Image Processing Reference
In-Depth Information
14. Jiang, H., Moloney, C.: A new direction adaptive scheme for image interpolation. In:
International Conference on Image Processing, Rochester, New York, USA, pp. 369-
372 (2002)
15. Kalogeropoulos, G., Sirakoulis, G.C., Karafyllidis, I.: Cellular automata on FPGA for
real-time urban traffic signals control. Journal of Supercomputing 65(2), 1-18 (2013)
16. Karafyllidis, I., Andreadis, I., Tzionas, P., Tsalides, P., Thanailakis, A.: A cellular au-
tomaton for the determination of the mean velocity of moving objects and its VLSI im-
plementation. Pattern Recognition 29(4), 689-699 (1996)
17. Katis, I., Sirakoulis, G.: Cellular automata on FPGAs for image processing. In: 16th
Panhellenic Conference on Informatics, pp. 308-313. IEEE Computer Society, Piraeus
(2012)
18. Keys, R.G.: Cubic convolution interpolation for digital image processing. IEEE Trans.
Acoust., Speech, Signal Process. 29, 1153-1160 (1981)
19. Lafe, O.: Cellular Automata Transforms: Theory and Applications in Multimedia Com-
pression. Encryption and Modeling. Kluwer Academic Publishers (2000)
20. Li, X., Orchard, M.T.: New edge-directed interpolation. IEEE Trans. Image Process. 10,
1521-1527 (2001)
21. Lin, C.T., Fan, K.W., Pu, H.C., Lu, S.M., Liang, S.F.: An HVS-directed neural network
based image resolution enhancement scheme for image resizing. IEEE Trans. Fuzzy
Syst. 15, 605-615 (2007)
22. Michailidis, G., Andreadis, I.: A real-time stereo correspondence algorithm based on 2-D
cellular automata. In: Int. Workshop on Advanced Imaging Technology, Kuala Lumbur,
Malaysia, pp. 1-6 (2010)
23. Muresan, D., Parks, T.: Adaptively quadratic (aqua) image interpolation. IEEE Trans.
Image Process. 13, 690-698 (2004)
24. Nalpantidis, L., Amanatiadis, A., Sirakoulis, G.C., Gasteratos, A.: Efficient hierarchical
matching algorithm for processing uncalibrated stereo vision images and its hardware
architecture. IET Image Processing 5(5), 481-492 (2011)
25. Panagiotopoulos, F.K., Mardiris, V.A., Chatzis, V.: Quantum-dot cellular automata de-
sign for median filtering and mathematical morphology operations on binary images. In:
Sirakoulis, G.C., Bandini, S. (eds.) ACRI 2012. LNCS, vol. 7495, pp. 554-564. Springer,
Heidelberg (2012)
26. Popovici, A., Popovici, D.: Cellular automata in image processing. In: Proceedings of the
15th International Symposium on the Mathematical Theory of Networks and Systems,
p. 6 (2002)
27. Porter, R., Frigo, J., Conti, A., Harvey, N., Kenyon, G., Gokhale, M.: A reconfig-
urable computing framework for multi-scale cellular image processing. Microprocess.
Microsyst. 31(8), 546-563 (2007)
28. Preston, K., Duff, J.: Modern Cellular Automata: Theory and Applications. Plenum Press
(1984)
29. Progias, P., Sirakoulis, G.C.: An FPGA processor for modelling wildfire spread. Mathe-
matical and Computer Modeling 57(5-6), 1436-1452 (2013)
30. Rosin, P.L.: Training cellular automata for image processing. IEEE Trans. Image Pro-
cess. 15(7), 2076-2087 (2006)
31. Rosin, P.L.: Image processing using 3-state cellular automata. Computer Vision and Im-
age Understanding 114(7), 790-802 (2010)
32. Rosin, P.L., Sun, X.: Cellular automata as a tool for image processing. In: Chen, C.H.
(ed.) Emerging Topics in Computer Vision and its Applications, pp. 233-251 (2011)
33. Shi, H., Ward, R.: Canny edge based image expansion. In: IEEE International Sympo-
sium on Circuits and Systems, pp. 785-788. IEEE, Scottsdale (2002)
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