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A Nobel Blur Detection Classification Technique using KNN Classifier


Ravi Saini , Doon valley institute of engg. and technnology karnal, haryana; Sarita Gera, doon valley institute of engg. and technology karnal, haryana


Blur detection, SVM support vector machine KNN (nearest neighbour), Hybrid Classifier and DCT Feature Extraction


Blur Detection, in this when the image is spital varying, then the blur detection in the picture is quite difficult task, As the image is captured, the image can blured due motion and out of focus parameters and on the basis of this, blur can be devided as the local blur and global blur. Blur detection is basically to separate the Blured part and Unblured part. In this work, the use of KNN (nearest neighbor) search algorithm allows the recovering as much information as possible from the available data. An image look more sharp or more detailed if able to perceive all the objects and their shape correctly in it. The shape of an object due to its edges. So, in blurring, simply reduce the edge content and make the transition from one colour to the other very smooth. The reviewed paper proposed method of classifying discriminative feature of blur detection Using SVM Based Classifier. But there were few limitations associated with SVM (support Vector Machine) based Classifier, they failed to segment the image properly and has inherent problems of kernels which are solved by the use of KNN search algorithm which improves the Blur Detection technique and classifies the Blur area more accurately. The hybrid classifier, which is basically the combination of the two SVM and KNN classifier shows the more better and accurate results of the blured Detection.

Other Details

Paper ID: IJSRDV4I50723
Published in: Volume : 4, Issue : 5
Publication Date: 01/08/2016
Page(s): 1371-1376

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