scholarly journals Feature Tracking for Target Identification in Acoustic Image Sequences

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jue Gao ◽  
Ya Gu ◽  
Peiyi Zhu
Keyword(s):  
Feature Tracking ◽  
Statistical Data ◽  
Target Identification ◽  
Image Sequences ◽  
Tracking Algorithm ◽  
False Alarms ◽  
Quantitative Investigation ◽  
Acoustic Image ◽  
Feature Detectors ◽  
Underwater Target

This paper proposes underwater target identification with local features and a feature tracking algorithm for acoustic image sequences. Feature detectors and descriptors are key to feature tracking. Their performance in underwater scene is evaluated by the change of multitarget parameters. A comprehensive quantitative investigation into the performance of feature tracking is thereby presented. Experimental results confirm that the proposed algorithm can accurately track potential targets and determine whether the potential targets are static targets, dynamic targets, or false alarms according to the tracking trajectories and statistical data.

scholarly journals Underwater Target Perception in Local HOS Space

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jue Gao ◽  
Peiyi Zhu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Detection Rate ◽  
Region Of Interest ◽  
Image Sequences ◽  
Acoustic Imaging ◽  
Acoustic Image ◽  
Acoustic Images ◽  
Underwater Target ◽  
High Order Statistics

In this paper, we propose an underwater target perception architecture, which adopts the three-stage processing including underwater scene acoustic imaging, local high-order statistics (HOS) space conversion, and region-of-interest (ROI) detection. After analysing the problem of the underwater targets represented by the acoustic images, the unique cube structure of the target in local skewness space is noticed, which is used as a clue to develop the ROI detection of underwater scenes. In order to restore the actual appearance of the ROI as much as possible, the focus processing is explored to achieve the target reconstruction. When the target size and number are unknown, using an uncertain theoretical template can achieve a better target reconstruction effect. The performance of the proposed method in terms of SNR, detection rate, and false alarm rate is verified by experiments with several acoustic image sequences. Moreover, target perception architecture is general and can be generalized to a wider range of underwater applications.

scholarly journals Feature Tracking Algorithm for Circumferential Strain using High Frame Rate Echocardiography

2016 ◽  
Author(s):  
Martin Vandborg Andersen ◽  
Cooper Moore ◽  
Samuel Schmidt ◽  
Peter Sogaard ◽  
Johannes Struijk ◽  
...  
Keyword(s):  
Feature Tracking ◽  
Circumferential Strain ◽  
Tracking Algorithm ◽  
Frame Rate ◽  
High Frame Rate

scholarly journals UAV IMAGE BLUR – ITS INFLUENCE AND WAYS TO CORRECT IT

2015 ◽  
Vol XL-1/W4 ◽  
pp. 33-39 ◽  
Author(s):  
T. Sieberth ◽  
R. Wackrow ◽  
J. H. Chandler
Keyword(s):  
Image Processing ◽  
Target Identification ◽  
Image Sequences ◽  
Identification Measurements ◽  
Filtering Technique ◽  
Coordinate Measurements ◽  
Active Research ◽  
Uav Image ◽  
Blurred Images ◽  
Degradation Effect

Unmanned aerial vehicles (UAVs) have become an interesting and active research topic in photogrammetry. Current research is based on image sequences acquired by UAVs which have a high ground resolution and good spectral resolution due to low flight altitudes combined with a high-resolution camera. One of the main problems preventing full automation of data processing of UAV imagery is the unknown degradation effect of blur caused by camera movement during image acquisition. <br><br> The purpose of this paper is to analyse the influence of blur on photogrammetric image processing, the correction of blur and finally, the use of corrected images for coordinate measurements. It was found that blur influences image processing significantly and even prevents automatic photogrammetric analysis, hence the desire to exclude blurred images from the sequence using a novel filtering technique. If necessary, essential blurred images can be restored using information of overlapping images of the sequence or a blur kernel with the developed edge shifting technique. The corrected images can be then used for target identification, measurements and automated photogrammetric processing.

Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review

2019 ◽  
Author(s):  
Mingtao Wu ◽  
Young B. Moon
Keyword(s):  
Intrusion Detection ◽  
Manufacturing Systems ◽  
High Volume ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
False Alarms ◽  
Alert Correlation ◽  
Acoustic Image ◽  
Detection Systems

Abstract Cyber-physical manufacturing system is the vision of future manufacturing systems where physical components are fully integrated through various networks and the Internet. The integration enables the access to computation resources that can improve efficiency, sustainability and cost-effectiveness. However, its openness and connectivity also enlarge the attack surface for cyber-attacks and cyber-physical attacks. A critical challenge in defending those attacks is that current intrusion detection methods cannot timely detect cyber-physical attacks. Studies showed that the physical detection provides a higher accuracy and a shorter respond time compared to network-based or host-based intrusion detection systems. Moreover, alert correlation and management methods help reducing the number of alerts and identifying the root cause of the attack. In this paper, the intrusion detection research relevant to cyber-physical manufacturing security is reviewed. The physical detection methods — using side-channel data, including acoustic, image, acceleration, and power consumption data to disclose attacks during the manufacturing process — are analyzed. Finally, the alert correlation methods — that manage the high volume of alerts generated from intrusion detection systems via logical relationships to reduce the data redundancy and false alarms — are reviewed. The study show that the cyber-physical attacks are existing and rising concerns in industry. Also, the increasing efforts in cyber-physical intrusion detection and correlation research can be utilized to secure the future manufacturing systems.

scholarly journals An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking

2018 ◽  
Vol 8 (11) ◽  
pp. 2154 ◽  
Author(s):  
Xingmei Wang ◽  
Guoqiang Wang ◽  
Zhonghua Zhao ◽  
Yue Zhang ◽  
Binghua Duan
Keyword(s):  
Target Tracking ◽  
Adaptive Filter ◽  
Poor Performance ◽  
Image Sequences ◽  
Correlation Filter ◽  
Tracking Errors ◽  
The Poor ◽  
Continuous Scale ◽  
Underwater Target ◽  
Update Strategy

To obtain accurate underwater target tracking results, an improved kernelized correlation filter (IKCF) algorithm is proposed to track the target in forward-looking sonar image sequences. Specifically, a base sample with a dynamically continuous scale is first applied to solve the poor performance of fixed-scale filters. Then, in order to prevent the filter from drifting when the target disappears and appears again, an adaptive filter update strategy with the peak to sidelobe ratio (PSR) of the response diagram is developed to solve the following target tracking errors. Finally, the experimental results show that the proposed IKCF can obtain accurate tracking results for the underwater targets. Compared to other algorithms, the proposed IKCF has obvious superiority and effectiveness.

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