Radiotekhnika
Publishing house Radiotekhnika

"Publishing house Radiotekhnika":
scientific and technical literature.
Books and journals of publishing houses: IPRZHR, RS-PRESS, SCIENCE-PRESS


Тел.: +7 (495) 625-9241

 

Methods and algorithms for image processing using fuzzy features

Keywords:

E.V. Pugin – Post-graduate Student, Department «CAD», Murom branch of Vladimir State University named after A.&N. Stoletovs
E-mail: egor.pugin@gmail.com
A.L. Zhiznyakov – Dr. Sc. (Eng.), Professor, First Deputy Director of Murom branch of Vladimir State University named after A.&N. Stoletovs
E-mail: lvovich@newmail.ru


Fundamentals of the theory of fuzzy sets and the theory of fuzzy logic were laid in the works of L. Zadeh in the mid-1970s. Advanced application developments in the field of fuzzy logic and fuzzy control were made by Japanese scientists in the 80’s.
The basic notion of fuzzy logic is a fuzzy set. It expands the notion of a classical set, assuming that the membership of an element can take any value in the interval [0, 1], not just the value 0 or 1. One of the most important concepts of fuzzy logic is the linguistic variable. Linguistic variable is a variable that can take the meanings of phrases from a natural or artificial language. For example, the linguistic variable ”speed” can have the values ”high”, ”average”, ”very low”, etc. On the basis of fuzzy sets, over time, a large number of modifications of the original theory have been developed: type 2 fuzzy sets and higher order fuzzy sets, rough sets, soft sets.
The theory of fuzzy sets is actively used in image processing and pattern recognition. In terms of linguistic and fuzzy variables, existing works can be divided into works using linguistic variables, and works that operate with membership functions. By application in image processing and recognition tasks, it is possible to single out image processing (calculation of fuzzy analogs of classical features), recognition and classification (fuzzy clustering, fuzzy C average algorithm, fuzzy neural networks, etc.), characteristics extraction (extraction of a small number of the most significant characteristics from a large data set – multifactor diminution of dimensions, analysis of components, etc.). Also, applications of fuzzy-set theory for processing digital image sequences are known.
The drawbacks of the existing approaches are: late fuzzification (transition to fuzzy features), early defuzzification (transition to classical features), use of type1 fuzzy sets. To solve these problems, it is necessary to study image processing algorithms based on soft sets and type 2 fuzzy sets and higher types, algorithms for distinguishing fuzzy features for problems of recognition of individual images and their sequences, possibilities for self-tuning of the parameters type 2 membership functions of the fuzzy set with the help of: neural networks and genetic algorithms.

