https://scholars.lib.ntu.edu.tw/handle/123456789/339761
標題: | Blurred image detection and classification | 作者: | Hsu, P. BING-YU CHEN |
公開日期: | 2008 | 卷: | 4903 LNCS | 起(迄)頁: | 277-286 | 來源出版物: | Lecture Notes in Computer Science | 摘要: | Digital photos are massively produced while digital cameras are becoming popular, however, not every photo has good quality. Blur is one of the conventional image quality degradation which is caused by various factors. In this paper, we propose a scheme to detect blurred images and classify them into several different categories. The blur detector uses support vector machines to estimate the blur extent of an image. The blurred images are further classified into either locally or globally blurred images. For globally blurred images, we estimate their point spread functions and classify them into camera shake or out of focus images. For locally blurred images, we find the blurred regions using a segmentation method, and the point spread function estimation on the blurred region can sort out the images with depth of field or moving object. The blur detection and classification processes are fully automatic and can help users to filter out blurred images before importing the photos into their digital photo albums. © Springer-Verlag Berlin Heidelberg 2008. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-38549151426&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/339761 https://www.scopus.com/inward/record.uri?eid=2-s2.0-38549151426&doi=10.1007%2f978-3-540-77409-9_26&partnerID=40&md5=86b4bc274878abcfeb477c569a8cd628 |
ISSN: | 03029743 | SDG/關鍵字: | Classification (of information); Image quality; Photography; Quality of service; Image detection; Moving object; Image segmentation |
顯示於: | 資訊管理學系 |
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