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dc.contributor.advisorCrick, Christopher John
dc.contributor.authorRamineni, Mounika
dc.date.accessioned2019-03-25T20:05:18Z
dc.date.available2019-03-25T20:05:18Z
dc.date.issued2017-12-01
dc.identifier.urihttps://hdl.handle.net/11244/317682
dc.description.abstractAutomatic Image Cropping have been enormously popular in the fields of photography, printing industries and in many other fields. It can be usually done in two ways; attention based and aesthetic based. Attention based approach mainly focuses on the important subject in the image whereas aesthetic based approach focuses on the attractiveness of the image. In this project, we proposed a novel attention based approach, which crops out the distracting objects from the image. In our approach, spatially weighted dissimilarity saliency model is used for object detection. In contrast to many methods, cropping of the detected object is done based on the centroid which is obtained from the weighted mean formula. The region of cropping starts from the centroid and is expanded by comparing the surrounding values of centroid with threshold value. The cropped region covers 80% of the total saliency values. Our experiment shows the accomplishment of qualitative image cropping and even the efficiency in terms of time complexity. Moreover, we can demonstrate improvements of our method over recent cropping algorithms on a broad range of images.
dc.formatapplication/pdf
dc.languageen_US
dc.rightsCopyright is held by the author who has granted the Oklahoma State University Library the non-exclusive right to share this material in its institutional repository. Contact Digital Library Services at lib-dls@okstate.edu or 405-744-9161 for the permission policy on the use, reproduction or distribution of this material.
dc.titleSaliency Based Automated Image Cropping
dc.contributor.committeeMemberGeorge, K. M.
dc.contributor.committeeMemberChan-Tin, Eric David
osu.filenameRamineni_okstate_0664M_15550.pdf
osu.accesstypeOpen Access
dc.description.departmentComputer Science
dc.type.genreThesis
dc.type.materialtext


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