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- W2601545956 abstract "Image, audio and video are the popular entertainment and communication services of internet. Sometime they suffer from many problems, Blur is one of them. Blur is a factor that breakdown the status of image. In this paper, we are going to perform comparison of four different Blur Detection classifiers. This paper introduces our proposed technique (Hybrid Classifier). To verify the accuracy of hybrid Classifier we collect 1000 images from internet and hence results are predicted. From result and discussion, it is clear that Proposed Classifier give 96% accuracy which is 10% more than existing Classifier (SVM)." @default.
- W2601545956 created "2017-04-07" @default.
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- W2601545956 date "2016-06-14" @default.
- W2601545956 modified "2023-09-24" @default.
- W2601545956 title "A Novel Hybrid Classification Technique for Blur Detection" @default.
- W2601545956 hasPublicationYear "2016" @default.
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