Empirical Evaluation of Suitable Segmentation Algorithms for IR Images
Image segmentation is the first stage of processing in many
practical computer vision systems. Development of
segmentation algorithms has attracted considerable research
interest, relatively little work has been published on the
subject of their evaluation. Hence this paper enumerates and
reviews mainly the image segmentation algorithms namely
Otsu, Fuzzy C means, Global Active Contour / Snake model
and Watershed. These suitable segmentation methods are
implemented for IR images and are evaluated based on the
parameters. The parameters are Variation Of Information
(VOI), Global Consistency Error (GCE) and Probabilistic
Rand Index (PRI). The objective of the paper is to identify
the best segmentation algorithm that is suitable for IR
images. From the experimentation and evaluation it is
observed that the Global Active Contour/Snake model works
better compared to other methods for IR images.
Keywords: IR Image, Segmentation, Otsu, Global Active
Contour/Snake, Fuzzy C Means, Watershed
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