Celik等人开发了一套基于机器视觉的自动织物缺陷检测和分类系统，对5种常见缺陷类型的织物进行检测和分类，系统的缺陷检测算法采用小波变换、双阈值二值化和形态处理等图像处理方法检测织物图像，缺陷分类算法可以检测织物图像。帮我修改一下内容降重一下

有一款自动织物缺陷检测和分类系统，能够针对5种常见的缺陷类型进行检测和分类。该系统采用了机器视觉技术中的小波变换、双阈值二值化和形态处理等图像处理方法来进行缺陷检测。此外，该系统还拥有能够检测缺陷的分类算法。

相关问题

对抗神经网络的文献有哪些

以下是对抗神经网络的文献：

1. Goodfellow, I., Shlens, J., & Szegedy, C. (2014). Explaining and Harnessing Adversarial Examples.

2. Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., & Swami, A. (2016). The limitations of deep learning in adversarial settings.

3. Kurakin, A., Goodfellow, I., & Bengio, S. (2016). Adversarial examples in the physical world.

4. Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013). Intriguing properties of neural networks.

5. Carlini, N., & Wagner, D. (2017). Towards evaluating the robustness of neural networks.

6. Nguyen, A., Yosinski, J., & Clune, J. (2015). Deep neural networks are easily fooled: High confidence predictions for unrecognizable images.

7. Madry, A., Makelov, A., Schmidt, L., Tsipras, D., & Vladu, A. (2018). Towards deep learning models resistant to adversarial attacks.

8. Brown, T. B., Mané, D., Roy, A., Abadi, M., & Gilmer, J. (2017). Adversarial patch.

9. Moosavi-Dezfooli, S. M., Fawzi, A., & Frossard, P. (2016). Deepfool: a simple and accurate method to fool deep neural networks.

10. Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., & Belongie, S. (2017). Adversarial attacks on neural network policies.