Performance evaluation of different Segmentation techniques for Underwater and Arial images
Journal Title: International Journal of Research in Computer and Communication Technology - Year 2014, Vol 3, Issue 1
Abstract
The quality of underwater images is directly affected by water medium, atmosphere medium, pressure and Temperature. Arial images are affected by space temperature and solar noise. The segmentation is the most challenging and important process in the image processing. This emphasizes the necessity of image segmentation, which divides an image into parts that have nearest similarity with objects to reflect the actual information collected from the real world. Feature extraction and recognition have numerous applications on telecommunication, weather forecasting, environment exploration and medical diagnosis. Several general-purpose algorithms and techniques have been developed for image segmentation However ,evaluation of segmentation algorithms thus far has been largely subjective , leaving a system designer to judge the effectiveness of a technique based only on intuition and results in the form of few example segmented images .This is largely due to image segmentation being a ill defined problem-there is no unique ground truth segmentation of an image against which the output of an algorithm may be compared. The performance of an image segmentation algorithm depends on its Simplification of image. In this paper, different segmentation algorithms namely, edge based image segmentation, watershed, and K-means segmentation are implemented for underwater and Arial images and they are compared using quality parameter like PSNR, RMSE and quantitative parameter like Entropy. Out of the above methods the experimental results show that K-means clustering algorithm performs better than other methods in processing underwater images.
Authors and Affiliations
Kelvin Jayantilal Bhalodiya, Kaushal Doshi
Keywords
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