Publication: Robust motion estimation on a low-power multi-core DSP
Loading...
Official URL
Full text at PDC
Publication Date
2013
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Springer International Publishing AG
Citations
Exportar
Abstract
Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.
Description
© Springer International Publishing AG.
This work has been supported by Spanish Projects CICYT-TIN 2008/508 and TIN2012-32180.
UCM subjects
Unesco subjects
Keywords
Citation
1. CL Huang, YT Chen, Motion estimation method using a 3D steerable filter. Image Vision Comput. 13(1), 21–32 (1995)
2. BD Lucas, T Kanade, in Proc. of 7th Int. Joint Conf. on Artificial Intelligence (IJCAI ’81). An iterative image registration technique with an application to stereo vision (Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, April 1981), pp. 674–679
3. H-S Oh, H-K Lee, Block-matching algorithm based on an adaptive reduction of the search area for motion estimation. Real-Time Imaging. 6, 407–414 (October 2000)
4. D Sun, JP Lewis, Michaelj, in Proc. ECCV. Black. Learning optical flow (Brown University, Providence, Rhode Island 02912, USA, 2008), pp. 83–97
5. S Baker, R Gross, I Matthews, Lucas-kanade 20 years on: a unifying framework: part 3. Int J Comput Vis. 56, 221–255 (2002)
6. X Liang, PW McOwan, A Johnston, Biologically inspired framework for spatial and spectral velocity estimations. J. Opt. Soc. Am. A. 28(4), 713–723 (April 2011)
7. CP Benton, PW McOwan, A Johnston, Robust velocity computation from a biologically motivated model of motion perception. Proc R Soc B. 266, 509–518 (1999)
8. A Johnston, CW Clifford, A unified account of three apparent motion illusions. Vision Res. 35(8), 1109–1123 (April 1995)
9. F Ayuso, G Botella, C Garcia, M Prieto, F Tirado, GPU-based acceleration of bio-inspired motion estimation model. Concurrency and Computation: Practice and Experience, In press
10. GB Juan, Ríos García A, M Rodriguez-Alvarez, ER Vidal, U Meyer-Bäse, MC Molina, Robust bioinspired architecture for optical-flow computation. IEEE Trans. VLSI Syst. 18(4), 616–629 (2010)
11. C Dhoot, VJ Mooney, SR Chowdhury, LP Chau, in VLSI-SoC. Fault tolerant design for low power hierarchical search motion estimation algorithms (IEEE Computer Society, Los Alamitos, CA (USA), 2011), pp. 266–271
12. Vleeschouwer De C, T Nilsson, in ICIP (2). Motion estimation for low power video devices (IEEE Computer Society, Los Alamitos, CA (USA), 2001), pp. 953–956
13. M Anguita, J D´ıaz, E Ros, FJ Fernandez-Baldomero, Optimization strategies for high-performance computing of optical-flow in general-purpose processors. IEEE Trans. Circuits Syst. Video Techn. 19(10), 1475–1488 (2009)
14. D Honegger, P Greisen, L Meier, P Tanskanen, M Pollefeys. IROS (IEEE, 2012), pp. 5177–5182
15. B Subramaniam, Wu-chun Feng, in 8th IEEE Workshop on High-Performance, Power-Aware Computing (HPPAC). The Green Index: A Metric for Evaluating System-Wide Energy Efficiency in HPC Systems (IEEE Computer Society, Los Alamitos, CA (USA), May 2012)
16. Y Shirai, J Miura, Y Mae, M Shiohara, H Egawa, S Sasaki, in Computer Architectures for Machine Perception, 1993. Proceedings. Moving object perception and tracking by use of dsp (IEEE Computer Society, Los Alamitos, CA (USA), Dec 1993), pp. 251–256
17. BKP Horn, BG Schunck, Determining optical flow. Artif. Intell. 17, 185–203 (1981)
18. T Rwekamp, M Platzner, L Peters, in In Proceedings of the 8th ICSPAT. Specialized architectures for optical flow computation: A performance comparison of asic, dsp, and multi-dsp, (1997), pp. 829–833
19. A Steimer, in Master Thesis. ETH Zurich. Global optical flow estimation by linear interpolation algorithm on a DSP microcontroller, (Switzerland, October, 2011)
20. MV Srinivasan, An image-interpolation technique for the computation of optic flow and egomotion. Biol. Cybernetics. 71, 401–415 (1994)
21. RJ Snowden, RF Hess, Temporal frequency filters in the human peripheral visual field. Vision Res. 32(1), 61–72 (1992)
22. JJ Koenderink, Optic flow. Vision Res. 26, 161–180 (1996)
23. TMS320C6678 Multicore Fixed and Floating-Point Digital Signal Processor. http://www.ti.com/lit/ds/sprs691c/sprs691c.pdf, February 2012. Texas Instruments Literature Number: SPRS691C
24. F Ayuso, G Botella, C Garcia, M Prieto, F Tirado, in WPABA 2011. GPU-based acceleration of bioinspired motion estimation model (IEEE Computer Society Washington, DC, USA, 2011)
25. F Ayuso, G Botella, C Garcia, M Prieto, F Tirado, in 21st Int. Conf. on Field Programmable Logic and Applications, Workshop on Computer Vision on Low-Power Reconfigurable Architectures, 2011. GPU-based signal processing scheme for bioinspired optical flow (IEEE Computer Society, Los Alamitos, CA (USA), p. 2011. 09/2011 (2011)
26. Introduction to TMS320C6000 DSP optimization. http://www.ti.com/lit/an/sprabf2/sprabf2.pdf, October 2011. Texas Instruments Literature Number: SPRABF2