Abstract
One of the crucial problems in image processing is Image Matching, i.e., to match two images, or in our case, to match a model with the given image. This problem being highly computation intensive, parallel processing is essential to obtain the solutions in time due to real world constraints. The Hausdorff method is used to locate human beings in images by matching the image with models and is parallelized with MPI. The images are usually stored in files with different formats. As most of the formats can be converted into ASCII file format containing integers, we have implemented 3 strategies namely, Normal File Reading, Off-line Conversion and Run-time Conversion for free format integer file reading and writing. The parallelization strategy is optimized so that I/O overheads are minimal. The relative performances with multiple processors are tabulated for all the cases and discussed. The results obtained demonstrate the efficiency of our strategies and the implementations will enhance the file interoperability which will be useful for image processing community to use parallel systems to meet the real time constraints.
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Piriyakumar, D.A.L., Levi, P., Rabenseifner, R. (2002). Enhanced File Interoperability with Parallel MPI File-I/O in Image Processing. In: Kranzlmüller, D., Volkert, J., Kacsuk, P., Dongarra, J. (eds) Recent Advances in Parallel Virtual Machine and Message Passing Interface. EuroPVM/MPI 2002. Lecture Notes in Computer Science, vol 2474. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45825-5_32
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DOI: https://doi.org/10.1007/3-540-45825-5_32
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