References:
  1. Zakharov A., Tuzhilkin A., Zhiznyakov A. Automatic building detectionfrom satellite images using spectral graph theory // IEEE International Conference on Mechanical Engineering, Automation and Control Systems (MEACS). 12.2015.
  2. Privezentsev D.G., Zhiznyakov A.L. Use of characteristic image segmentsin tasks of digital image processing // IEEE International Conference «Stability and Control Processes» in Memory of V.I. Zubov (SCP). 10.2015.
  3. Zhiznyakov A.L., Privezentsev D.G., Zakharov A.A. Using fractal features of digital images for the detection of surface defects // Pattern Recognition and Image Analysis. Yanvar' 2015. T. 25. № 1. S. 122−131.
  4. Kofman A. Vvedenie v teoriyu nechetkix mnozhestv. M.: Radio i svyaz'. 1982. 432 s.
  5. Zadeh L.A. Fuzzy sets // Information and Control. 1965. V. 8. № 3. P. 338−353.
  6. Kruglov V.V., Dli M.I., Golunov R.Yu. Nechetkaya logika i iskusstvenny'e nejronny'e seti. M.: Fizmatlit. 2000. 224 s.
  7. Kuz'min V.B. Postroenie gruppovy'x reshenij v prostranstvax chetkix i nechetkix binarny'x otnoshenij. M.: Nauka. 1982. 168 s.
  8. Nechetkie mnozhestva i teoriya vozmozhnostej: Poslednie dostizheniya / Pod red. R.R. Yagera. M.: Radio i svyaz'. 1986. 408 s.
  9. Zade L. Ponyatie lingvisticheskoj peremennoj i ego primenenie k prinyatiyu priblizhenny'x reshenij. M.: Mir. 1976. 166 s.
  10. Te'rano T., Asai K., Suge'no M.M. Prikladny'e nechetkie sistemy'. M.: Mir. 1993. 368 s.
  11. Blizard W.D. Multiset theory // Notre Dame J. Formal Logic. December 1988. V. 30. № 1. P. 36−66.
  12. Zadeh L.A. The Concept of a Linguistic Variable and its Application to Approximate Reasoning // Learning Systems and Intelligent Robots. Springer Science + Business Media. 1974. P. 1−10.
  13. Mendel J.M., John R.I.B. Type 2 fuzzy sets made simple // IEEE Transactions on Fuzzy Systems. April 2002. V. 10. № 2. P. 117−127.
  14. Remezova E.M. Nechetkie mnozhestva vtorogo poryadka: ponyatie, analiz i osobennosti primeneniya // Sovremenny'e problemy' nauki i obrazovaniya. 2013. № 5.
  15. Pawlak Z. Rough sets // International Journal of Computer and Information Sciences. October 1982. V. 11. № 5. P. 341−356.
  16. Molodtsov D. Soft set theory – First results // Computers & Mathematics with Applications. 1999. V. 37. № 4. P. 19−31.
  17. Molodczov D.A. Teoriya myagkix mnozhestv. M.: Editorial URSS. 2004. 360 s.
  18. Kharal A., Ahmad B. Mappings on soft classes // New Mathematics and Natural Computation. 2011. V. 07. № 3. P. 471−481.
  19. Bezdek J.C., et al. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer US. 1999. V. 4 (The Handbooks of Fuzzy Sets Series).
  20. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, MA, USA: Kluwer Academic Publishers. 1981.
  21. Li D., Pedrycz W., Pizzi N.J. Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification // IEEE Transactions on Biomedical Engineering. June 2005. V. 52. № 6. P. 1132−1139.
  22. Jensen R. Combining rough and fuzzy sets for feature selection: PhD thesis. School of Informatics University of Edinburgh. 2005.
  23. Karkishhenko A.N., Butenkov S.A., Krivsha V.V. Raspoznavanie v sistemax e'kologicheskogo monitoringa s primeneniem nechetkix geometricheskix priznakov // Izvestiya TRTU (Taganrogskij Gosudarstvenny'j radiotexnicheskij universitet). 2000. S. 144−147.
  24. Geppener V.V., Sokolov M.A. Klassifikacziya podpoverxnostny'x ob''ektov v zadachax geolokaczii na osnove ispol'zovaniya nechetkix priznakov // Mezhdunar. konf. po myagkim vy'chisleniyam i izmereniyam (Proceedings of SCM’99 (International Conference on Soft Computing and Measurements). SPb.: 1999. S. 198−200.
  25. Te'rano T., Masui A., Kono S. Raspoznavanie formy' ovoshhej s pomoshh'yu nechetkoj logiki // Tez. dokl. 3-ego naczional'nogo simpoziuma po nechetkim sistemam. Tokio. 1987.
  26. Xirota, Arai, Xatisi. Raspoznavanie dvizhushhixsya czelej s pomoshh'yu nechetkoj logiki i robot dlya peremeshheniya dvizhushhixsya ob''ektov // Tez. dokl. 3-ego naczional'nogo simpoziuma po nechetkim sistemam. Tokio. 1987.
  27. Obradović D., et al. Intelligent Systems: Models and Applications // Revised and Selected Papers from the 9th IEEE International Symposium on Intelligent Systems and Informatics (SISY). 2011. Ed. by Pap E. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. Chap. Fuzzy Geometry in Linear Fuzzy Space. P. 137−153.
  28. Glova V.I., Anikin I.V. Razrabotka metoda raspoznavaniya razmy'ty'x 2D-primitivov izobrazheniya // Vestnik Kazanskogo gosudarstvennogo texnicheskogo universiteta im. A.N. Tupoleva. 2000. № 4. S. 66−72.
  29. Glova V.I., Anikin I.V., Adzheli M.A. Nechetkaya model' raspoznavaniya razmy'ty'x dvumerny'x form // Vestnik Kazanskogo gosudarstvennogo texnicheskogo universiteta im. A.N. Tupoleva. 2001. № 3. S. 32−36.
  30. Han J.H., Koczy L.T., Poston T. Fuzzy Hough transform // Second IEEE International Conference on Fuzzy Systems. 1993. V. 2. 803−808.
  31. Suetake N., Uchino E., Hirata K. Generalized Fuzzy Hough Transform for Detecting Arbitrary Shapes in a Vague and Noisy Image // Soft Computing. 2006. V. 10. № 12. P. 1161−1168.
