Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2325
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dc.contributor.authorTripathy, D P-
dc.contributor.authorReddy, R G R-
dc.date.accessioned2015-05-26T13:22:59Z-
dc.date.available2015-05-26T13:22:59Z-
dc.date.issued2015-05-
dc.identifier.citation11th Annual International Conference on Information & Computer Science, Athens, Greece,18- 21 May 2015.en_US
dc.identifier.urihttp://hdl.handle.net/2080/2325-
dc.descriptionCopyright belong to proceeding publisheren_US
dc.description.abstractOre sorting is a useful tool to remove gangue material from the ore and increase the quality of the ore. The vast developments in the area of artificial intelligence allows fast processing of full color digital images for the preferred investigations. In this paper, a novel approach to categorize the ores of iron feed has been proposed based on analyzing color­texture features using two different approaches, based on extensions of the co­occurrence matrix method. In the first method, co­occurrence matrices were computed both between and within the color bands and the second method used joint color­texture features. The dimensions of the image features were reduced by applying Euclidean Distance ranking. A multi­layer perceptron neural network model was used as a mapping function to classify the material.en_US
dc.language.isoenen_US
dc.publisherATINERen_US
dc.subjectNovel approachen_US
dc.subjectCo­-occurrence matrixen_US
dc.subjectGangue separationen_US
dc.subjectIronen_US
dc.titleA Novel Approach of Co­Occurrence Matrix for Gangue Separation from Ironen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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