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Objective Construction of Ground Truth Images

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Cross Reality and Data Science in Engineering (REV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1231))

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Abstract

In the context of virtual and augmented reality, computer vision plays a pivotal role. To benchmark performance, evaluation of computer vision models, such as edge detection is essential. Traditionally this has relied on subjective analysis of the resultant images. Alternatively, models have been assessed against ground truth images. However, ground truth images are highly subjective, relying on human judging to determine the appropriate location of features. Literature complains about the lack of objective quantitative measures for model evaluation, yet no solution has been presented. Ground truth is the objective verification of properties of an image. In the context of this paper it is a data set that includes an accurate and complete representation of the edges. The subjective nature of creating ground truth images has meant that true image analysis model evaluation has been limited. Reducing the level of subjective decisions can improve the confidence level when measuring the performance of computer vision image analysis models. This work describes a new method to improve ground truth image confidence through an automated computer vision feature detection model voting system.

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Author information

Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, University of Southern Queensland, Toowoomba, Australia

    Mark Smith, Andrew Maxwell & Alexander A. Kist

  2. Discipline of ICT, University of Tasmania, Newnham, Australia

    Ananda Maiti

Authors
  1. Mark Smith
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  2. Ananda Maiti
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  3. Andrew Maxwell
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  4. Alexander A. Kist
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Corresponding author

Correspondence to Ananda Maiti .

Editor information

Editors and Affiliations

  1. Carinthia University of Applied Sciences, Villach, Austria

    Michael E. Auer

  2. College of Engineering, University of Georgia, Athens, GA, USA

    Dominik May

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Smith, M., Maiti, A., Maxwell, A., Kist, A.A. (2021). Objective Construction of Ground Truth Images. In: Auer, M., May, D. (eds) Cross Reality and Data Science in Engineering. REV 2020. Advances in Intelligent Systems and Computing, vol 1231. Springer, Cham. https://doi.org/10.1007/978-3-030-52575-0_24

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