Abstract
For image processing applications, an initial step is usually extracting features from the target image. Those features can be lines, curves, circles, circular arcs and other shapes. The Hough transform is a reliable and widely used method for straight line and circle detection, especially when the image is noisy. However, techniques of Hough transform for detecting lines and circles are different; when detecting circles it usually requires a three-dimensional parameter space while detecting straight lines only requires two. Higher dimensional parameter transforms suffer from high storage and computational requirements. However, in the two dimensional Hough transform space, straight lines and circles yield patterns with different shapes. By analysing the shape of patterns within the Hough transform space it is possible to reconstruct the circles in image space. This paper proposes a new circle detection method based on analysing the pattern shapes within a two-dimensional line Hough transform space. This method has been evaluated by a simulation of detecting multiple circles and a group of real-world images. From the evaluation our method shows ability for detecting multiple circles in an image with mild noise.
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Chang, Y., Bailey, D., Le Moan, S. (2020). The Shape of Patterns Tells More. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_6
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DOI: https://doi.org/10.1007/978-3-030-41299-9_6
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