Zobrazeno 1 - 10
of 226
pro vyhledávání: '"SHIBA, SHINTARO"'
Publikováno v:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshop On Computer Vision For Mixed Reality (CV4MR), Seattle, 2024
Capturing a 3D human body is one of the important tasks in computer vision with a wide range of applications such as virtual reality and sports analysis. However, conventional frame cameras are limited by their temporal resolution and dynamic range,
Externí odkaz:
http://arxiv.org/abs/2404.08504
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence, Oct. 2023
Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding. However, conventional frame-based techniques require both high spatial and temporal resolution cameras, which impo
Externí odkaz:
http://arxiv.org/abs/2311.00434
Publikováno v:
IEEE Signal Processing Letters, Vol. 29, pp. 2712-2716, 2022
Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-b
Externí odkaz:
http://arxiv.org/abs/2212.12218
Publikováno v:
Advanced Intelligent Systems, 2023
Event cameras are emerging vision sensors and their advantages are suitable for various applications such as autonomous robots. Contrast maximization (CMax), which provides state-of-the-art accuracy on motion estimation using events, may suffer from
Externí odkaz:
http://arxiv.org/abs/2212.07350
Publikováno v:
European Conference on Computer Vision (ECCV), Tel Aviv, 2022
Event cameras respond to scene dynamics and offer advantages to estimate motion. Following recent image-based deep-learning achievements, optical flow estimation methods for event cameras have rushed to combine those image-based methods with event da
Externí odkaz:
http://arxiv.org/abs/2207.10022
Publikováno v:
Sensors 2022, 22(14), 5190
Contrast maximization (CMax) is a framework that provides state-of-the-art results on several event-based computer vision tasks, such as ego-motion or optical flow estimation. However, it may suffer from a problem called event collapse, which is an u
Externí odkaz:
http://arxiv.org/abs/2207.04007
Autor:
Suzuki, Shunsuke, Yagihashi, Takayuki, Nitta, Kazunori, Yamanaka, Masashi, Shimo, Takahiro, Sato, Naoki, Matsubayashi, Nishiki, Takata, Takushi, Sugimoto, Satoru, Hashimoto, Harumitsu, Shiba, Shintaro, Gotoh, Shinichi, Nagata, Hironori, Tanaka, Hiroki
Publikováno v:
In Nuclear Inst. and Methods in Physics Research, A July 2024 1064
Publikováno v:
In Advances in Radiation Oncology April 2024 9(4)
Autor:
Shiba, Shintaro, Okamoto, Masahiko, Shibuya, Kei, Kobayashi, Daijiro, Miyasaka, Yuhei, Ohno, Tatsuya
Publikováno v:
In Clinical and Translational Radiation Oncology January 2024 44
Autor:
Arakawa, Riku, Shiba, Shintaro
We demonstrate the first reinforcement-learning application for robots equipped with an event camera. Because of the considerably lower latency of the event camera, it is possible to achieve much faster control of robots compared with the existing vi
Externí odkaz:
http://arxiv.org/abs/2004.00801