Road Crack Detection using Gaussian of Mixture Model Technique |
Author(s): |
| E. Subashri , Sadakathullah Appa College; S. Shajun Nisha, Sadakathullah Appa College; M. Mohamed Sathik, Sadakathullah Appa College |
Keywords: |
| Road Images, Histogram Equalization, GMM |
Abstract |
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Clustering can be considered the most important unsupervised learning problem so, as every other problem of this kind; it deals with finding a structure in a collection of unlabeled data. The most widely used clustering method of this kind is the one based on learning a mixture of Gaussians it can actually consider clusters as Gaussian distributions centered. It is more flexible because you can view it as a fuzzy or soft clustering method. Soft clustering methods assign a score to a data point for each cluster. In this paper GMM clustering algorithm is applied into the road crack images and the performance parameters shows how the GMM is performed and find the cracks on the road. |
Other Details |
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Paper ID: IJSRDV5I40943 Published in: Volume : 5, Issue : 4 Publication Date: 01/07/2017 Page(s): 826-829 |
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