New ‘Spider’ Convex Hull Algorithm - For an Unknown Polygon in Object Recognition

Dmitriy Dubovitskiy, Jeff McBride

Abstract

Object recognition in machine vision system and robotic applications has, and is still, an important aspect in automation applications of our everyday life. Although there are a lot of machine vision algorithms there are not always entirely clear and unified solutions for particular applications. This paper is concerned one particular step in image interpretation connected with the convex hull algorithm. This new approach to the process of convex hull step of object recognition offers a wide range of application and improves the accuracy of decision making on later steps. The challenging fundamental problem of computational geometry is offering the solution in this work to solve convex hull procedure for an unknown image polygon. The unique feature of the offered new approach is the flexible intersection of all convex set points of an object on a digital image. The convex combination points remains unknown and allow us to get the real vector space. The image segmentation algorithm and decision making procedure working in conjunction with this new convex hull algorithm will take robotic applications to a higher level of flexibility and automation. We present this unique procedure for automating and a new model of image understanding.

References

  1. Abdou, I. and W.K.Pratt (1979). Quantitative design and evaluation of enhancement thresholding edge detectors. Proc. IEEE, (67):753-763.
  2. Andrew, A. M. (1979). Another efficient algorithm for convex hulls in two dimensions. Information Processing Letters 9, (5):216219.
  3. B.Lipkin and A.Rosenfeld (1970). Picture Processing and Psychopictorics. Academic Press, New York.
  4. Brice, C. and Fennema, C. (1970). Scene analysis using regions. Artificial Intelligence, (1):205-226.
  5. Brown, K. Q. (1979). Voronoi diagrams from convex hulls. Information Processing Letters 9, (5):223228.
  6. C. B. Barber, D. P. D. and Huhdanpaa, H. (1993). The quickhull algorithm for convex hull. Tech. Rep. GCG53, The Geometry Center, Univ. of Minnesota.
  7. C.A.Harlow and S.A.Eisenbeis (1973). The analysis of radio-graphic images. IEEE Trans. Computer, (C22):678-6881.
  8. Dubovitskiy, D. A. and Blackledge, J. M. (2008). Surface inspection using a computer vision system that includes fractal analysis. ISAST Transaction on Electronics and Signal Processing, 2(3):76 -89.
  9. Dubovitskiy, D. A. and Blackledge, J. M. (2009). Texture classification using fractal geometry for the diagnosis of skin cancers. EG UK Theory and Practice of Computer Graphics 2009, pages 41 - 48.
  10. Dubovitskiy, D. A. and Blackledge, J. M. (October, 2012). Targeting cell nuclei for the automation of raman spectroscopy in cytology. In Targeting Cell Nuclei for the Automation of Raman Spectroscopy in Cytology. British Patent No. GB1217633.5.
  11. E.R.Davies (1997). Machine Vision: Theory, Algorithms, Practicalities. Academic press, London.
  12. Feldman, J. and Yakimovsky, Y. (1974). Decision theory and artificial intelligence: I. a semantic-based region analyzer. Artificial Intelligence, (5(4)):349-371.
  13. Freeman, H. (1988). Machine vision. Algorithms, Architectures, and Systems. Academic press, London.
  14. Grimson, W. E. L. (1990). Object Recognition by Computers: The Role of Geometric Constraints. MIT Press.
  15. J.M.S.Perwitt (1970). Object enhancement and extraction. Academic Press, New York.
  16. L.G.Roberts (1965). Machine perception of threedimensional solids. MIT Press, Cambridge.
  17. Louis, J. and Galbiati, J. (1990). Machine vision and digital image processing fundamentals. State University of New York, New-York.
  18. M.A.Hornish, L. and R.A.Goulart (2008). Computerassisted cervical cytology. Medical information Science.
  19. Marr, D. and E.Hildreth (1977). Theory of edge detection. Number B 207. Proc. R. Soc., London.
  20. Nalwa, V. S. and Binford, T. O. (1986). On detecting edge. IEEE Trans. Pattern Analysis and Machine Intelligence, (PAMI-8):699-714.
  21. Ohlander, R. (1975). Analysis of Natural Scenes. PhD thesis, Carnegie-Mellon University.
  22. Price, R. O. K. and Reddy, D. (1978). Picture segmentation using a recursive region splitting method. Computer Graphics Image Processing, (8(3)):313-333.
  23. P.W.Swain, T. R. and Fu, K. (1973). Multispectral image partitioning. Technical report, School of Electrical Engineering, Purdue Univ.
  24. R.A.Kirsh (1971). Computer determination of the constituent structure of biological images. MIT Press, Comput. Biomed. Res.
  25. Ripley, B. D. (1996). Pattern Recognition and Neural Networks. Academic Press, Oxford.
  26. R.M.Haralick (1980). Edge and region analysis for digital image data. Computer graphics and Image Processing, (12):60-73.
  27. R.M.Haralick (1984). Digital step edges from zero crossing of second directional derivatives. IEEE Trans. on Pattern Recognition and Machine Intelligence, (PAMI6):58-68.
  28. R.O. Duba, P. H. (1973). Pattern Classification and Scene Annalysis. Academic Press, New York,Wiley.
  29. Rosenfeld, A. (1982). Digtal Picture Processing, volume 1,2. Academic Press, New York.
  30. Russ, J. C. (1990). Computer - Assisted Microscopy. The Measurement and Analysis of Images. Prenum press, New York.
  31. Snyder, W. E. and Qi, H. (2004). Machine Vision. Cambridge University Press, England.
  32. Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Wiley, New York.
  33. Yakimovsky, Y. and Feldman, J. (1973). A semantics-based decision theoretic region analyzer. 3-th int. Conference Artificial Intelligence, (1):580-588.
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Paper Citation


in Harvard Style

Dubovitskiy D. and McBride J. (2013). New ‘Spider’ Convex Hull Algorithm - For an Unknown Polygon in Object Recognition . In Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: MHGInterf, (BIOSTEC 2013) ISBN 978-989-8565-34-1, pages 311-317. DOI: 10.5220/0004368703110317


in Bibtex Style

@conference{mhginterf13,
author={Dmitriy Dubovitskiy and Jeff McBride},
title={New ‘Spider’ Convex Hull Algorithm - For an Unknown Polygon in Object Recognition},
booktitle={Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: MHGInterf, (BIOSTEC 2013)},
year={2013},
pages={311-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004368703110317},
isbn={978-989-8565-34-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Biomedical Electronics and Devices - Volume 1: MHGInterf, (BIOSTEC 2013)
TI - New ‘Spider’ Convex Hull Algorithm - For an Unknown Polygon in Object Recognition
SN - 978-989-8565-34-1
AU - Dubovitskiy D.
AU - McBride J.
PY - 2013
SP - 311
EP - 317
DO - 10.5220/0004368703110317