A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images

Min-Song Wei; Fei Xing; Zheng You

doi:10.1038/lsa.2018.6

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A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images

Original Articles

- A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images
- A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images
- LSA 2018年7卷第3期
- Affiliations：
  
  1.Department of Precision Instrument, Tsinghua University, Beijing 100084, China
  2.State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing 100084, China
  3.Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA
- Author bio：
  
  F Xing, Email: xingfei@mail.tsinghua.edu.cn
  Z You, Email: yz-dpi@mail.tsinghua.edu.cn
- Funds：
- DOI：10.1038/lsa.2018.6
  中图分类号：
- 纸质出版日期：2018，
  
  网络出版日期：2018-05-04，
  
  收稿日期：2017-09-21，
  
  修回日期：2018-01-05，
  
  录用日期：2018-01-15
- Accepted：
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A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images[J]. LSA, 2018,7(3):18006-9.

Wei, M. S.,Xing, F.& You, Z. et al. A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images. Light: Science & Applications, 3, 18006 (2018).
A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images[J]. LSA, 2018,7(3):18006-9. DOI： 10.1038/lsa.2018.6.

Wei, M. S.,Xing, F.& You, Z. et al. A real-time detection and positioning method for small and weak targets using a 1D morphology-based approach in 2D images. Light: Science & Applications, 3, 18006 (2018). DOI： 10.1038/lsa.2018.6.

摘要

Abstract

A small and weak target detection method is proposed in this work that outperforms all other methods in terms of real-time capability. It is the first time that two-dimensional (2D) images are processed using only one-dimensional1D structuring elements in a morphology-based approach

enabling the real-time hardware implementation of the whole image processing method. A parallel image readout and processing structure is introduced to achieve an ultra-low latency time on the order of nanoseconds

and a hyper-frame resolution in the time domain can be achieved by combining the row-by-row structure and the electrical rolling shutter technique. Experimental results suggest that the expected target can be successfully detected under various interferences with an accuracy of 0.1 pixels (1

) under the worst sky night test condition and that a centroiding precision of better than 0.03 pixels (1

) can be reached for static tests. The real-time detection method with high robustness and accuracy is attractive for application to all types of real-time small target detection systems

such as medical imaging

infrared surveillance

and target measurement and tracking

where an ultra-high processing speed is required.

关键词

Keywords

high robustness and accuracy1D morphology-based approachrow-by-row structurereal-time detection methodsmall and weak target detection and positioning

references

Sarkar SG, Dey D. Mathematical morphology aided enhancement and segmentation of T2-weighted brain MRI images. Proceedings of International Conference on Intelligent Control Power and Instrumentation (ICICPI); 21–23 October 2016, Kolkata, India, IEEE, 2016, pp122–126.

Sridhar B, KVVS Reddy, Prasad AM. An unsupervisory qualitative image enhancement using adaptive morphological bilateral filter for medical images.Int J Comput Appl2014;99: 31–38..

Mkayes AA, Saad NM, Faye I, Walter N. Image histogram and FFT based critical study on noise in fluorescence microscope images. Proceedings of the 6th International Conference on Intelligent and Advanced Systems (ICIAS); 15–17 August 2016; Kuala Lumpur, Malaysia, Malaysia. IEEE, 2016, pp1–4.

Bai XZ. Morphological infrared image enhancement based on multi-scale sequential toggle operator using opening and closing as primitives.Infrared Phys Technol2015;68: 143–151..

Han JH, Ma Y, Zhou B, Fan F, Liang Ket al. A robust infrared small target detection algorithm based on human visual system.IEEE Geosci Remote Sens Lett2014;11: 2168–2172..

Yang L, Yang J, Yang K. Adaptive detection for infrared small target under sea-sky complex background.Electron Lett2004;40: 1083–1085..

Maška M, Ulman V, Svoboda D, Matula P, Matula Pet al. A benchmark for comparison of cell tracking algorithms.Bioinformatics2014;30: 1609–1617..

Bendicks C, Tarlet D, Michaelis B, Thévenin D, Wunderlich B. Use of coloured tracers in gas flow experiments for a lagrangian flow analysis with increased tracer density. In: Denzler J, Notni G, Süße H, editors. Pattern Recognition: 31st DAGM Symposium Proceedings; Berlin Heidelberg: Springer; 2009, pp392–401.

Liu W, Ma X, Li X, Chen L, Zhang Yet al. High-precision pose measurement method in wind tunnels based on laser-aided vision technology.Chin J Aeronaut2015;28: 1121–1130..

Wei MS, Xing F, You Z, Wang G. Multiplexing image detector method for digital sun sensors with arc-second class accuracy and large FOV.Opt Express2014;22: 23094–23107..

Qu F, Liu JZ, Xu H, Ye X, Yang DJet alDesign of real-time small target detection system for infrared image based on FPGA. Proceedings of the 2010 International Conference on Optics Photonics and Energy Engineering (OPEE); 10–11 May 2010; Wuhan, China: Wuhan, China, IEEE, 2010, pp9–13.

