Ken Sakurada

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This paper proposes a method for detecting changes of a scene using a pair of its vehicular, omnidirectional images. Previous approaches to the problem require the use of a 3D scene model and/or pixel-level registration between different time images. They are also computationally costly for estimating city-scale changes. We propose a novel change detection(More)
This paper proposes a method for detecting temporal changes of the three-dimensional structure of an outdoor scene from its multi-view images captured at two separate times. For the images, we consider those captured by a camera mounted on a vehicle running in a city street. The method estimates scene structures probabilistically, not deterministically, and(More)
This paper presents a novel method for fully automatic and convenient extrinsic calibration of a 3D LiDAR and a panoramic camera with a normally printed chessboard. The proposed method is based on the 3D corner estimation of the chessboard from the sparse point cloud generated by one frame scan of the LiDAR. To estimate the corners, we formulate a(More)
Automated visual analysis is an effective method for understanding changes in natural phenomena over massive city-scale landscapes. However, the view-point spectrum across which image data can be acquired is extremely wide, ranging from macro-level overhead (aerial) images spanning several kilometers to micro-level front-parallel (streetview) images that(More)
This paper explores the effective use of Convolutional Neural Networks (CNNs) in the context of washed-away building detection from pre- and post-tsunami aerial images. To this end, we compile a dedicated, labeled aerial image dataset to construct models that classify whether a building is washed-away. Each datum in the set is a pair of pre- and(More)
Gyro-based odometry is an easy-to-use localization method for tracked vehicles because it uses only internal sensors. However, on account of track-terrain slippage and transformation caused by changes in sub-track angles, gyro-based odometry for tracked vehicles with sub-tracks experiences difficulties in estimating the exact location of the vehicles. In(More)
In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation(More)