McDonald, M. and Dunbar, I., Market segmentation: how to do it, how to profit from it, Wiley, New Jersey, 2012.
2. Chang, C.-C. and Wang, L.-L., Color texture segmentation for clothing in a computer-aided fashion design system. Image Vision Comput., 14, 9, 685–702, 1996.
3. Ridzuan, S., Omar, Z., Sheikh, U., A review of content-based video retrieval techniques for person identification. ELEKTRIKA- J. Electr. Eng., 18, 49–56, 12 2019.
4. Dong, T. and Li, J., Software design of cloth design and simulation system, in: Proc. of IEEE International Conference on Computer-Aided Industrial Design Conceptual Design, pp. 605–609, 2009.
5. Cychnerski, J., Brzeski, A., Boguszewski, A., Marmolowski, M., Trojanowicz, M., Clothes detection and classification using convolutional neural networks, in: Proc. of IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8, 2017.
6. Yang, M. and Yu, K., Real-time clothing recognition in surveillance videos, in: Proc. of IEEE International Conference on Image Processing, pp. 2937–2940, 2011.
7. Huang, C., Chen, J., Pan, Y., Lai, H., Yin, J., Huang, Q., Clothing landmark detection using deep networks with prior of key point associations. Proc. IEEE Trans. Cybern., 49, 10, 3744–3754, 2019.
8. Sidnev, A., Trushkov, A., Kazakov, M., Korolev, I., Sorokin, V., Deepmark: One-shot clothing detection, in: Proc. of IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3201–3204, 2019.
9. He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask r-cnn, in: Proc. of IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988, 2017.
10. Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A., Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no.1, 172–186, July 2019.
11. Bu, Q., Zeng, K., Wang, R., Feng, J., Multi-depth dilated network for fashion landmark detection with batch-level online hard keypoint mining. Image Vision Comput., 99, 103930, 2020.
12. Yu, W., Liang, X., Gong, K., Jiang, C., Xiao, N., Lin, L., Layout-graph reasoning for fashion landmark detection, in: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019, pp. 2932–2940.
13. Ge, Y., Zhang, R., Wang, X., Tang, X., Luo, P., Deepfashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images, in: Proc. of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019, pp. 5332–5340.
14. Hara, K., Jagadeesh, V., Piramuthu, R., Fashion apparel detection: The role of deep convolutional neural network and pose-dependent priors. IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–9, 2016.
15. Kita, Y., Ueshiba, T., Neo, E.S., Kita, N., Clothes state recognition using 3d observed data, in: Proc. of International Conference on Robotics and Automation, pp. 1220–1225, 2009.
16. Sutoyo, R., Prayoga, B., Fifilia, D., Suryani, Shodiq, M., The implementation of hand detection and recognition to help presentation processes. Proc. Comput. Sci., 59, 550–558, 2015, International Conference on Computer Science and Computational Intelligence (ICCSCI 2015).
17. Modanwal, G. and Sarawadekar, K., A robust wrist point detection algorithm using geometric features. Pattern Recognit. Lett., 110, 72–78, 2018.
18. Godbehere, A.B., Matsukawa, A., Goldberg, K., Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation, in: Proc. of American Control Conference (ACC), pp. 4305–4312, 2012.
19. Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell., 39, 6, 1137–1149, 2017.
20. Nascimento, J.C., Abrantes, A.J., Marques, J.S., An algorithm for centroid-based tracking of moving objects, in: Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 1999, vol. 6, pp. 3305–3308.
21. Google colab. https://colab.research.google.com/.
1 *Corresponding author: [email protected]
2 †Corresponding author: [email protected]
4
Intelligent Computing on Complex Numbers for Cryptographic Applications
Ni Ni Hla1* and Tun Myat Aung2†
1Faculty of Computing, University of Computer Studies, Yangon (UCSY), Shwe Pyi Thar Township, Yangon, Myanmar
2University of Information Technology (UIT), Hlaing Township, Yangon, Myanmar
Abstract
This chapter focuses on matrix algebra and elliptic curve arithmetic computation, going under the combination of modular number crunching and complex number crunching. It explains the intelligent computation of non-linear transformations using residue matrices and elliptic curve arithmetic in the plane made of complex numbers, which may be used in computing science areas dealing with the applications in cryptography to make them more stable. In classical ciphers, elliptic curve cryptography, and quantum cryptography, their computing properties in mathematics on the plane made of complex numbers are used to construct cryptographic non-linear transformation techniques.
Keywords: Complex number, cryptography, descryption, encryption, elliptic curve, matrix algebra, modular arithmetic, signature
4.1 Introduction
Cryptography is the computing science of hiding sensitive information by scientifically transforming it from understandable to un-understandable format in order to ensure secrecy, credibility, and precision during data transfers over public communication networks. Residue matrices and elliptic curve arithmetic based on modular number crunching are often utilized by numerical calculations in encryption and authentication. In recent years, well-known ciphers used non-linear cryptographic transformation methods focused on residue matrices and elliptic curve arithmetic [12].
A plane made of complex numbers is becoming more valuable in computing science areas that deal with the applications in cryptography in order to make them more stable. Quantum proposition introduced in the terms of vector spaces is a computational framework that applied to complex numbers, and may be utilized in quantum variants of asymmetric key distribution protocols in this direction [14, 15, 17]. As a result, the mathematical properties of residue matrices and elliptic curve arithmetic on the plane made of complex numbers are observed to use them in the applications of cryptographic science.
The aim of this chapter is to extend non-linear cryptographic transformation techniques by using mathematical properties of residue matrices and elliptic curve arithmetic over the complex plane using modular arithmetic.
4.2 Modular Arithmetic
4.2.1