Mende SB, Heetderks H, Frey HU, Lampton M, Geller SPet al. Far ultraviolet imaging from the IMAGE spacecraft. 1. System design.Space Sci Rev2000;91: 243–270..

Bai XZ, Zhang S, Du BB, Liu ZY, Jin Tet al. Survey on dim small target detection in clutter background: wavelet, inter-frame and filter based algorithms.Procedia Eng2011;15: 479–483..

Zhang F, Li CF, Shi LN. Detecting and tracking dim moving point target in IR image sequence.Infrared Phys Technol2005;46: 323–328..

Kim S, Lee J. Scale invariant small target detection by optimizing signal-to-clutter ratio in heterogeneous background for infrared search and track.Pattern Recogn2012;45: 393–406..

Sun T, Xing F, Wang XC, Li J, Wei MSet al. Effective star tracking method based on optical flow analysis for star trackers.Appl Opt2016;55: 10335–10340..

Deng LZ, Zhu H, Wei YT, Lu GM, Wei Y. Small target detection using quantum genetic morphological filter. Proceedings of the Volume 9812, MIPPR 2015: Automatic Target Recognition and Navigation. Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015); 14 December 2015; Enshi, China, SPIE, 2015, 9812: 98120A.

Bai XZ, Zhou FG. Analysis of new top-hat transformation and the application for infrared dimsmall target detection.Pattern Recogn2010;43: 2145–2156..

Jackway PT. Improved morphological top-hat.Electron Lett2000;36: 1194–1195..

Jie J, Liu L, Zhang GJ. Robust and accurate star segmentation algorithm based on morphology.Opt Eng2016;55: 063101..

Genovese M, Napoli E. FPGA-based architecture for real time segmentation and denoising of HD video.J Real-Time Image Process2013;8: 389–401..

Dong Y, Xing F, You Z. An APS-based autonomous star tracker. Proceedings of the Volume 5633, Advanced Materials and Devices for Sensing and Imaging II; 20 January 2005; Beijing, China, SPIE, 2005, 5633: pp225–233.

Arbabmir MV, Mohammadi SM, Salahshour S, Somayehee F. Improving night sky star image processing algorithm for star sensors.J Opt Soc Am A2014;31: 794–801..

Zhao JF, Feng HJ, Xu ZH, Li Q, Peng H. Real-time automatic small target detection using saliency extraction and morphological theory.Opt Laser Technol2013;47: 268–277..

Wei MS, Xing F, You Z. An implementation method based on ERS imaging mode for sun sensor with 1 kHz update rate and 1″ precision level.Opt Express2013;21: 32524–32533..

Gustafsson N, Culley S, Ashdown G, Owen DM, Pereira PMet al. Fast live-cell conventional fluorophore nanoscopy with ImageJ through super-resolution radial fluctuations.Nat Commun2016;7: 12471..

Ramesh B, George AD, Lam H. Real-time, low-latency image processing with high throughput on a multi-core SoC. Proceedings of the 2016 IEEE High Performance Extreme Computing Conference (HPEC); 13–15 September 2016; Waltham, MA, USA, IEEE, 2016, pp1–7.

Li J, Liu ZL, Liu FD. Using sub-resolution features for self-compensation of the modulation transfer function in remote sensing.Opt Express2017;25: 4018–4037..

Li ZL, Liang B, Zhang T, Zhu HL. Imagesimulation for airborne star tracker under strong background radiance. Proceedings of the 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE); 25–27 May 2012; Zhangjiajie, China, IEEE, 2012, pp644–648.

Soille P.Morphological Image Analysis: Principles and Applications. Berlin: Springer; 2004.

Kim HS, Yoon HS, Trung KN, Lee GS. Automatic lung segmentation in CT images using anisotropic diffusion and morphology operation. Proceedings of the 7th IEEE International Conference on Computer and Information Technology; 16–19 October 2007; Aizu-Wakamatsu, Fukushima, Japan. IEEE: Aizu-Wakamatsu, Fukushima, Japan, 2007, pp557–561.

Delabie T. Star position estimation improvements for accurate star tracker attitude estimation. Proceedings of AIAA Guidance, Navigation, and Control Conference; Kissimmee, FL, USA: Kissimmee, FL, USA, AIAA, 2015, pp1–15.

Sun T, Xing F, You Z, Wei MS. Motion-blurred star acquisition method of the star tracker under high dynamic conditions.Opt Express2013;21: 20096–20110..

Wei MS, Bao JY, Xing F, Liu ZY, Sun Tet alSystem-on-a-chip based nano star tracker and its real-time image processing approach. Proceedings of the 30th Annual AIAA/USU Conference on Small Satellites; August 6–11, 2016; Logan, UT, USA. AIAA/USU: Logan, UT, USA, 2016, ppSSC16-IV-3.

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