activity
20172022
most citedMastering Sketching: Adversarial Augmentation for Structured Prediction

14 citations · 20 across the 4 of their papers we have counts for

collaborators

7 papers

cs.CV20221 cited

P2Net: A Post-Processing Network for Refining Semantic Segmentation of LiDAR Point Cloud based on Consistency of Consecutive Frames

Yutaka Momma, Weimin Wang, Edgar Simo-Serra +3

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encou…

eess.IV2020

Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks

Naoto Masuzawa, Yoshiro Kitamura, Keigo Nakamura +2

This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks…

cs.CV20205 cited

DeepRemaster: Temporal Source-Reference Attention Networks for Comprehensive Video Enhancement

Satoshi Iizuka, Edgar Simo-Serra

The remastering of vintage film comprises of a diversity of sub-tasks including super-resolution, noise removal, and contrast enhancement which aim to restore the deteriorated film…

cs.CV2020

TopNet: Topology Preserving Metric Learning for Vessel Tree Reconstruction and Labelling

Deepak Keshwani, Yoshiro Kitamura, Satoshi Ihara +2

Reconstructing Portal Vein and Hepatic Vein trees from contrast enhanced abdominal CT scans is a prerequisite for preoperative liver surgery simulation. Existing deep learning base…

cs.CV2020

Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval

Shuhei Yokoo, Kohei Ozaki, Edgar Simo-Serra +1

We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is b…

eess.IV2019

Virtual Thin Slice: 3D Conditional GAN-based Super-resolution for CT Slice Interval

Akira Kudo, Yoshiro Kitamura, Yuanzhong Li +2

Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis…