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A Pipelined Approach to Deal with Image Distortion in Computer Vision

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Intelligent Systems (BRACIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12319))

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Abstract

Image classification is a well-established problem in computer vision. Most state-of-the-art models rely on Convolutional Neural Networks to achieve near-human performance in that task. However, CNNs have shown to be susceptible to image manipulation, which undermines the trustability of perception systems. This property is critical, especially in unmanned systems, autonomous vehicles, and scenarios where light cannot be controlled. We investigate the robustness of several Deep-Learning based image recognition models and how the accuracy is affected by several distinct image distortions. The distortions include ill-exposure, low-range image sensors, and common noise types. Furthermore, we also propose and evaluate an image pipeline designed to minimize image distortion before the image classification is performed. Results show that most CNN models are marginally affected by mild miss-exposure and Shot noise. On the one hand, the proposed pipeline can provide significant gain on miss-exposed images. On the other hand, harsh miss-exposure, signal-dependent noise, and impulse noise, incur in a high impact on all evaluated models.

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001.

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Authors and Affiliations

  1. Centro de Ciências Computacionais, Universidade Federal do Rio Grande – FURG, Rio Grande, Brazil

    Cristiano Rafael Steffens, Lucas Ricardo Vieira Messias, Paulo Lilles Jorge Drews-Jr & Silvia Silva da Costa Botelho

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  1. Cristiano Rafael Steffens
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  2. Lucas Ricardo Vieira Messias
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  3. Paulo Lilles Jorge Drews-Jr
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  4. Silvia Silva da Costa Botelho
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Corresponding author

Correspondence to Cristiano Rafael Steffens .

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Editors and Affiliations

  1. Federal University of São Carlos, São Carlos, Brazil

    Ricardo Cerri

  2. Federal University of ABC, Santo Andre, Brazil

    Ronaldo C. Prati

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Steffens, C.R., Messias, L.R.V., Drews-Jr, P.L.J., da Costa Botelho, S.S. (2020). A Pipelined Approach to Deal with Image Distortion in Computer Vision. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-61377-8_15

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