An introduction to parallel algorithms
An introduction to parallel algorithms
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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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.