Browse > Article

Fixed-Point Modeling and Performance Analysis of a SIFT Keypoints Localization Algorithm for SoC Hardware Design  

Park, Chan-Ill (Department of Electronics and Communications Engineering, Kwangwoon University)
Lee, Su-Hyun (Department of Electronics and Communications Engineering, Kwangwoon University)
Jeong, Yong-Jin (Department of Electronics and Communications Engineering, Kwangwoon University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SD / v.45, no.6, 2008 , pp. 49-59 More about this Journal
Abstract
SIFT(Scale Invariant Feature Transform) is an algorithm to extract vectors at pixels around keypoints, in which the pixel colors are very different from neighbors, such as vortices and edges of an object. The SIFT algorithm is being actively researched for various image processing applications including 3-D image constructions, and its most computation-intensive stage is a keypoint localization. In this paper, we develope a fixed-point model of the keypoint localization and propose its efficient hardware architecture for embedded applications. The bit-length of key variables are determined based on two performance measures: localization accuracy and error rate. Comparing with the original algorithm (implemented in Matlab), the accuracy and error rate of the proposed fixed point model are 93.57% and 2.72% respectively. In addition, we found that most of missing keypoints appeared at the edges of an object which are not very important in the case of keypoints matching. We estimate that the hardware implementation will give processing speed of $10{\sim}15\;frame/sec$, while its fixed point implementation on Pentium Core2Duo (2.13 GHz) and ARM9 (400 MHz) takes 10 seconds and one hour each to process a frame.
Keywords
SIFT; Keypoint; Object recognition; Fixed point modeling;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. brown and D. G. Lowe. "Recognising Panoramas-slides." University of British Columbia, 2004
2 Cristina Cabani, W. James MacLean, "A Proposed Pipelined-Architecture for FPGA-Based Affine-Invariant Feature Detectors." accepted for presentation at The Second IEEE Workshop on Embedded Computer Vision Systems, New York, June 2006
3 Stephen Se and Piotr Jasiobedzki, "Photo- realistic 3D Model Reconstruction", Procedings of IEEE International Conference on Robotics and Automation, ICRA 2006, pages 3076-3082, Orlando, Florida, May 2006
4 정성태, Visual C++을 이용한 실용 영상처리, 생능출판사, 2007
5 D. G. Lowe. "Object recognition from local scaleinvariant features." In Proceedings of the Seventh International Conference on Computer Vision (ICCV''99), pages 1150-1157, Kerkyra, Greece, September 1999
6 Tim Barfoot, Stephen Se, Piotr Jasiobedzki, "Vision-based Localization and Terrain Modelling for Planetary Rovers", Chapter in Intelligence for Space Robotics, A. Howard and E. Tunstel, editors, TSI Press, Albuquerque, NM, 2006
7 M. Brown and D. G. Lowe. "Recognising Panoramas." Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2, 2003
8 D. G. Lowe, "Distinctive image features from scale invariant keypoints", International Journal of Computer Vision, vol. 60, no. 2, 2004
9 Pettersson. N, Petersson. L, "Online stereo calibration using FPGAs", Intelligent Vehicles Symposium, 2005. Proceedings. IEEE, 6-8, June, 2005
10 Cabani C, MacLean WJ. "Implementation of an Affien-Covariant Feature Detector in Field-Programmable Gate Arrays", International Conference on Computer Vision Systems 2007, Bielefeld, Germany, March 21-24, 2007