Yuz tasvirini Gabor filtri asosida segmentlash
Боковая панель статьи
Основное содержимое статьи
Аннотация
Yuzni tanib olish muhim qismlaridan biri sifatida yuz tasvirini segmentatsiyalash inson belgilarini aniqlashning asosiy yo‘nalishiga aylangan. Ushbu maqolada AdaBoost algoritmi va Gabor tekstura tahlili algoritmidan foydalanilib, bir nechta yuzlarni o‘z ichiga olgan tasvir segmentatsiyalanadi, bu esa yuz tasvirini segmentatsiyalashdagi noto‘g‘ri aniqlash darajasini samarali kamaytirilgan, yuz tasvirini segmentatsiyalashda, yuz ma’lumotlarini o‘z ichiga olgan tasvir avval Gabor algoritmi orqali tekstura bo‘yicha tahlil qilinadi va turli teriga o‘xshash sohalarni turli bo‘sag‘alari o‘rnatiladi, bu orqali tasvirni fon ma’lumotlaridagi teriga o‘xshash sohalar olib tashlanadi. So‘ngra AdaBoost algoritmi yordamida yuz sohalari aniqlanadi va aniqlangan yuz sohalari segmentatsiyalanadi. Tajribalar taklif etilgan usulni tasvirdagi yuzlarni tez va aniq segmentatsiyalay olishi hamda yo‘qotish va noto‘g‘ri aniqlash darajasini samarali kamaytirishini ko‘rsatdi.
Информация о статье
Это произведение доступно по лицензии Creative Commons «Attribution» («Атрибуция») 4.0 Всемирная.
Библиографические ссылки
X. Y. Xu, “Survey of face recognition technology,” Electronic Test, vol. 2015, no. 5, pp. 885–894, 2015.
Y. P. Yin, Z. Ying, Z. Dan et al., “Face segmentation using CRFs based on multiple feature fusion,” Electronic Measurement Technology, vol. 38, no. 6, pp. 54–59, 2015.
X. Wu, J. Zhao, and H. Wang, “Face segmentation based on level set and improved DBM prior shape,” Progress in Artificial Intelligence, vol. 8, no. 46, pp. 1–13, 2019.
S. Wazarkar, B. N. Keshavamurthy, and A. Hussain, “Region-based segmentation of social images using soft KNN algorithm,” Procedia Computer Science, vol. 125, pp. 93–98, 2018.
D. Liu, L. F. Wang, and H. F. Zhang, “Design of statistical analysis model based on big data,” Software Guide, vol. 15, no. 7, pp. 28–30, 2016.
W. Liu, S. Liu, R. C. Bai, X. Zhou, and D. N. Zhou, “Research of mutual learning neural network training method,” Chinese Journal of Computers, vol. 40, no. 6, pp. 1291–1308, 2017.
Y. Zhang, B. Li, H. Lu, A. Irie, and X. Ruan, “Sample-specific SVM learning for person re-identification,” in Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
J. Z. Cao and A. G. Song, “Research on the texture image segmentation method based on Markov random field,” Chinese Journal of Scientific Instrument, vol. 36, no. 4, pp. 776–786, 2015.
L. G. Ixaru, G. Vanden Berghe, and H. De Meyer, “Exponentially fitted variable two-step BDF algorithm for first order ODEs,” Computer Physics Communications, vol. 150, no. 2, pp. 116–128, 2003.
J. Jun and Z. G. Lin, “A knowledge-based approach for fast human face detection,” Journal of Image and Graphics, vol. 7, no. 1, pp. 6–10, 2002.
Y. Ming. Research on 3D face recognition based on invariant features, Ph.D. thesis, Beijing Jiaotong University, Beijing, China, 2012.
G. Yang and T. S. Huang, “Human face detection in a complex background,” Pattern Recognition, vol. 27, no.1, pp. 53–63, 1994.
Z. P. Gong, “A face detection method based on skin color,” Journal of Luohe Vocational and Technical College, vol. 12, no. 2, pp. 42–44, 2013.
Y. Ma, Face Detection Method Based on Color and Template Matching, Dalian University of Technology, Dalian, China, 2006.
Z. H. Zeng and Y. M. Zhang, “3D face standard model based on OpenGL,” Mechanical Engineering and Automation, vol. 34, no. 4, pp. 34–36, 2007.
Liang and H. Z. Ai, “Multi-template-matching based single face detection,” Journal of Image and Graphics, vol. 4, no. 10, pp. 825–830, 1999.
J. Zhu, H. Zou, S. Rosset, and T. Hastie, “Multi-class AdaBoost,” Statistics & Its Interface, vol. 2, no. 3, pp. 349–360, 2006.
Y. Hong-Yu, Y. U. Lei, and W. Sen, “Face recognition with Gabor texture features,” Application Research of Computers, vol. 28, no. 10, pp. 3974–3976, 2011.
L. I. Wei, H. E. Peng-Ju, and Y. Heng, “AdaBoost pedestrian detection algorithm based on dual-threshold motion area segmentation,” Application Research of Computers, vol. 29, no. 9, pp. 3571–3570, 2012.
S. M. Hu, D. Liang, G. Y. Yang, G.-W. Yang, and W.-Y. Zhou, “Jittor: a novel deep learning framework with meta-operators and unified graph execution,” Science China Information Sciences, vol. 63, no. 12, pp. 1–21, 2020.