https://scholars.lib.ntu.edu.tw/handle/123456789/580968
標題: | Generic Image Segmentation in Fully Convolutional Networks by Superpixel Merging Map | 作者: | Huang J.-Y JIAN-JIUN DING |
關鍵字: | Computer vision; Convolution; Convolutional neural networks; Deep learning; Learning algorithms; Merging; Semantics; Superpixels; Convolutional networks; Learning-based algorithms; Learning-based methods; Segmentation algorithms; Segmentation methods; Segmentation process; Segmentation results; Semantic segmentation; Image segmentation | 公開日期: | 2021 | 卷: | 12622 LNCS | 起(迄)頁: | 723-737 | 來源出版物: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 摘要: | Recently, the Fully Convolutional Network (FCN) has been adopted in image segmentation. However, existing FCN-based segmentation algorithms were designed for semantic segmentation. Before learning-based algorithms were developed, many advanced generic segmentation algorithms are superpixel-based. However, due to the irregular shape and size of superpixels, it is hard to apply deep learning to superpixel-based image segmentation directly. In this paper, we combined the merits of the FCN and superpixels and proposed a highly accurate and extremely fast generic image segmentation algorithm. We treated image segmentation as multiple superpixel merging decision problems and determined whether the boundary between two adjacent superpixels should be kept. In other words, if the boundary of two adjacent superpixels should be deleted, then the two superpixels will be merged. The network applies the colors, the edge map, and the superpixel information to make decision about merging suprepixels. By solving all the superpixel-merging subproblems with just one forward pass, the FCN facilitates the speed of the whole segmentation process by a wide margin meanwhile gaining higher accuracy. Simulations show that the proposed algorithm has favorable runtime, meanwhile achieving highly accurate segmentation results. It outperforms state-of-the-art image segmentation methods, including feature-based and learning-based methods, in all metrics. ? 2021, Springer Nature Switzerland AG. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103282703&doi=10.1007%2f978-3-030-69525-5_43&partnerID=40&md5=2b0acc7adeb9655a05196f9ee610c5d5 https://scholars.lib.ntu.edu.tw/handle/123456789/580968 |
ISSN: | 03029743 | DOI: | 10.1007/978-3-030-69525-5_43 |
顯示於: | 電機工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。