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On the impact of illumination-invariant image pre-transformation for contemporary automotive semantic scene understanding.

Alshammari, N. and Akcay, S. and Breckon, T. (2018) 'On the impact of illumination-invariant image pre-transformation for contemporary automotive semantic scene understanding.', in 2018 IEEE Intelligent Vehicles Symposium (IV) : 26-30 June 2018, Changshu, Suzhou, China ; proceedings. Piscataway: IEEE, pp. 1027-1032.


Illumination changes in outdoor environments under non-ideal weather conditions have a negative impact on automotive scene understanding and segmentation performance. In this paper, we present an evaluation of illuminationinvariant image transforms applied to this application domain. We compare four recent transforms for illumination invariant image representation, individually and with colour hybrid images, to show that despite assumptions to contrary such invariant pre-processing can improve the state of the art in scene understanding performance. In addition, we propose a robust approach based on using an illumination-invariant image representation, combined with the chromatic component of a perceptual colour-space to improve contemporary automotive scene understanding and segmentation. By using an illumination invariant pre-process, to reduce the impact of environmental illumination changes, we show that the performance of deep convolutional neural network based scene understanding and segmentation can yet be further improved. This illuminating result enforces the need for invariant (unbiased) training sets within such deep network training and shows that even a welltrained network may still not offer truly optimal performance (if we ignore any prior data transforms attributable to a priori insight). Our approach is demonstrated over a range of example imagery where we show a notable improvement in performance using pre-processed, illumination invariant, automotive scene imagery.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:16 April 2018
Date deposited:25 June 2018
Date of first online publication:22 October 2018
Date first made open access:No date available

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