different condition that would we very useful to match against would be the number of circles.

The more stuff we can tell our model the easy a task we can make it to detect circles. For example, do we know: The number of circles.

Their sizes. Where they can be in an image. Maybe if you want to detect things that are not circles, a different approach such as Tim Lukins may be better. For example a large solid disc after processing can appear a ring, so HughesCircles may find the inner ring. Furthermore even the docs note that These 4 points are estimates of points on the circumference. Use these four estimates to determine a more accurate radius, and centre position! This could be generalised by using an expanding ring instead of four rays.

Results The code at end does pretty good quite a lot of the time, these examples were done with code as shown: Detects all circles in your first image: How the pre-processed image looks before canny filter is applied different colour circles are highly visible : Detects all but two blobs in second image: Altered second image blobs are circle-afied, and large oval made more circular, thus improving detection , all detected: Does pretty well in detecting centres in this Kandinsky painting I cannot find concentric rings due to he boundary condition.

CreateImage orig. CreateMat orig. Dilate channel, channel, None, 1 cv. HoughCircles processed, storage, cv. Circle output, x, y , 1, cv. Circle output, x, y , Radius, cv. And rrr, ggg, rrr cv. And rrr, bbb, processed cv. Canny processed, processed, 5, 70, 3 smooth to reduce noise a bit more cv. Smooth processed, processed, cv.

ShowImage "original with circles", output cv. WaitKey 0.

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Nikohn In order to assess our system in other realistic situations, we gathered a number of images taken from two different cellphones, and created three new data sets. These points are extracted by thresholding the transformed image taking a suitable threshold. In our approach, as barcoee in the figure, first original image is converted to edge image. The intensity profile from the segment highlighted in red on the blue scanline is shown in the plot, where the black lines represent the symbols as output by our algorithm.

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Reading Challenging Barcodes with Cameras

Abstract: Automatic identification technology such as RFID promises to connect physical objects with virtual representations or even computational capabilities. However, even though RFID tags are continuously falling in price, their widespread use on consumer items is still several years away, rende Much more ubiquitous are printed bar codes, yet so far their recognition required either specialized scanner equipment, custom-tailored bar codes or costly commercial licenses — all equally significant deployment hurdles. These approaches are often used in professional image recognition software, as the offer very good recognition rates. However, their requirements in terms of system resources can be too demanding fo Abstract — Camera cellphones have become ubiquitous, thus opening a plethora of opportunities for mobile vision applications.

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Classification and Decoding of Barcodes: An Image Processing Approach

Part of the Lecture Notes in Electrical Engineering book series LNEE, volume Abstract Barcodes are being widely used in many fields of applications of great commercial value, which provide a means of representing data in machine readable format. Various symbologies exist to map the data into barcodes. Image based barcode readers provide many advantages over laser scanners in terms of orientation independence, image archiving and high read rate performance even when barcodes are damaged, distorted, blurred, scratched, low-height and low-contrast. The availability of imaging technology provides a platform for decoding barcode rather than the use of the conventional laser scanner which is lack of mobility. In this paper, image based technique for classification of the given 1-D barcode into respective symbology and its decoding have been proposed. The proposed method first localizes the barcodes and subsequently, a classifier which classifies the given 1-D barcode into respective symbology is applied.

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