Share Email Print

Proceedings Paper

Image processing spreadsheet
Author(s): Claudio Delrieux; Gustavo Ramoscelli; Leonardo Arlenghi; Alejandro Vitale
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Consider an hypothetical image processing system, where a given target is to be identified. The usual sequence of steps consists on an image equalization to adapt to the illumination situation. Then the image is binarized, allowing a morphological filter to correct the noisy edges and shapes by means of an indeterminate sequence of openings or closings. The resulting image can then be segmented and recognized. If the results are unsatisfactory, then the processing parameters in any of the previous steps must be changed, perhaps by trial and error. For instance, the binarization threshold can be raised or lowered, and the following steps must be performed again to see the results. This is obviously cumbersome, tedious and error prone. The Image Processing Spreadsheet PDICalc is a simple but powerful combination of two different and widespread software technologies. It's benefit comes from enabling users to build an image processing pipeline, considering each step separately, and visualizing the results of modifying the parameters of each step in the final image. A spreadsheet based user interface eliminates the tedious and repetitive interaction that characterizes current image processing software. Users can build a processing template and reliably repeat often needed processing without the effort of redevelopment or recoding. In the cited example the user simply creates the processing template, defining each cell of the spreadsheet as the result of applying a given processing step on another cell. This template can be then reused with any input image, can be stored for future processing sessions, and every step can be trimmed precisely to achieve the desired results. Our implementation considers most of the image processing techniques as its building blocks. Arithmetic operators are overloaded to represent per pixel operations. We included also equalization and histogram correction, arbitrary convolution filtering, arbitrary morphological filtering (with programmed repetition), Fourier operations, and several segmentation techniques.

Paper Details

Date Published: 7 June 2002
PDF: 12 pages
Proc. SPIE 4735, Hybrid Image and Signal Processing VIII, (7 June 2002); doi: 10.1117/12.470104
Show Author Affiliations
Claudio Delrieux, Univ. Nacional del Sur (Argentina)
Gustavo Ramoscelli, Univ. Nacional del Sur (Argentina)
Leonardo Arlenghi, Univ. Nacional del Sur (Argentina)
Alejandro Vitale, Univ. Nacional del Sur (Argentina)

Published in SPIE Proceedings Vol. 4735:
Hybrid Image and Signal Processing VIII
David P. Casasent; Andrew G. Tescher, Editor(s)

© SPIE. Terms of Use
Back to Top