scholarly journals Hough Transform Clutter Reduction Algorithm for Piecewise Linear Path Active Sonar Target Detection and Tracking Improvement

2013 ◽  
Vol 32 (4) ◽  
pp. 354 ◽  
Author(s):  
Kim
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Piecewise Linear ◽  
Reduction Algorithm ◽  
Active Sonar ◽  
Linear Path ◽  
Detection And Tracking ◽  
Clutter Reduction

scholarly journals A clutter reduction algorithm based on clustering for active sonar systems

2016 ◽  
Vol 35 (2) ◽  
pp. 149-157
Author(s):  
ChulHyun Kwak ◽  
Myoung Jun Cheong ◽  
Jae-Kyun Ahn
Keyword(s):  
Reduction Algorithm ◽  
Active Sonar ◽  
Sonar Systems ◽  
Clutter Reduction

Modified Hough Transform in Problem of Increasing Probability of Target Detection in Electronic Surveillance Systems

2019 ◽  
Vol 24 (3) ◽  
pp. 291-300
Author(s):  
Evgeny I. Minakov ◽  
◽  
Aleksandr V. Meshkov ◽  
Elena O. Meshkova ◽  
◽  
...  
Keyword(s):  
Target Detection ◽  
Hough Transform ◽  
Surveillance Systems ◽  
Electronic Surveillance

scholarly journals Nystagmus Estimation for Dizziness Diagnosis by Pupil Detection and Tracking Using Mexican-Hat-Type Ellipse Pattern Matching

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 885
Author(s):  
Yoanda Alim Syahbana ◽  
Yokota Yasunari ◽  
Morita Hiroyuki ◽  
Aoki Mitsuhiro ◽  
Suzuki Kanade ◽  
...  
Keyword(s):  
Pattern Matching ◽  
Hough Transform ◽  
University Hospital ◽  
Transform Method ◽  
Data Set ◽  
Medical Specialists ◽  
Detection And Tracking ◽  
Mexican Hat ◽  
Pupil Detection ◽  
In The Wild

The detection of nystagmus using video oculography experiences accuracy problems when patients who complain of dizziness have difficulty in fully opening their eyes. Pupil detection and tracking in this condition affect the accuracy of the nystagmus waveform. In this research, we design a pupil detection method using a pattern matching approach that approximates the pupil using a Mexican hat-type ellipse pattern, in order to deal with the aforementioned problem. We evaluate the performance of the proposed method, in comparison with that of a conventional Hough transform method, for eye movement videos retrieved from Gifu University Hospital. The performance results show that the proposed method can detect and track the pupil position, even when only 20% of the pupil is visible. In comparison, the conventional Hough transform only indicates good performance when 90% of the pupil is visible. We also evaluate the proposed method using the Labelled Pupil in the Wild (LPW) data set. The results show that the proposed method has an accuracy of 1.47, as evaluated using the Mean Square Error (MSE), which is much lower than that of the conventional Hough transform method, with an MSE of 9.53. We conduct expert validation by consulting three medical specialists regarding the nystagmus waveform. The medical specialists agreed that the waveform can be evaluated clinically, without contradicting their diagnoses.

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