Slides
Reading Materials
1. P. Viola and M.J. Jones, Robust real-time face detection, IJCV' 04
2. Y. Freund and R.E. Schapire,
A Decision-Theoretic Generalization of on-Line Learning and an
Application to Boosting, Journal of Computer and System
Sciences,1995
1. M. Turk and A. Pentland, Eigenfaces for recognition, Journal of Cognitive Neuroscience' 91
2.
PCADemo: a matlab program used in our lectures to demonstrate
the basic concepts related to PCA
3.
FaceRecByEigenFace: a matlab demo composed by me to illustrate
how to make use of the eigen-face approach to perform face
recognition. Very simple and straightforward. (You need to run "training.m"
at first)
1. J.
Wright et al.,
Robust face recognition via sparse representation, IEEE PAMI' 09
2. L. Zhang et al.,
Sparse representation or collaborative representation: which helps
face recognition?, ICCV' 11
3.
Implementations of several l1-minization solvers, provided by
Allen Yang (EECS, Berkeley)
4.
CRC_RLS: a matlab demo program implementing the CRC_RLS based
face recognition method.
5. Basis pursuit denoising. In applied mathematics and statistics, the l1-minimization problem is named basis pursuit denoising (BPDN)
6. David L. Donoho and Yaakov Tsaig, Fast Solution of l1-norm Minimization Problems When the Solution May be Sparse, 2006
1. K. He et al., Deep Residual Learning for Image Recognition, CVPR 2016
2. G. Huang et al., Densely Connected Convolutional Networks, CVPR 2017
3. J. Redmon et al., Yolo: 9000 better, faster, stronger, CVPR 2017
4. N. Ma et al., ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design, ECCV 2018
5. J. Redmon et al., YOLOv3:
An Incremental Improvement, arXiv, 2018
6. Github for YOLOv4, https://github.com/AlexeyAB/darknet
7. Ultralytics YOLOv8, https://ultralytics.com/yolov8
8. J.R. Terven et al.,
A comprehensive review of YOLO: From YOLOv1 to YOLOv8 and beyond,
arXiv:2304.0050, 2023.
9.
典型卷积神经网络模型结构的演进
1. Xuan Shao, Lin Zhang* et al., "MOFISSLAM: A multi-object semantic SLAM system with front-view, inertial and surround-view sensors for indoor parking", IEEE Trans. Circuits and Systems for Video Technology, vol. 32, no. 7, pp. 4788-4803, 2022.
2. Tianjun Zhang, Nlong Zhao, Ying Shen, Xuan Shao, Lin Zhang*, and Yicong Zhou, “ROECS: A Robust Semi-direct Pipeline Towards Online Extrinsics Correction of the Surround-view System”, in: Proc. ACM MM, pp. 3153-3161, 2021.
3. Lin Zhang et al., "Vision-based parking-slot detection: A DCNN-based approach and a large-scale benchmark dataset", IEEE Trans. Image Processing, vol. 27, no. 11, pp. 5350-5364, 2018.
1. K. Madsen, H.B. Nielsen, O. Tingleff, Methods for nonlinear least squares, Technical University of Denmark, 2004
2. Lin Zhang, Understanding Singular Value Decomposition, Tongji University, 2020
1. "Part I: Theory" of the book "S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge Press, 2004".
1. A. Kowalczyk, Support Vector Machines Succinctly, Syncfusion, 2017
2. Demos for SVM, https://github.com/csLinZhang/CVBook/tree/main/chapter-17-SVM