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Multi-directional colour edge detector using linear quaternion system convolution

Yasmin, Shagufta and Sangwine, Stephen J (2018) 'Multi-directional colour edge detector using linear quaternion system convolution.' IET Image Processing, 12 (7). pp. 1111-1116. ISSN 1751-9659

IPR-2017-0921-FINAL(1).pdf - Accepted Version

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A new linear colour image filter based on linear quaternion systems (LQSs) is introduced. It detects horizontal, vertical, left- and right-diagonal edges with a single LQS convolution mask. The proposed filter is a canonic minimal filter of four LQS filters, each with different angles of rotation combined parallel wise. Different angles of rotation are a key features of the new filter such that horizontal, vertical, left, and right-diagonal LQS filter masks rotate pixels through angles π/2, 5π/2, 3π/2, and 7π/2, respectively. Although, the four LQS masks are combined parallel to make a single LQS mask but derived using four quaternion convolutions, one for each direction of edges, the LQS filter produces a result without the combination of results from four separate edge detectors. This methodology could be generalised to design more elaborate LQS filters to perform other geometric operations on colour image pixels. The proposed filter translates smoothly changing colours to different shades of grey and produces coloured edges in multiple directions, where there is a sudden change of colour in the original image. Another key idea of the proposed filter is that it is linear because it operates in homogeneous coordinates.

Item Type: Article
Uncontrolled Keywords: edge detection; image colour analysis; image filtering; multidirectional colour edge detector; linear colour image filter; linear quaternion systems; right-diagonal edges; left-diagonal edges; horizontal-diagonal edges; vertical-diagonal edges; single-LQS convolution mask; quaternion convolution; edge direction; LQS filters; colour image pixels; homogeneous coordinates; grey shades
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 26 Apr 2018 11:19
Last Modified: 06 Jan 2022 13:48

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