Balancing fusion, image depth and distortion in stereoscopic head-tracked displays

1999 ◽  
Author(s):  
Zachary Wartell ◽  
Larry F. Hodges ◽  
William Ribarsky
Keyword(s):  
Fusion Image ◽  
Image Depth

Fusion Image of Coronary Artery and Myocardial Perfusion Using Computed Tomography

2015 ◽  
Vol 99 (2) ◽  
pp. 715
Author(s):  
Teruhito Kido ◽  
Akira Kuata ◽  
Teruyoshi Uetani ◽  
Yuki Tanabe ◽  
Hikaru Nishiyama ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Fusion Image

Fusion Image–Based Programming After Subthalamic Nucleus Deep Brain Stimulation

2011 ◽  
Vol 75 (3-4) ◽  
pp. 517-524 ◽  
Author(s):  
Sun Ha Paek ◽  
Hee Jin Kim ◽  
Ji Young Yoon ◽  
Jae Heok Heo ◽  
Cheolyoung Kim ◽  
...  
Keyword(s):  
Deep Brain Stimulation ◽  
Subthalamic Nucleus ◽  
Brain Stimulation ◽  
Fusion Image ◽  
Deep Brain

scholarly journals Prostate cancer recurrence in vas deferens – fusion image guide as an important tool in dignosis

2018 ◽  
Vol 44 (1) ◽  
pp. 192-195 ◽  
Author(s):  
Leonardo Guedes Moreira Valle ◽  
Antônio Rahal Jr. ◽  
Priscila Mina Falsarella ◽  
Juliano Ribeiro de Andrade ◽  
Oren Smaletz ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Vas Deferens ◽  
Cancer Recurrence ◽  
Fusion Image ◽  
Image Guide ◽  
Prostate Cancer Recurrence

scholarly journals The Synthesis of Unpaired Underwater Images for Monocular Underwater Depth Prediction

2021 ◽  
Vol 8 ◽  
Author(s):  
Qi Zhao ◽  
Ziqiang Zheng ◽  
Huimin Zeng ◽  
Zhibin Yu ◽  
Haiyong Zheng ◽  
...  
Keyword(s):  
Structural Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Image Synthesis ◽  
Map Estimation ◽  
Estimation Model ◽  
Depth Prediction ◽  
Underwater Image ◽  
Underwater Vision ◽  
Image Depth

Underwater depth prediction plays an important role in underwater vision research. Because of the complex underwater environment, it is extremely difficult and expensive to obtain underwater datasets with reliable depth annotation. Thus, underwater depth map estimation with a data-driven manner is still a challenging task. To tackle this problem, we propose an end-to-end system including two different modules for underwater image synthesis and underwater depth map estimation, respectively. The former module aims to translate the hazy in-air RGB-D images to multi-style realistic synthetic underwater images while retaining the objects and the structural information of the input images. Then we construct a semi-real RGB-D underwater dataset using the synthesized underwater images and the original corresponding depth maps. We conduct supervised learning to perform depth estimation through the pseudo paired underwater RGB-D images. Comprehensive experiments have demonstrated that the proposed method can generate multiple realistic underwater images with high fidelity, which can be applied to enhance the performance of monocular underwater image depth estimation. Furthermore, the trained depth estimation model can be applied to real underwater image depth map estimation. We will release our codes and experimental setting in https://github.com/ZHAOQIII/UW_depth.

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