DC Field | Value | Language |
---|---|---|
dc.contributor.author | 홍은빈 | - |
dc.date.accessioned | 2018-10-17T05:48:13Z | - |
dc.date.available | 2018-10-17T05:48:13Z | - |
dc.date.issued | 2018 | - |
dc.identifier.other | OAK-2015-08037 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000007372 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/93584 | - |
dc.description | Master | - |
dc.description.abstract | This thesis presents a novel image composition enhancement framework using a repeated cropping and horizon correction based on a deep convolutional neural network. Firstly, we propose a repeated cropping technique for photo re-composition. For a given image, a trained network iteratively predicts cropping directions which make the image to have a better composition. The system can automatically and gradually crop the photo to follow specific composition guidelines, such as the rule of thirds and salient object size. We also propose a photo horizon correction method using convolutional neural network. The trained network estimates a slanted angle by learning generic features using a huge dataset. To obtain better performance, we utilize multiple-sized pooling layers to extract multi-scale image features. By combining proposed methods, an aesthetic composition of a given photo can be enhanced automatically and effectively, as shown in experimental results. | - |
dc.language | kor | - |
dc.publisher | 포항공과대학교 | - |
dc.title | 반복적 크롭핑과 수평 보정을 이용한 영상 구도 개선 | - |
dc.type | Thesis | - |
dc.contributor.college | 일반대학원 컴퓨터공학과 | - |
dc.date.degree | 2018- 2 | - |
dc.type.docType | Thesis | - |
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