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Named Entity Recogniser with Crawler
Developed a Named Entity Recogniser using Hidden Markov Model which understood the need for external knowledge resources and non‐local features to leverage the multiple occurrences of named entities like Person, Location and Organisation in any text without use of any dictionaries. This project automates the process of lookup and identification of the entities by linking to a comprehensive crawler that can be made to perform various kinds of crawling as per the need and usage.
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Content-Aware Saliency and Image Re-targeting
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Content aware image retargeting methods intelligently modify the aspect ratio of images while preserving the visually important features. The seam carving algorithm [Shamir et al. (2007)[3]] finds an optimal 8 connected path from the top to bottom of the image for horizontal resizing or from left to right for vertical resizing. This optimization is done by using a visual importance map (energy function). While, existing works have primarily used the gradient magnitude as the visual importance…
Content aware image retargeting methods intelligently modify the aspect ratio of images while preserving the visually important features. The seam carving algorithm [Shamir et al. (2007)[3]] finds an optimal 8 connected path from the top to bottom of the image for horizontal resizing or from left to right for vertical resizing. This optimization is done by using a visual importance map (energy function). While, existing works have primarily used the gradient magnitude as the visual importance map, we employ segmentation and content aware visual saliency features in addition to the gradient magnitude intensity to define a state of the art energy function. This use of additional features result in improved preservation of the visually salient regions and also their immediate context. In this report, we presented this novel technique for image retargeting and compared our results with those by other prominent researchers in the field.
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Honors & Awards
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Student of the Year 2015 - NIT Delhi
Director, NIT Delhi
Languages
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Hindi
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English
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Punjabi
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Haryanvi
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