1 |
Y. K. C. Yeon Ho Chu, "A Deep Learning based IOT Device Recognition System," Journal of the Semiconductor & Display Technology, vol. 18, no. 2, pp. 1-5, 2019.
|
2 |
K. He, G. Gkioxari, P. Doll'ar, and R. Girshick, "Mask R-CNN," Facebook AI Research (FAIR), 2018.
|
3 |
T. Nakazawa and D. V. Kulkarni, "Wafer Map Defect Pattern Classification and Image Retrieval Using Convolutional Neural Network," IEEE Transactions on Semiconductor Manufacturing, vol. 31, no. 2, pp. 309-314, 2018, doi: 10.1109/tsm.2018.2795466.
DOI
|
4 |
M. Everingham, L. Gool, C. Williams, J. Winn, and A. Zisserman, "The Pascal Visual Object Classes (VOC) Challenge," International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, 2010, doi: 10.1007/s11263-009-0275-4.
DOI
|
5 |
R. Girshick, "Fast R-CNN," Microsoft Research, 2015.
|
6 |
R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," arXiv.org, 2014.
|
7 |
J. Dai, Y. Li, K. He, and J. Sun, "R-FCN: Object Detection via Region-based Fully Convolutional Networks," arXiv.org, 2016.
|
8 |
W. Liu, D. Anguelov, C. Szegedy, S. Reed, F. Cheng-Yang, and A. Berg, "SSD: Single Shot MultiBox Detector," vol. 9905, ed. Ithaca, 2016.
|
9 |
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," vol. 2016-, ed, 2016, pp. 779-788.
|
10 |
L. Tsung-Yi, P. Goyal, R. Girshick, H. Kaiming, and P. Dollar, "Focal Loss for Dense Object Detection," vol. 2017-, ed, 2017, pp. 2999-3007.
|
11 |
T. Kong, A. Yao, Y. Chen, and F. Sun, "HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection," arXiv.org, 2016.
|
12 |
T. Kong, F. Sun, A. Yao, H. Liu, M. Lu, and Y. Chen, "RON: Reverse Connection with Objectness Prior Networks for Object Detection," arXiv.org, 2017.
|
13 |
Z. Cai and N. Vasconcelos, "Cascade R-CNN: Delving into High Quality Object Detection," 2017.
|
14 |
S. Bell, C. Zitnick, K. Bala, and R. Girshick, "Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks," arXiv.org, 2015.
|
15 |
P. Chao et al., "MegDet: A Large Mini-Batch Object Detector," arXiv.org, 2018.
|
16 |
B. Singh and L. Davis, "An Analysis of Scale Invariance in Object Detection - SNIP," arXiv.org, 2018.
|
17 |
Y.-H. L. Hyochang Ahn, "A Research of CNN-based Object Detection for Multiple Object Tracking in Image," Journal of the Semiconductor & Display Technology, vol. 18, no. 3, pp. 110-114, 2019.
|
18 |
E. Shelhamer, J. Long, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 4, pp. 640-651, 2017, doi: 10.1109/TPAMI.2016.2572683.
DOI
|
19 |
B. Hariharan, P. Arbelaez, R. Girshick, and J. Malik, "Hypercolumns for Object Segmentation and Finegrained Localization," arXiv.org, 2015.
|
20 |
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille, "Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs," arXiv.org, 2016.
|
21 |
D. Jia, D. Wei, R. Socher, L. Li-Jia, L. Kai, and F.-F. Li, "ImageNet: A large-scale hierarchical image database," ed, 2009, pp. 248-255.
|
22 |
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Microsoft Research, 2015.
|
23 |
W.-Y. Kang and B.-T. Zhang, "Image Classification using Convolutional Neural Networks Based on Discriminative Features," KOREA INFORMATION SCIENCE SOCIETY, pp. 645-647 (3 pages), 2016.12.
|
24 |
G. Huang, Z. Liu, and K. Weinberger, "Densely Connected Convolutional Networks," arXiv.org, 2018.
|
25 |
A. L. KABADE and D. V. G. Sangam, "Canny edge detection algorithm," International Journal of Advanced Research in Electronics and Communication Engineering, vol. 5, no. 5, May 2016.
|
26 |
O. R. Vincent and O. Folorunso, "A Descriptive Algorithm for Sobel Image Edge Detection," Proceedings of Informing Science & IT Education Conference (InSITE) 2009.
|
27 |
J. Revaud, J. Almazan, R. Sampaio de Rezende, and C. de Souza, "Learning with Average Precision: Training Image Retrieval with a Listwise Loss," arXiv.org, 2019.
|