1 citations · 3 across the 5 of their papers we have counts for
6 papers
Deep Depth from Focal Stack with Defocus Model for Camera-Setting Invariance
Yuki Fujimura, Masaaki Iiyama, Takuya Funatomi +1
We propose a learning-based depth from focus/defocus (DFF), which takes a focal stack as input for estimating scene depth. Defocus blur is a useful cue for depth estimation. Howeve…
Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media with Airlight and Scattering Coefficient Estimation
Yuki Fujimura, Motoharu Sonogashira, Masaaki Iiyama
We propose a learning-based multi-view stereo (MVS) method in scattering media, such as fog or smoke, with a novel cost volume, called the dehazing cost volume. Images captured in…
Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift
Ryuhei Takahashi, Atsushi Hashimoto, Motoharu Sonogashira +1
This paper proposes a novel approach for unsupervised domain adaptation (UDA) with target shift. Target shift is a problem of mismatch in label distribution between source and targ…
Defogging Kinect: Simultaneous Estimation of Object Region and Depth in Foggy Scenes
Yuki Fujimura, Motoharu Sonogashira, Masaaki Iiyama
Three-dimensional (3D) reconstruction and scene depth estimation from 2-dimensional (2D) images are major tasks in computer vision. However, using conventional 3D reconstruction te…
Photometric Stereo in Participating Media Considering Shape-Dependent Forward Scatter
Yuki Fujimura, Masaaki Iiyama, Atsushi Hashimoto +1
Images captured in participating media such as murky water, fog, or smoke are degraded by scattered light. Thus, the use of traditional three-dimensional (3D) reconstruction techni…
Outlier Cluster Formation in Spectral Clustering
Takuro Ina, Atsushi Hashimoto, Masaaki Iiyama +3
Outlier detection and cluster number estimation is an important issue for clustering real data. This paper focuses on spectral clustering, a time-tested clustering method, and reve…