  32. Pugin E.V., Zhiznyakov A.L. Fil'tracziya znachimy'x priznakov nechetkogo preobrazovaniya Xafa // Dinamika sistem, mexanizmov i mashin. 2016. T. 2. № 1. S. 284−290.
  33. Rosenfeld A. Fuzzy geometry: An updated overview // Information Sciences. 1998. V. 110. № 3/4. P. 127−133.
  34. Bloch I. Fuzzy relative position between objects in image processing: a morphological approach // IEEE Transactions on Pattern Analysis and Machine Intelligence. July 1999. V. 21. № 7. P. 657−664.
  35. Chen Z., Qiu T., Ruan S. Fuzzy adaptive level set algorithm for brain tissue segmentation // 9th International Conference on Signal Processing. 10.2008. P. 1047−1050.
  36. Begelrnan G., et al. Cell nuclei segmentation using fuzzy logic engine // International Conference on Image Processing (ICIP). 10/2004. V. 5. P. 2937−2940.
  37. Kass M., Witkin A., Terzopoulos D. Snakes: Active contour models // International Journal of Computer Vision. 1988. V. 1. № 4. P. 321−331.
  38. Krinidis S., Chatzis V. Fuzzy Energy-Based Active Contours // IEEE Transactions on Image Processing. December 2009. V. 18. № 12. P. 2747−2755.
  39. Shi J., et al. An interval type 2 fuzzy active contour model for auroral oval segmentation // Soft Computing. 2015. P. 1−21.
  40. Thieu Q.T., et al. Efficient segmentation with the convex local-global fuzzy Gaussian distribution active contour for medical applications // Annals of Mathematics and Artificial Intelligence. 2015. V. 75. № 1. P. 249−266.
  41. Petrov V.O., Privalov O.O. Modifikacziya algoritma aktivny'x konturov dlya resheniya zadachi interaktivnoj segmentaczii rastrovy'x izobrazhenij defektov metallicheskix otlivok // Sovremenny'e problemy' nauki i obrazovaniya. 2008. № 6. S. 14−19.
  42. Höwing F., Dooley L.S., Wermser D. Linguistic Contour Modelling through a Fuzzy Active Contour // New Frontiers in Computational Intelligence and its Applications (UK: IOS Press). 2000. V. 57 (ed. by M. Mohammadian). P. 274−282. (Frontiers in Artificial Intelligence and Applications).
  43. Gong M., et al. An efficient bi-convex fuzzy variational image segmentation method // Information Sciences. 2015. V. 293. P. 351−369.
  44. Shi J., et al. Change Detectionin Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms // MathematicalProblems in Engineering. 2014. V. 2014. № 15. P. 10−1155 (Article ID 870936).
  45. Tishkin R.V. Myagkie vy'chisleniya v zadachax segmentaczii kosmicheskix izobrazhenij // Czifrovaya obrabotka signalov. 2010. № 3. S. 25−29.
  46. Phophalia A., Mitra S.K., Rajwade A. A new denoising filter for brain MRimages // Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP). 2012. Association for Computing Machinery (ACM).
  47. Phophalia A., Mitra S.K., Rajwade A. Object boundary detection using Rough Set Theory // Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). 12/2013. P. 1−4.
  48. Munshi P., Mitra S.K. A rough-set based binarization technique for fingerprint images // IEEE International Conference on Signal Processing, Computing and Control (ISPCC). 03/2012. P. 1−6.
  49. Pal S.K., Shankar B.U., Mitra P. Granular computing, rough entropy and object extraction // Pattern Recognition Letters. 2005. V. 26. № 16. P. 2509−2517.
  50. Małyszko D., Stepaniuk J. Adaptive multilevel rough entropy evolutionary thresholding // Information Sciences. 2010. V. 180. № 7. P. 1138−1158.
  51. Swiniarski R. An Application of Rough Sets and Haar Wavelets to Face Recognition // Revised Papers of Second International Conference «Rough Sets and Current Trends in Computing (RSCTC)». 16−19 October 2000. Banff, Canada. Ed. by Ziarko W., Yao Y. Berlin, Heidelberg: Springer Berlin Heidelberg. 2001. P. 561−568.
  52. Wojcik Z. Rough approximation of shapes in pattern recognition // Computer Vision, Graphics and Image Processing. 1987. V. 40. № 2. P. 228−249.
  53. Kimachi M., Kanayama K., Teramoto K. Incident prediction by fuzzy image sequence analysis // Proceedings of Vehicle Navigation and Information Systems Conference. 08/1994. P. 51−56.
  54. Hiremath P.S., Hiremath M.R.M. Face Detection and Trackingin Video Sequence using Fuzzy Geometric Face Model and Motion Estimation // International Journal of Computer Applications. November 2012. V. 58. № 15. P. 12−16.
  55. Cho J.-S., Yun B.-J., Ko Y.-H. Precision Tracking Based-on Fuzzy Reasoning Segmentation in Cluttered Image Sequences // Proceedings of 9th International Conference «Knowledge-Based Intelligent Information and Engineering Systems (KES)». Melbourne, Australia. 14−16 September 2005. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005 (ed. by Khosla R., Howlett R.J., Jain L.C.). Part II. P. 371−377.
  56. Pugin E.V., Zhiznyakov A.L. Classification of features of image sequences // International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS). May 2015. T. XL–5/W6. S. 79−81.

© Издательство «РАДИОТЕХНИКА», 2004-2017            Тел.: (495) 625-9241                   Designed by [SWAP]Studio