An Adaptive Threshold Method for Hyperspectral Target Detection

2006 ◽  
Author(s):  
J. Broadwater ◽  
R. Chellappa
Keyword(s):  
Target Detection ◽  
Adaptive Threshold ◽  
Threshold Method

scholarly journals A Two-Dimensional Adaptive Target Detection Algorithm in the Compressive Domain

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 567 ◽  
Author(s):  
Wen-Huan Cao ◽  
Shu-Cai Huang
Keyword(s):  
Compressive Sensing ◽  
Target Detection ◽  
Adaptive Threshold ◽  
Computational Time ◽  
Gram Matrix ◽  
Small Target ◽  
Two Dimensional ◽  
Threshold Method ◽  
Small Target Detection ◽  
Infrared Small Target

By applying compressive sensing to infrared imaging systems, the sampling and transmitting time can be remarkably reduced. Therefore, in order to meet the real-time requirements of infrared small target detection tasks in the remote sensing field, many approaches based on compressive sensing have been proposed. However, these approaches need to reconstruct the image from the compressive domain before detecting targets, which is inefficient due to the complex recovery algorithms. To overcome this drawback, in this paper, we propose a two-dimensional adaptive threshold algorithm based on compressive sensing for infrared small target detection. Instead of processing the reconstructed image, our algorithm focuses on directly detecting the target in the compressive domain, which reduces both the time and memory requirements for image recovery. First, we directly subtract the spatial background image in the compressive domain of the original image sampled by the two-dimensional measurement model. Then, we use the properties of the Gram matrix to decode the subtracted image for further processing. Finally, we detect the targets by employing the advanced adaptive threshold method to the decoded image. Experiments show that our algorithm can achieve an average 100% detection rate, with a false alarm rate lower than 0.4%, and the computational time is within 0.3 s, on average.

13‐4: Adaptive Threshold Method for Dither Noise Reduction in Response Time Acceleration for 8K LCD TV

2021 ◽  
Vol 52 (1) ◽  
pp. 161-164
Author(s):  
Ho-Joon Chung ◽  
Hong-Ki Kwon ◽  
Jin-Yong Park ◽  
Tae-Woo Kim ◽  
Hyeon-Su Park ◽  
...  
Keyword(s):  
Response Time ◽  
Noise Reduction ◽  
Adaptive Threshold ◽  
Threshold Method

Visual interpretability analysis of Deep CNNs using an Adaptive Threshold method on Diabetic Retinopathy images

2021 ◽  
Author(s):  
George Ioannou ◽  
Tasos Papagiannis ◽  
Thanos Tagaris ◽  
Georgios Alexandridis ◽  
Andreas Stafylopatis
Keyword(s):  
Diabetic Retinopathy ◽  
Adaptive Threshold ◽  
Threshold Method

scholarly journals Wavelet-Based Contourlet Transform and Kurtosis Map for Infrared Small Target Detection in Complex Background

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 755
Author(s):  
He Wang ◽  
Yunhong Xin
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Saliency Map ◽  
Adaptive Threshold ◽  
Contourlet Transform ◽  
Small Target ◽  
Small Target Detection ◽  
Complex Background ◽  
Infrared Small Target

Wavelet-based Contourlet transform (WBCT) is a typical Multi-scale Geometric Analysis (MGA) method, it is a powerful technique to suppress background and enhance the edge of target. However, in the small target detection with the complex background, WBCT always lead to a high false alarm rate. In this paper, we present an efficient and robust method which utilizes WBCT method in conjunction with kurtosis model for the infrared small target detection in complex background. We mainly made two contributions. The first, WBCT method is introduced as a preprocessing step, and meanwhile we present an adaptive threshold selection strategy for the selection of WBCT coefficients of different scales and different directions, as a result, the most background clutters are suppressed in this stage. The second, a kurtosis saliency map is obtained by using a local kurtosis operator. In the kurtosis saliency map, a slide window and its corresponding mean and variance is defined to locate the area where target exists, and subsequently an adaptive threshold segment mechanism is utilized to pick out the small target from the selected area. Extensive experimental results demonstrate that, compared with the contrast methods, the proposed method can achieve satisfactory performance, and it is superior in detection rate, false alarm rate and ROC curve especially in complex background.

Comparison of the early aberration reporting system (EARS) W2 methods to an adaptive threshold method

2011 ◽  
Vol 30 (5) ◽  
pp. 489-504 ◽  
Author(s):  
John L. Szarka ◽  
Linmin Gan ◽  
William H. Woodall
Keyword(s):  
Reporting System ◽  
Adaptive Threshold ◽  
Threshold Method

Adaptive threshold method for recovered images of FMT

2018 ◽  
Vol 35 (2) ◽  
pp. 256 ◽  
Author(s):  
Huangjian Yi ◽  
Hongna Wei ◽  
Jinye Peng ◽  
Yuqing Hou ◽  
Xiaowei He
Keyword(s):  
Adaptive Threshold ◽  
Threshold Method

Locating Method of DPM 2D Barcode Image

2014 ◽  
Vol 989-994 ◽  
pp. 3865-3867 ◽  
Author(s):  
He Rong Zheng ◽  
Bing Shan ◽  
Zhi Liu
Keyword(s):  
Adaptive Threshold ◽  
High Light ◽  
Threshold Method ◽  
Filtering Method ◽  
2D Barcode ◽  
Otsu Method ◽  
Homomorphic Filtering ◽  
Positioning Method ◽  
Convex Hull Algorithm ◽  
Segmentation Problem

This paper studies the positioning method of DPM 2D barcode image. Because of the reflective problems of metal surface, the barcode image gotten will have uneven illumination, high light issues. In this paper, the homomorphic filtering method is used to enhance the image. Then the gradient projection is used to positioning the barcode region preliminary. We use an adaptive threshold method composed of Otsu method and neighborhood threshold method to solve the error segmentation problem. Finally the convex hull algorithm is applied to locate barcode region. Experiment results show that this method can effectively locate the barcode region quickly.

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