On extending the capability of the image ray transform

Abstract

A conventional approach to image analysis is to separately perform feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points) using the Hough transform. The original Image Ray Transform (IRT) demonstrated capability to emphasise structures at a low level. Here we extend the IRT to add shape specificity that makes it select specific shapes rather than just edges; the new capability is achieved by addition of a single parameter that controls which shape is selected by the extended IRT. The extended approach can then perform low-and high-level feature extraction simultaneously. We show how the IRT process can be extended to focus on chosen shapes such as lines and circles. We also suggest an additional extension of IRT to detect shapes of chosen colours. The new approach uses the CIEL*a*b* colour model within the IRT’s light ray analogy. The capability of the extended IRT using colour information is evaluated for correct shape location by conventional methods such as the Hough Transform. We analyse performance with images from the Caltech-256 dataset and show that the new approach can indeed select chosen shapes and colours. We also show how the new approach has the capability to detect objects with specific shape and colour. Further research will aim to capitalise on the new extraction ability to extend descriptive capability.

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Identifiers

ORCID for Mark Nixon: orcid.org/0000-0002-9174-5934

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