Abstract:
The massive volume of video and image data,
compels them to be stored in a distributed file system. To process
the data stored in the distributed file system, Google proposed
a programming model named MapReduce. Existing methods of
processing images held in such a distributed file system, requires
whole image or a substantial portion of the image to be streamed
every time a filter is applied. In this work an image filtering
technique using MapReduce programming model is proposed,
which only requires the image to be streamed only once. The
proposed technique extends for a cascade of image filters with the
constrain of a fixed kernel size. To verify the proposed technique
for a single filter a median filter is applied on an image with
salt and pepper noise. In addition a corner detection algorithm
is implemented with the use of a filter cascade. Comparison
of the results of noise filtering and corner detection with the
corresponding CPU version show the accuracy of the method.
