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A Pipelined Approach to Deal with Image Distortion in Computer Vision

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Intelligent Systems (BRACIS 2020)

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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References

  1. Afifi, M., Derpanis, K.G., Ommer, B., Brown, M.S.: Learning to correct overexposed and underexposed photos. arXiv preprint arXiv:2003.11596 (2020)

  2. Chen, C., Seff, A., Kornhauser, A., Xiao, J.: DeepDriving: learning affordance for direct perception in autonomous driving. In: The IEEE International Conference on Computer Vision (ICCV), December 2015

    Google Scholar 

  3. Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)

    Google Scholar 

  4. Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. Int. J. Comput. Vision 88(2), 303–338 (2010)

    Article  Google Scholar 

  5. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  6. Hou, Y., et al.: NLH: a blind pixel-level non-local method for real-world image denoising. IEEE Trans. Image Process. 29, 5121–5135 (2020)

    Article  Google Scholar 

  7. Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  8. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  9. Iocchi, L., Holz, D., Ruiz-del Solar, J., Sugiura, K., Van Der Zant, T.: RoboCup@Home: analysis and results of evolving competitions for domestic and service robots. Artif. Intell. 229, 258–281 (2015)

    Article  MathSciNet  Google Scholar 

  10. Karim, R., Islam, M.A., Mohammed, N., Bruce, N.D.: On the robustness of deep learning models to universal adversarial attack. In: 2018 15th Conference on Computer and Robot Vision (CRV), pp. 55–62. IEEE (2018)

    Google Scholar 

  11. Krizhevsky, A., Nair, V., Hinton, G.: The CIFAR-10 dataset. http://www.cs.toronto.edu/kriz/cifar.html 55 (2014)

  12. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  13. Liu, D., Wen, B., Jiao, J., Liu, X., Wang, Z., Huang, T.S.: Connecting image denoising and high-level vision tasks via deep learning. IEEE Trans. Image Process. 29, 3695–3706 (2020)

    Article  Google Scholar 

  14. Lopez, A., Paredes, R., Quiroz, D., Trovato, G., Cuellar, F.: Robotman: a security robot for human-robot interaction. In: 2017 18th International Conference on Advanced Robotics (ICAR), pp. 7–12, July 2017. https://doi.org/10.1109/ICAR.2017.8023489

  15. Lv, F., Lu, F.: Attention-guided low-light image enhancement. arXiv preprint arXiv:1908.00682 (2019)

  16. Maity, A., Pattanaik, A., Sagnika, S., Pani, S.: A comparative study on approaches to speckle noise reduction in images. In: 2015 International Conference on Computational Intelligence and Networks, pp. 148–155. IEEE (2015)

    Google Scholar 

  17. Molina, M., Frau, P., Maravall, D.: A collaborative approach for surface inspection using aerial robots and computer vision. Sensors 18(3), 893 (2018)

    Article  Google Scholar 

  18. Piyathilaka, L., Kodagoda, S.: Human activity recognition for domestic robots. In: Mejias, L., Corke, P., Roberts, J. (eds.) Field and Service Robotics. STAR, vol. 105, pp. 395–408. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-07488-7_27

    Chapter  Google Scholar 

  19. Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? arXiv preprint arXiv:1902.10811 (2019)

  20. Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y

    Article  MathSciNet  Google Scholar 

  21. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  22. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  23. Soares, L.B., et al.: Seam tracking and welding bead geometry analysis for autonomous welding robot. In: 2017 Latin American Robotics Symposium (LARS) and 2017 Brazilian Symposium on Robotics (SBR), pp. 1–6. IEEE (2017)

    Google Scholar 

  24. Steffens, C.R., Huttner, V., Messias, L.R.V., Drews, P.L.J., Botelho, S.S.C., Guerra, R.S.: CNN-based luminance and color correction for ill-exposed images. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 3252–3256, September 2019. https://doi.org/10.1109/ICIP.2019.8803546

  25. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, Inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  26. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)

    Google Scholar 

  27. Szeliski, R.: Computer Vision: Algorithms and Applications. TCS. Springer, London (2010). https://doi.org/10.1007/978-1-84882-935-0

    Book  MATH  Google Scholar 

  28. Talbot, H., Phelippeau, H., Akil, M., Bara, S.: Efficient Poisson denoising for photography. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 3881–3884. IEEE (2009)

    Google Scholar 

  29. Taqi, F., Al-Langawi, F., Abdulraheem, H., El-Abd, M.: A cherry-tomato harvesting robot. In: 2017 18th International Conference on Advanced Robotics (ICAR), pp. 463–468, July 2017. https://doi.org/10.1109/ICAR.2017.8023650

  30. Therrien, R., Doyle, S.: Role of training data variability on classifier performance and generalizability. In: Medical Imaging 2018: Digital Pathology, vol. 10581, p. 1058109. International Society for Optics and Photonics (2018). https://doi.org/10.1117/12.2293919

  31. Verma, R., Ali, J.: A comparative study of various types of image noise and efficient noise removal techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(10) (2013)

    Google Scholar 

  32. van der Walt, S., et al.: The scikit-image contributors: Scikit-image: image processing in Python. PeerJ 2, e453 (2014). https://doi.org/10.7717/peerj.453

    Article  Google Scholar 

  33. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  34. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26(7), 3142–3155 (2017)

    Article  MathSciNet  Google Scholar 

  35. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697–8710 (2018)

    Google Scholar 

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Correspondence to Cristiano Rafael Steffens .

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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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