Browse > Article
http://dx.doi.org/10.9723/jksiis.2014.19.1.077

Medicine-Bottle Classification Algorithm Based on SIFT  

Park, Kil Houm (경북대학교 전자공학부)
Cho, Woong Ho (대구공업대학교 디지털전자정보계열)
Publication Information
Journal of Korea Society of Industrial Information Systems / v.19, no.1, 2014 , pp. 77-85 More about this Journal
Abstract
Medicine-bottle classification algorithm to avoid medicine accidents must be robust to a geometric change such as rotation, size variation, location movement of the medicine bottles. In this paper, we propose an algorithm to classify the medicine bottles exactly in real-time by using SIFT(Scale Invariant Feature Transform) which is robust to the geometric change. In first, we classify medicine bottles by size using minimum boundary rectangle(MBR) of medicine bottles as a striking feature in order to classify the medicine bottles. We extract label region in the MBR and the region of interest(ROI) considering rotation. Then, we classify medicine bottles using SIFT for the extracted ROI. We also simplify the number of octave of SIFT in order to improve a process speed of SIFT. We confirm to classify all the medicine bottles exactly as a result of performance evaluation of the proposed algorithm about images of 250 medicine bottles. We also confirm to improve the process time more than twice the processing time by simplifying the number of octave of SIFT.
Keywords
Medicine Accident; Medicine-Bottle Classification; SIFT;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 D. G. Lowe, "Object Recognition from Local Scale-Invariant Features," Proc. Seventh IEEE International Conf. Computer Vision, vol. 2, pp. 1150-1157, 1999.
2 C. W. Kim, S. H. Woo, Z. M. U. Din, C. H. Won, J. P. Hong and J. H. Cho, "An Algorithm for Detecting Residual Quantity of Ringer's Solution for Automatic Replacement," Journal of the Korea Industrial Information System Society, vol. 13, no. 1, 2008.
3 K. Mikolaiczyk and C. Schmid, "A performance evaluation of local descriptors," IEEE Tran. on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, 2005.
4 K. Mikolaiczyk and C. Schmid, "An Affine Invariant Interest Point Detector," Proc. Seventh European Conf. Computer Vision, pp. 128-142, 2002.
5 K. Mikolaiczyk and C. Schmid, "Scale and Affine Invariant Interest Point Detectors," Int. J. Computer Vision, vol. 60, no. 1, pp. 63-86, 2004.   DOI
6 Y. Ke and R. Sukthankar, "PCA-SIFT: A More Distinctive Representation for Local Image Descriptors," Proc. Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 511-517, 2004.
7 D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.   DOI   ScienceOn
8 H. Y. Lee, J. H. Kim, S. Y. Kim, B. J. Choi, S. H. Moon and K. H. Park, "Design of a SIFT based Target Classification Algorithm robust to Geometric Transformation of Target," Journal of the Korean Institute of Intelligent Systems, vol. 20, no. 1, pp. 116-122, 2010.   과학기술학회마을   DOI   ScienceOn
9 K. J. Hong, K. C. Jung, E. J. Han and J. Y. Yang, "Mixed Mobile Education System using SIFT Algorithm," Journal of the Korea Industrial Information System Society, vol. 13, no. 2, pp. 69-79, 2008.   과학기술학회마을