scholarly journals Improved Adaptive Vibe and the Application for Segmentation of Complex Background

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Le Chang ◽  
Zhenghua Liu ◽  
Yan Ren
Keyword(s):  
Background Noise ◽  
Adaptive Threshold ◽  
Complex Environment ◽  
Basic Algorithm ◽  
Complex Background ◽  
Texture Characteristics ◽  
Improvement Measures ◽  
Threshold Setting ◽  
Computing Speed ◽  
Vibe Algorithm

To solve the problems that basic Vibe algorithm cannot effectively eliminate the influence of background noise, follower shadow, and ghost under complex background effectively, an adaptive threshold algorithm, AdaVibe, based on the framework of basic Vibe is proposed. Aiming at the shortage of the basic algorithm, this paper puts forward some improvement measures in threshold setting, shadow eliminating, and ghost suppression. Firstly, judgment threshold takes adjustment with the changes of background. Secondly, a fast eliminating ghost algorithm depending on adaptive threshold is introduced. Finally, follower shadow is detected and inhibited effectively through the gray properties and texture characteristics. Experiments show that the proposed AdaVibe algorithm works well in complex environment without affecting computing speed and has stronger robustness and better adaptability than the basic algorithm. Meanwhile, the ghost and follower shadow can be absorbed quickly as well. Therefore, the accuracy of target detection is effectively improved.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

Adaptive Threshold Setting for Novelty Mining

Text Mining ◽  
2010 ◽  
pp. 129-148
Author(s):  
Wenyin Tang ◽  
Flora S. Tsai
Keyword(s):  
Adaptive Threshold ◽  
Threshold Setting

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.

An automatic portscan detection system with adaptive threshold setting

2010 ◽  
Vol 12 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Sang Kon Kim ◽  
Seung Ho Lee ◽  
Seung Woo Seo
Keyword(s):  
Detection System ◽  
Adaptive Threshold ◽  
Threshold Setting

Novel Code Acquisition Scheme for Weak GPS Signal

2014 ◽  
Vol 610 ◽  
pp. 915-920
Author(s):  
Xian Shan Li ◽  
Wei Guo
Keyword(s):  
False Alarm ◽  
Signal Strength ◽  
Doppler Frequency ◽  
Adaptive Threshold ◽  
Frequency Deviation ◽  
Code Acquisition ◽  
Acquisition Scheme ◽  
Simulation Results ◽  
Threshold Setting ◽  
Second Peak

In this paper, we propose a novel GPS C/A code paralleled acquisition scheme with adaptive threshold setting method. The proposed threshold setting criterion is based on the ratio between the peak and the second peak which determine the signal strength over the noise. For our scheme, the detection and false alarm probabilities are derived. When the length of the received data is 200ms, simulation results show that the proposed scheme improves the acquisition sensitivity by about 2 dB in comparison with the two conventional schemes under various Doppler frequency deviation conditions.

scholarly journals EXTRACTION OF EXTENDED SMALL-SCALE OBJECTS IN DIGITAL IMAGES

2015 ◽  
Vol XL-5/W6 ◽  
pp. 87-93 ◽  
Author(s):  
V. Y. Volkov
Keyword(s):  
Heavy Tails ◽  
Speckle Noise ◽  
Radio Observation ◽  
Adaptive Threshold ◽  
Binary Image ◽  
Small Scale ◽  
Sar Images ◽  
Television Camera ◽  
Directional Filtering ◽  
Threshold Setting

Detection and localization problem of extended small-scale objects with different shapes appears in radio observation systems which use SAR, infra-red, lidar and television camera. Intensive non-stationary background is the main difficulty for processing. Other challenge is low quality of images, blobs, blurred boundaries; in addition SAR images suffer from a serious intrinsic speckle noise. Statistics of background is not normal, it has evident skewness and heavy tails in probability density, so it is hard to identify it. The problem of extraction small-scale objects is solved here on the basis of directional filtering, adaptive thresholding and morthological analysis. New kind of masks is used which are open-ended at one side so it is possible to extract ends of line segments with unknown length. An advanced method of dynamical adaptive threshold setting is investigated which is based on isolated fragments extraction after thresholding. Hierarchy of isolated fragments on binary image is proposed for the analysis of segmentation results. It includes small-scale objects with different shape, size and orientation. The method uses extraction of isolated fragments in binary image and counting points in these fragments. Number of points in extracted fragments is normalized to the total number of points for given threshold and is used as effectiveness of extraction for these fragments. New method for adaptive threshold setting and control maximises effectiveness of extraction. It has optimality properties for objects extraction in normal noise field and shows effective results for real SAR images.

A New Method Based on Radio Frequency Spectrum for Background Noise Curve Extraction

2013 ◽  
Vol 475-476 ◽  
pp. 247-252
Author(s):  
Jia Nan Zhang ◽  
Zhi Cai Liu ◽  
Zheng Pei
Keyword(s):  
Background Noise ◽  
Adaptive Threshold ◽  
Key Factors ◽  
Electromagnetic Environment ◽  
Signal To Noise ◽  
Separation Algorithm ◽  
Smoothing Filter ◽  
Radio Monitoring ◽  
Monitoring Frequency ◽  
Radio Frequency Spectrum

The background noise is one of the key factors that interferes the radio monitoring. How to efficiently extract original signals from noises is important for the signal-to-noise separation algorithm. Recently, several algorithms have been proposed to extract signal on certain conditions, which also have their limitations. This paper first reviews and discusses the existing signal-to-noise separation algorithm. Then, we propose a piecewise adaptive threshold algorithm based on the principle of smoothing filter. The algorithm can be adjusted adaptively with the change of the external electromagnetic environment, and can be applied in a wide monitoring frequency bands.

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