Efficient Detection of Defects in Magnetic Labyrinthine Patterns: Conclusion and References

Table of Links

Abstract and I Introduction

II. Related Works

III Methodology

IV Experiments and Results

V Conclusion and References

V. CONCLUSION

In this work, we presented a new algorithm named TMCNN to detect defects in magnetic labyrinthine patterns, contributing to a pioneering analysis in material science. Our study characterized the evolution of junctions and terminals in magnetic stripes during demagnetization procedures, aiming at better understanding defect arrangement in magnetic materials [6].

TM-CNN employs a two-stage detection procedure, combining template matching for initial detection and a convolutional network classifier for refining misdetections. This approach ensures a high detection accuracy and facilitates dataset annotation through a semi-automatic procedure.

In our experiments, TM-CNN exhibited performance superior to other techniques, achieving an impressive F1 score of 0.988. This high performance is mainly due to TMCNN’s ability to locate small and clustered objects. TM-CNN achieves almost 100% accuracy with a simple CNN classifier with less than half a million parameters and can be used even on computers without GPUs.

While TM-CNN was developed for defect detection in labyrinthine magnetic patterns, its potential applications are not limited to this field. Future research could explore the use of TM-CNN in other domains, such as identifying bifurcations in blood vessels or adapting it to other structures that can be modeled using templates.

REFERENCES

[1] Masaya Uchida, Yoshinori Onose, Yoshio Matsui, and Yoshinori Tokura. Real-space observation of helical spin order. Science, 311(5759):359–361, 2006.

[2] T. Garel and S. Doniach. Phase transitions with spontaneous modulationthe dipolar ising ferromagnet. Phys. Rev. B, 26:325–329, Jul 1982.

[3] P. Molho, J. L. Porteseil, Y. Souche, J. Gouzerh, and J. C. S. Levy. Irreversible evolution in the topology of magnetic domains (invited). Journal of Applied Physics, 61(8):4188–4193, 04 1987.

[4] M. Seul, L. R. Monar, L. O’Gorman, and R. Wolfe. Morphology and local structure in labyrinthine stripe domain phase. Science, 254(5038):1616– 1618, 1991.

[5] Naoto Nagaosa and Yoshinori Tokura. Topological properties and dynamics of magnetic skyrmions. Nature Nanotechnology, 8(12):899–911, Dec 2013.

[6] Kotaro Shimizu, Vinicius Yu Okubo, Rose Knight, Ziyuan Wang, Joseph Burton, Hae Yong Kim, Gia-Wei Chern, and B. S. Shivaram. Machine Learning Assisted Characterization of Labyrinthine Pattern Transitions. arXiv:2311.10558, 2023.

[7] Kai Briechle and Uwe D. Hanebeck. Template matching using fast normalized cross correlation. In David P. Casasent and Tien-Hsin Chao, editors, Optical Pattern Recognition XII, volume 4387, pages 95 – 102. International Society for Optics and Photonics, SPIE, 2001.

[8] P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, volume 1, pages I–I, 2001.

[9] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pages 886–893 vol. 1, 2005.

[10] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. 25, 2012.

[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition, 2015.

[12] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation, 2014.

[13] M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303–338, June 2010.

[14] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks, 2016.

[15] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection, 2016.

[16] Yang Liu, Peng Sun, Nickolas Wergeles, and Yi Shang. A survey and performance evaluation of deep learning methods for small object detection. Expert Systems with Applications, 172:114602, 2021.

[17] Ricardo H Maruta, Hae Yong Kim, Danilo R Huanca, and Walter J Salcedo. A new correlation-based granulometry algorithm with application in characterizing porous silicon nanomaterials. ECS Transactions, 31(1):273, 2010.

[18] Hae Yong Kim, Ricardo Hitoshi Maruta, Danilo Roque Huanca, and Walter Jaimes Salcedo. Correlation-based multi-shape granulometry with application in porous silicon nanomaterial characterization. Journal of Porous Materials, 20:375–385, 2013.

