Cellular Automata for Image Resizing - Cellular Automata in Image Processing and Geometry - page 42

robust models able to overcome complex limitations of the examined systems and
processes. Here, the idea is to advance the selection of the CA rules and neigh-
borhoods depending on the specific texture and edges of the examined images. In
such a way we will be able to tackle with the complexity imposed by the differ-
ent characteristics of different images and to propose a robust adaptive CA resizing
technique. It should be mentioned that when CA neighborhood is increased the cor-
responding results are advantageous in terms of PSNR comparison while the compu-
tational burden also increases. To overcome the computational speed decrement the
GA could possible introduce different rules while the demand of low computational
burden could serve through appropriate selection of the fitness function resulting in
more adaptive CA rules and neighborhoods as a compromise between quality and
computational speed.
References
1. Amanatiadis, A., Andreadis, I., Gasteratos, A.: A log-polar interpolation applied to im-
age scaling. In: IEEE International Workshop on Imaging Systems and Techniques,
pp. 1-5. IEEE, Cracovia (2007)
2. Andreadis, I., Illiades, P., Karafyllidis, Y., Tsalides, P., Thanailakis, A.: Design and VLSI
implementation of a new ASIC for colour measurement. IEE Proceedings - Circuits,
Devices and Systems 142(3), 153-157 (1995)
3. Andreadis, I., Karafyllidis, I., Tzionas, P., Thanailakis, A., Tsalides, P.: A new hardware
system for automated visual inspection based on a cellular automaton architecture. J.
Intellig. Robot. Sys. 16, 89-102 (1996)
4. Canny, J.: A computational approach to edge-detection. IEEE Trans. Pattern Anal. Mach.
Intell. 8, 679-700 (1986)
5. Cardenas-Barrera, J.L., Plataniotis, K.N., Venetsanopoulos, A.N.: QCA implementation
of a multichannel filter for image processing. Mathematical Problems in Engineering 8,
87-99 (2002)
6. Cha, Y., Kim, S.: The error-amended sharp edge (EASE) scheme for imaging zooming.
IEEE Trans. Image Process. 16, 1496-1505 (2007)
7. Chen, J.L., Chang, J.Y., Shieh, K.L.: 2D discrete signal interpolation and its image re-
sampling application using fuzzy rule-based inference. Fuzzy Sets Syst. 114, 225-238
(2000)
8. Chen, M.J., Huang, C.H., Lee, W.L.: A fast edge-oriented algorithm for image interpo-
lation. Image and Vision Computing 23, 791-798 (2005)
9. Georgoulas, C., Kotoulas, L., Sirakoulis, G.C., Andreadis, I., Gasteratos, A.: Real-time
disparity map computation module. Microprocess. Microsyst. 32(3), 159-170 (2008)
10. Huang, Y., Fan, H.: Learning from interpolated images using neural networks for digital
forensics. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 177-
182. IEEE, San Francisco (2010)
11. Hwang, J.W., Lee, H.S.: Adaptive image interpolation based on local gradient features.
IEEE Signal Process. Lett. 29, 359-362 (2004)
12. Ioannidis, K., Andreadis, I., Sirakoulis, G.C.: An edge preserving image resizing method
based on cellular automata. In: Sirakoulis, G.C., Bandini, S. (eds.) ACRI 2012. LNCS,
vol. 7495, pp. 375-384. Springer, Heidelberg (2012)
13. Jain, A.K.: Fundamentals of Digital Image Processing. Prentice-Hall, Upper Saddle
River (1978)