Colour segmentation for multiple low dynamic range images using boosted cascaded classifiers

Andre L.C. Barczak, Teo Susnjak, Napoleon H. Reyes, Martin J. Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

This paper proposes a multi-camera system approach to real-time colour segmentation using a cascade of AdaBoost colour classifiers with error-correcting codes (AdaBoost ECC) trained on arbitrary low-dynamic range cameras. As compared to traditional High-Dynamic Range systems that require the consolidation of multiple low-dynamic range (LDR) images to produce a single HDR image or a single tone mapped image, the proposed approach feeds directly on the LDR images, and is therefore less computationally intensive. Furthermore, the proposed approach can be employed without necessitating any spectrometric calibration of the cameras. It treats each chromatic/achromatic channel of the multi-camera system as a feature vector, allowing for a multi-dimensional colour search space over a combination of different cameras and colour spaces, without any theoretical limit to the number and type of cameras. A scene plagued with spatially-varying illumination conditions was used to test the efficacy of the proposed system. A dataset of over 89,000 samples were used for training the AdaBoost classifiers to learn eight colour categories in a matter of minutes. The experiments showed that under these extreme illumination conditions, the classifier using three cameras achieved 93% correct classification compared to less than 75% when using a single camera.

Original languageEnglish
Title of host publicationProceedings of 2013 28th International Conference on Image and Vision Computing New Zealand, IVCNZ 2013
Pages136-141
Number of pages6
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event28th International Conference on Image and Vision Computing New Zealand, IVCNZ 2013 - Wellington, New Zealand
Duration: 27 Nov 201329 Nov 2013
Conference number: 28th

Publication series

NameInternational Conference Image and Vision Computing New Zealand
ISSN (Print)2151-2191
ISSN (Electronic)2151-2205

Conference

Conference28th International Conference on Image and Vision Computing New Zealand, IVCNZ 2013
Abbreviated titleIVCNZ
Country/TerritoryNew Zealand
CityWellington
Period27/11/1329/11/13

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