位相相関とCNNによる移動推定とSuperpixelsを用いた物体の形状変形に対応可能なオプティカルフロー推定

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2022/01/01

タイトル(英語): Opticalflow Estimation for Deformable Object Utilizing Superpixels and Motion Estimation Using Phase Correlation and CNN

著者名: 大石　修也（横浜国立大学大学院理工学府），濱上　知樹（横浜国立大学大学院理工学府）

著者名(英語): Nobuya Oishi (Graduate School of Engineering Science, Yokohama National University), Tomoki Hamagami (Graduate School of Engineering Science, Yokohama National University)

キーワード: ブロックマッチング，位相相関，オプティカルフロー　　block matching，phase correlation，optical flow

要約(英語): There are roughly two methods for estimating optical flow. One is the gradient method and the other is the block matching method. Each method has a few problems. In the gradient method, it is difficult to estimate optical flow when images include a noise and the difference in brightness between adjacent pixels is small. In the block matching method, an accuracy depends on the block division method and it isn't able to deal with a non-rigid body. Therefore, it is difficult to estimate the optical flow in a video that is easily affected by noise and exists deforming objects such as medical images. For each of these problems, we propose a method for estimating optical flow that is robust especially for object deformation while concerning a noise. The proposed method is based on the block matching method and has two improvements. One is to use Superpixels for block division. The other is to use the phase correlation for displacement estimation. Furthermore, we extend it by deep learning to be robust for a complicated change. The experimental results show the proposed method was robust to the object deforming in the medical image dataset.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.142 No.1 （2022） 特集：電子回路関連技術

本誌掲載ページ: 100-107 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/142/1/142_100/_article/-char/ja/