Seamline determination based on semantic segmentation for aerial image mosaicking

Shunta Saito, Ryota Arai, Yoshimitsu Aoki

研究成果: Article査読

10 被引用数 (Scopus)

抄録

We propose a novel method for seamline determination based on semantic segmentation for aerial image mosaicking. First, we train a convolutional neural network (CNN) for pixel labeling to extract building regions. Using the trained CNN, we create a building probability map from an input aerial image with no pre-processing. We then use Dijkstra's algorithm to find the optimal seamline as a shortest path on the map. We evaluate the quality of the seamlines produced by our method on actual aerial images. Finally, we show that our seamlines never pass through any buildings and compare the effectiveness with the conventional mean-shift segmentation-based method.

本文言語English
論文番号7355281
ページ(範囲)2847-2856
ページ数10
ジャーナルIEEE Access
3
DOI
出版ステータスPublished - 2015 12 17

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

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