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	Binary thresholding defense against adversarial attacks 期刊论文 OAI收割 Neurocomputing, 2021, 期号: 445, 页码: 61-71 作者: Yutong Wang; Wenwen Zhang; Tianyu Shen; Hui Yu; Fei-Yue Wang \| 收藏 \| 浏览/下载：22/0 \| 提交时间：2021/06/15 Binary thresholding Defense Adversarial training Adversarial attack
	Electro-optical imaging system identification using pseudo-random binary pattern (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Le Y.; Jian W.; Jianzhong Z.; Qiang S.; Jianzhuo L. 收藏 \| 浏览/下载：22/0 \| 提交时间：2013/03/25 A method using pseudo-random binary pattern is developed for electro-optical imaging system identification. The imaging system is taken as a stable linear time-invariant and causal filter and its transfer function is measured through the pseudo-random binary sequence impulse responses identification. A digital mirror device (DMD) light projector is developed as the target generator and wavelet thresholding is used to denoise the captured image. Pre-filtering spectral estimation algorithm with adaptive parameter selection is also proposed for the identification process overcoming the challenge brought by the size limit derived from the optical isoplantic region. Simulations and experiments are presented to show the effectiveness of the proposed method.0-64. 2010 IEEE.
	High-accuracy real-time automatic thresholding for centroid tracker (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Wang Y.; Wang Y.; Wang Y.; Wang Y.; Zhang Y. 收藏 \| 浏览/下载：37/0 \| 提交时间：2013/03/25 Many of the video image trackers today use the centroid as the tracking point. In engineering we can get several key pairs of peaks which can include the target and the background around it and use the method of Otsu to get intensity thresholds from them. According to the thresholds it give a great help for us to get a glancing size a target's centroid is computed from a binary image to reduce the processing time. Hence thresholding of gray level image to binary image is a decisive step in centroid tracking. How to choose the feat thresholds in clutter is still an intractability problem unsolved today. This paper introduces a high-accuracy real-time automatic thresholding method for centroid tracker. It works well for variety types of target tracking in clutter. The core of this method is to get the entire information contained in the histogram we can gain the binary image and get the centroid from it. To track the target so that we can compare the size of the object in the current frame with the former. If the change is little such as the number of the peaks the paper also suggests subjoining an eyeshot-window we consider the object has been tracked well. Otherwise their height just like our eyes focus on a target if the change is bigger than usual position and other properties in the histogram. Combine with this histogram analysis we will not miss it unless it is out of our eyeshot we should analyze the inflection in the histogram to find out what happened to the object. In general the impression will help us to extract the target in clutter and track it and we will wait its emergence since it has been covered. To obtain the impression what we have to do is turning the analysis into codes for the tracker to determine a feat threshold. The paper will show the steps in detail. The paper also discusses the hardware architecture which can meet the speed requirement. the paper offers a idea comes from the method of Snakes