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  •   T. Kishan Rao

  •   M. Shankar Lingam

  •   Manish Prateek

  •   E. G. Rajan

Abstract

This paper provides an algorithmic procedure to predict interpolants of zero diluted images. Given a digital image, one can zero dilute it by right adjoining a column consisting of ‘0s’ to every column except the last column and inserting a row consisting of ‘0s’ below every row except the last row. This yields a new image with a size (2W-1)×(2H-1), where W is the width and H is the height of the original image. Another way of zero diluting an image is by right adjoining a column consisting of ‘0s’ to every column and inserting a row consisting of ‘0s’ below every row. This yields a new image with a size (2W)×(2H), where W is the width and H is the height of the original image. Alternatively, subsampling of an image is carried out by forcing pixel values in the alternate columns and rows to zero. Thus, the size of the subsampled image is reduced to half of the size of the original image. This means 75% of the information in the original image is lost in the subsampled image. On the other hand, zero dilution of an image does not cause loss of information but increases the possibility of predicting more information. The question that arises here is whether it is possible to predict more pixel values, which are called interpolants so that the reconstructed image is an enhanced version of the original image in resolution. In this paper, two novel interpolant prediction techniques, which are reliable and computationally efficient, are discussed. They are (i) interpolant prediction using neighborhood pixel value averaging and (ii) interpolant prediction using extended morphological filtering. These techniques can be applied to predict interpolants in a subsampled image also.

Keywords: Ground Penetrating Radar, Zero Diluted Imaging, Targeted Buried Object Detection, Interpolant Prediction

References

Jean Serra; Cube, cube-octahedron or rhomb-dodecahedron as bases for 3-D shape descriptions, Advances in visual form analysis, World Scientific 1997, 502-519.

Wuthrich, C.A. and Stucki, P.; An algorithm comparison between square- and hexagonal based grids; Graphical Models and Image Processing, 53(4), 324–339,1991.

Reinhard Klette and Azriel Rosenfeld; Digital Geometry: Geometric methods for picture analysis; Elsevier, 2004.

B. Nagy; Geometry of Neighborhood sequences in hexagonal grid; Discrete Geometry of computer imagery; LNCS-4245, Springer.

Tristan Roussillon, Laure Tougne, and Isabelle Sivignon; What Does Digital Straightness tell about Digital Convexity?.

Edward Angel, “Interactive Computer Graphics- a top down approach with OpenGL”, Second Edition, Addison Wesley, 2000.

S.W. Zucker, R.A. Hummel, “A Three-Dimensional Edge Operator”, IEEE Trans. On PAMI, Vol. 3, May 1981.

Jürgen, H., Manner, R., Knittel, G., and Strasser, W. (1995). Three Architectures for Volume Rendering. International Journal of the Eurographics Association, 14, 111-122.

Dachille, F. (1997). Volume Visualization Algorithms and Architectures. Research Proficiency Examination, SUNY at Stony Brook.

Günther, T., Poliwoda, C., Reinhart, C., Hesser, J., and Manner, J. (1994). VIRIM: A Massively Parallel Processor for Real-Time Volume Visualization in Medicine. Proceedings of the 9th Eurographics Hardware Workshop, 103-108.

Boer, M.De, Gröpl, A., Hesser, J., and Manner, R. (1996). Latency-and Hazard-free Volume Memory Architecture for Direct Volume Rendering. Eurographics Workshop on Graphics Hardware, 109-119.

Swan, J.E. (1997). Object Order Rendering of Discrete Objects. PhD. Thesis, Department of Computer and Information Science, The Ohio State University.

Yagel, R. (1996). Classification and Survey of Algorithms for Volume Viewing. SIGGRAPH tutorial notes (course 34).

Law, A. (1996). Exploiting Coherency in Parallel Algorithms for Volume Rendering. PhD. Thesis, Department of Computer and Information Science, The Ohio State University.

Ray, H., Pfister, H., Silver, D., and Cook, T.A. (1999). Ray Casting Architectures for Volume Visualization. IEEE Transactions on Visualization and Computer Graphics, 5(3), 210-233.

Yagel, R. (1996). Towards Real Time Volume Rendering. Proceedings of GRAPHICON' 96, 230-241.

Kaufman, A.E. (1994). Voxels as a Computational Representation of Geometry. in The Computational Representation of Geometry SIGGRAPH '94 Course Notes.

Lacroute, P., and Levoy, M. (1994). Fast Volume Rendering Using a Shear-Warp Factorization of the Viewing Transform. Computer Graphics Proceedings Annual Conference Series ACM SIGGRAPH, 451-458.

Sutherland, I.E., Sproull, R.F., and Schumaker, R.A. (1974) A Characterization of Ten Hidden Surface Algorithms. ACM Computing Surveys, 6(1), 1-55.

Roberts, J.C. (1993). An Overview of Rendering from Volume Data including Surface and Volume Rendering. Technical Report 13-93*, University of Kent, Computing Laboratory, Canterbury, UK.

Frieder, G., Gordon, D., and Reynolds, R.A. (1985). Back-to-Front Display of Voxel-Based Objects. IEEE Computer Graphics and Applications, 5(1), 52-60.

Westover, A.L. (1991). Splatting: A Parallel Feed-Forward Volume Rendering Algorithm. Ph.D. Dissertation, Department of Computer Science, The University of North Carolina at Chapel Hill.

Zwicker, M., Pfister., H., Baar, J.B. and Gross M. (2001). Surface Splatting. In Computer Graphics SIGGRAPH 2001 Proceedings, 371-378.

Nulkar, M., and Mueller, K. (2001). Splatting With Shadows. International Workshop on Volume Graphics 2001,35-50.

J. Krüger and R. Westermann, Acceleration Techniques for GPU-based Volume Rendering, Proceedings of the 14th IEEE Visualization 2003 (VIS'03), 38-43.

Markus Hadwiger, Joe M. Kniss, Christ of Rezk-salama, Daniel Weiskopf, and Klaus Engel. Real time Volume Graphics. A. K. Peters, Ltd., USA, 2006.

Goodman, D., Nishimura, Y., Hongo, H and Noriaki N., 2006. Correcting for topography and the tilt of the GPR antenna, Archaeological Prospection, 13: 157-161.

Goodman, D., Y. Nishimura, and J. D. Rogers, 1995. GPR time slices in archaeological prospection: Archaeological Prospection, 2:85-89.

Goodman, D., 1994. Ground-penetrating radar simulation in engineering and archaeology: GEOPHYSICS, 59:224-232.

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How to Cite
[1]
Kishan Rao, T., Shankar Lingam, M., Prateek, M. and Rajan, E.G. 2021. Prediction of Interpolants in Zero Diluted Images. European Journal of Electrical Engineering and Computer Science. 5, 1 (Jan. 2021), 9-16. DOI:https://doi.org/10.24018/ejece.2021.5.1.271.