A clutter-calibrated Hough transform for the estimation of directional structure and dominant directions in grey-level images
Keywords: Big data, image analysis, non-parametric smoothing, scale-space methods, thresholding
AbstractIncreasing amounts of image data are being routinely collected as part of the big-data revolution, with applications as diverse as automated security surveillance and dynamic medical imaging. To make best use of the data, the analyses must be automatic and rapid. Simple image properties can be used to highlight specific features in an initial screening or form input to elaborate classification techniques. A key stage in any image analysis is the identification of structure amongst the noise. It is important to realise that noise can be localized, independent and random, or it could contain small-scale structure which, in some ways, resembles the important features---this is called clutter. This paper uses the concept of the Hough transform to convert grey-level images into a more useful feature space representation. This space is searched for high density regions to identify dominant structure whilst taking into account micro-line clutter. Further, a directional distribution is introduced and a resulting dominant direct is proposed as a single structural summary.Many examples of simulated and real data images are used to illustrate the proposed techniques.
How to Cite
Hamed, F., & Aykroyd, R. (2017). A clutter-calibrated Hough transform for the estimation of directional structure and dominant directions in grey-level images. Statistics, Optimization & Information Computing, 5(4), 348-359. https://doi.org/10.19139/soic.v5i4.338
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