[19] Sidnei Alves De Araújo, Jorge Henrique Pessota, and Hae Yong Kim. Beans quality inspection using correlation-based granulometry. Engineering Applications of Artificial Intelligence, 40:84–94, 2015.

[20] Gui-Song Xia, Julie Delon, and Yann Gousseau. Accurate junction detection and characterization in natural images. International Journal of Computer Vision, 106(1):31–56, Jan 2014.

[21] Harry Pratt, Bryan M. Williams, Jae Yee Ku, Charles Vas, Emma McCann, Baidaa Al-Bander, Yitian Zhao, Frans Coenen, and Yalin Zheng. Automatic detection and distinction of retinal vessel bifurcations and crossings in colour fundus photography. Journal of Imaging, 4(1), 2018.

[22] Sheng He, Marco Wiering, and Lambert Schomaker. Junction detection in handwritten documents and its application to writer identification. Pattern Recognition, 48(12):4036–4048, 2015.

[23] M Elena Martinez-Perez, Alun D Hughes, Alice V Stanton, Simon A Thom, Neil Chapman, Anil A Bharath, and Kim H Parker. Retinal vascular tree morphology: a semi-automatic quantification. IEEE Trans Biomed Eng, 49(8):912–917, August 2002.

[24] K. Liu, Y.S. Huang, and C.Y. Suen. Identification of fork points on the skeletons of handwritten chinese characters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10):1095–1100, 1999.

[25] Chungnan Lee and Bohom Wu. A chinese-character-stroke-extraction algorithm based on contour information. Pattern Recognition, 31(6):651– 663, 1998.

[26] Michael Maire, Pablo Arbelaez, Charless Fowlkes, and Jitendra Malik. Using contours to detect and localize junctions in natural images. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1–8, 2008.

[27] Ran Su, Changming Sun, and Tuan D. Pham. Junction detection for linear structures based on hessian, correlation and shape information. Pattern Recognition, 45(10):3695–3706, 2012.

[28] R. Deriche and T. Blaszka. Recovering and characterizing image features using an efficient model based approach. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 530–535, 1993.

[29] He Zhao, Yun Sun, and Huiqi Li. Retinal vascular junction detection and classification via deep neural networks. Computer Methods and Programs in Biomedicine, 183:105096, 2020.

[30] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. Mask R-CNN. CoRR, abs/1703.06870, 2017.

[31] I B Puchalska, G A Jones, and H Jouve. A new aspect on the observation of domain structure in garnet epilayers. J. Phys. D: Appl. Phys., 11(15):L175, oct 1978.

[32] Hae Yong Kim and Sidnei Alves de Araújo. Grayscale template-matching invariant to rotation, scale, translation, brightness and contrast. In Domingo Mery and Luis Rueda, editors, Advances in Image and Video Technology, pages 100–113, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg.

[33] J. P. Lewis. Fast normalized cross-correlation. Vision Interface, 95:120, 1995.

[34] Sidnei Alves de Araújo and Hae Yong Kim. Ciratefi: An rst-invariant template matching with extension to color images. Integr. Comput.-Aided Eng., 18(1):75–90, jan 2011.

[35] Hae Yong Kim. Rotation-discriminating template matching based on fourier coefficients of radial projections with robustness to scaling and partial occlusion. Pattern Recognition, 43(3):859–872, 2010. [36] Li Deng. The mnist database of handwritten digit images for machine learning research [best of the web]. IEEE Signal Processing Magazine, 29(6):141–142, 2012.

[37] Christian Eggert, Stephan Brehm, Anton Winschel, Dan Zecha, and Rainer Lienhart. A closer look: Small object detection in faster r-cnn. In 2017 IEEE International Conference on Multimedia and Expo (ICME), pages 421–426, 2017.

[38] T. Yamada, Y. Suzuki, C. Mitsumata, K. Ono, T. Ueno, I. Obayashi, Y. Hiraoka, and M. Kotsugi. Visualization of topological defect in labyrinth magnetic domain by using persistent homology. Vacuum and Surface Science, 62(3):153–160, 3 2019.