Resolution Analysis of Passive Synthetic Aperture Imaging of Fast Moving Objects

2017 ◽  
Vol 10 (2) ◽  
pp. 665-710 ◽  
Author(s):  
L. Borcea ◽  
J. Garnier ◽  
G. Papanicolaou ◽  
K. Solna ◽  
C. Tsogka
2011 ◽  
Vol 36 (4) ◽  
Author(s):  
Ihor Trots ◽  
Yuriy Tasinkevych ◽  
Andrzej Nowicki ◽  
Marcin Lewandowski

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2275
Author(s):  
Hae Gyun Lim ◽  
Hyung Ham Kim ◽  
Changhan Yoon

High-frequency ultrasound (HFUS) imaging has emerged as an essential tool for pre-clinical studies and clinical applications such as ophthalmic and dermatologic imaging. HFUS imaging systems based on array transducers capable of dynamic receive focusing have considerably improved the image quality in terms of spatial resolution and signal-to-noise ratio (SNR) compared to those by the single-element transducer-based one. However, the array system still suffers from low spatial resolution and SNR in out-of-focus regions, resulting in a blurred image and a limited penetration depth. In this paper, we present synthetic aperture imaging with a virtual source (SA-VS) for an ophthalmic application using a high-frequency convex array transducer. The performances of the SA-VS were evaluated with phantom and ex vivo experiments in comparison with the conventional dynamic receive focusing method. Pre-beamformed radio-frequency (RF) data from phantoms and excised bovine eye were acquired using a custom-built 64-channel imaging system. In the phantom experiments, the SA-VS method showed improved lateral resolution (>10%) and sidelobe level (>4.4 dB) compared to those by the conventional method. The SNR was also improved, resulting in an increased penetration depth: 16 mm and 23 mm for the conventional and SA-VS methods, respectively. Ex vivo images with the SA-VS showed improved image quality at the entire depth and visualized structures that were obscured by noise in conventional imaging.


2014 ◽  
Vol 687-691 ◽  
pp. 564-571 ◽  
Author(s):  
Lin Bao Xu ◽  
Shu Ming Tang ◽  
Jin Feng Yang ◽  
Yan Min Dong

This paper proposes a robust tracking algorithm for an autonomous car-like robot, and this algorithm is based on the Tracking-Learning-Detection (TLD). In this paper, the TLD method is extended to track the autonomous car-like robot for the first time. In order to improve accuracy and robustness of the proposed algorithm, a method of symmetry detection of autonomous car-like robot rear is integrated into the TLD. Moreover, the Median-Flow tracker in TLD is improved with a pyramid-based optical flow tracking method to capture fast moving objects. Extensive experiments and comparisons show the robustness of the proposed method.


2014 ◽  
Vol 58 ◽  
pp. 193-203 ◽  
Author(s):  
Jianfei Chen ◽  
Yuehua Li ◽  
Jianqiao Wang ◽  
Yuanjiang Li ◽  
Yilong Zhang

1977 ◽  
Vol 82 (24) ◽  
pp. 3445-3451 ◽  
Author(s):  
David Atlas ◽  
Charles Elachi ◽  
Walter E. Brown

Author(s):  
Tao Yang ◽  
Yanning Zhang ◽  
Jingyi Yu ◽  
Jing Li ◽  
Wenguang Ma ◽  
...  

2013 ◽  
Author(s):  
Qian Xu ◽  
Yu Zhou ◽  
Jianfeng Sun ◽  
Ya'nan Zhi ◽  
Xiaoping Ma ◽  
...  

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