Abstract
The general object recognition method is based on the various area segmentation algorithms. However, there might be difficulties with segmenting the adjacent objects when their boundaries are not clear. In order to solve this problem, we propose an efficient method of dividing adjacent circular-shape objects into single object through three steps: detection of the region of interest (ROI), determination of the candidate segmentation points, and creation of a segmentation boundary. The simulation shows robust results of 6.5 % average difference ratio compared to the existing methods, even when SNR was severe.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kang CC, Wang WJ A novel edge detection method based on maximization of the objective function. Pattern Recognit 40(2):609–618
Norio B, Norihiko I, Toshiyuki T Image area extraction of biological objects from a thin section image by statistical texture analysis. Electron Microse 45:298–306
Muerle JL, Allen DC (1968) Experimental evaluation of a technique for automatic segmentation of objects in complex scenes. IPPR, Thopmson
Unser M (1995) Texture classification and segmentation for using wavelet frames. IEEE Trans Image Process 4(11):1549–1560
Li W, Zhou C, Zhang Z (2004) Segmentation of the body of the tongue based on the improved snake algorithm in traditional Chinese medicine. In: Proceedings of the 5th world congress on intelligent control and automation, pp 15–19
Zabih R, Kolmogorov V (2004) Spatially coherent clustering using graph cuts. In: Proceedings of computer vision and pattern recognition, vol 2, pp 437–444
Rother C, Kolmogorov V, Blake A (2004) GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans Graphics 23(3):309–314
Kass M, Witkin A (2004) Demetri Terzopoulos active contour models. Int J Comput Vision 1:321–331
Ng EYK, Chen Y (2006) Segmentation of the breast thermogram: improved boundary detection with the modified snake algorithm. J Mech Med Biol 6(2):123–136
Kang DJ, Kweon IS (1999) A fast and stable snake algorithm for medical images. Pattern Recogn Lett 20(10):1069
Murphy TM, Math M, Finke LH (2003) Curvature covariation as a factor of perceptual salience. In: International IEEE EMBS CNECI, pp 16–19
Comaniciu D, Meer P (1997) Mean shift analysis and application. In: Seventh international conference computer vision and pattern recognition, pp 750–755
Catmull E, Rom R (1974) A class of local interpolating splines. Comput Aided Geom Des 317–326
Acknowledgments
This research was supported by Gachon University in 2012; by the Ministry of Knowledge Economy of Korea under its Convergence Information Technology Research Center support program (NIPA-2012-H0401-12-1001) supervised by the National IT Industry Promotion Agency.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Eun, SJ., Whangbo, TK. (2013). Efficient Object Recognition Method for Adjacent Circular-Shape Objects. In: Kim, K., Chung, KY. (eds) IT Convergence and Security 2012. Lecture Notes in Electrical Engineering, vol 215. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5860-5_110
Download citation
DOI: https://doi.org/10.1007/978-94-007-5860-5_110
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5859-9
Online ISBN: 978-94-007-5860-5
eBook Packages: EngineeringEngineering (R0)