Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(160.5KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Two-dimensional Block of Spatial Convolution Algorithm and Simulation
Mussa Mohamed Ahmed
Pages - 243 - 254     |    Revised - 15-07-2012     |    Published - 10-08-2012
Volume - 6   Issue - 4    |    Publication Date - August 2012  Table of Contents
MORE INFORMATION
KEYWORDS
Spatial Convolution, Algorithm, Simulation
ABSTRACT
This paper proposes an algorithm based on sub image-segmentation strategy. The proposed scheme divides a grayscale image into overlapped 6×6 blocks each of which is segmented into four small 3x3 non-overlapped sub-images. A new spatial approach for efficiently computing 2-dimensional linear convolution or cross-correlation between suitable flipped and fixed filter coefficients (sub image for cross-correlation) and corresponding input sub image is presented. Computation of convolution is iterated vertically and horizontally for each of the four input sub-images. The convolution outputs of these four sub-images are processed to be converted from 6×6 arrays to 4×4 arrays so that the core of the original image is reproduced. The present algorithm proposes a simplified processing technique based on a particular arrangement of the input samples, spatial filtering and small sub-images. This results in reducing the computational complexity as compared with other well known FFT-based techniques. This algorithm lends itself for partitioned small sub-images, local image spatial filtering and noise reduction. The effectiveness of the algorithm is demonstrated through some simulation examples.
CITED BY (0)  
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 S. E Umbaugh. Computer Imaging digital image analysis and processing. CRC press Book, 2005, PP.659.
2 A. R. Weeks. Fundamentals of Electronic Image Processing. New Delhi: University of Central Florida and Prentice-Hall of India, 2005, pp.570.
3 A. K. Jain. Fundamentals of Digital image processing. New Delhi: Prentice-Hall of India,1997, PP.569.
4 R. C. Gonzalez, R. E. Woods , S. L. Eddins. Digital image processing using Matlab.Pearson Education. Inc, 2005(2002), PP.310.
5 K. Berberidis. An efficient partitioning-based scheme for 2-D convolution and application to image restoration. IEEE, PP.843-846, 2002.
6 L. Ismail , D. Guerchi. “Performance Evaluation of convolution on the cell broad band engine processor”. IEEE Transactions on parallel and distributed systems, vol. 22, no.2,pp.337-351. 2011.
7 T. C. Lu, S. R. Liao, P.L. Chen, C.C. Chang, Z.H. Wang. “Information hiding technology based on block-partitioning strategy”. ISECS international colloquium computing,communication, control, and management, pp.500-505. 2009.
8 E. D. Gelasca.“Full-reference objective quality metrics for video watermarking, video partitioning and 3D model watermarking”. PhD thesis, PP.187, 2005.
9 R. Cuccchiara, M. Piccardi. “Exploiting image processing locality in cache pre-fetching.In High Performance Computing”. 2002.
10 Li Ma, Y. Wang, T. Tan. “Iris recognition based on multichannel Gabor filtering”. The 5th Asian Conference on Computer Vision, pp.1-5, 2002.
11 D. Agrawal, D. Ali, J.Singhai. “A modified partition fusion technique of mulltifocus images for improved image quality”. Conference - Bioinformatics and image, special issue on ICIT, vol. 4 No. 3, pp.658-663, 2009.
12 T. Bose. Digital signal and image processing. John Wiley & sons.inc, pp.706, 2004.
13 M. Sonka, V. Hlavac, R. Boyle. Image processing analysis and machine vision.Chapman and Hall, PP-555, 1994.
14 M. Holia, V.K.Thakar. “Image registration for recovering affine transformation using Nelder Mead Simplex method for optimization.” IJIP-Journal, vol. 3, Iss.5, pp.218-228,2011.
Associate Professor Mussa Mohamed Ahmed
Aden university - Yemen
mussa_m7@yahoo.com