Abstract :
Abstract - With the rapid developments of higher
resolution imaging systems, larger image data are produced.
To process the increasing image data with conventional
methods, the processing time increases tremendously. Image
segmentation is emerging as a solution for computer vision
and image processing. With the help of several image
processing algorithms efficiency of segmentation can be
improved, and it is widely used in medical imaging (i.e. find
tumor in MRI), robotic vision (i.e. vision-based navigation),
and face recognition. New faster image processing techniques
are needed with their complete database including
algorithms detail, their shortcoming with expected solution
and implementation, to keep up with the ever increasing
image data size. The focus of our study is Watershed and
Clustering algorithm with their modified version to get better
result. Watershed and K-means algorithm are each
considered for their speed, complexity, and utility.
Implementation of each algorithm is then discussed. Finally,
the experimental results of each algorithm are presented and
discussed with quantitative and qualitative comparison.
