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pro vyhledávání: '"Qiao, Zhi"'
Over the past decades, we have witnessed a rapid emergence of soft and reconfigurable robots thanks to their capability to interact safely with humans and adapt to complex environments. However, their softness makes accurate control very challenging.
Externí odkaz:
http://arxiv.org/abs/2411.07309
Autor:
Qiao, Zhi, Ouyang, Hanqiang, Chu, Dongheng, Yuan, Huishu, Zhen, Xiantong, Dong, Pei, Qian, Zhen
For surgical planning and intra-operation imaging, CT reconstruction using X-ray images can potentially be an important alternative when CT imaging is not available or not feasible. In this paper, we aim to use biplanar X-rays to reconstruct a 3D CT
Externí odkaz:
http://arxiv.org/abs/2408.09736
Intraoperative CT imaging serves as a crucial resource for surgical guidance; however, it may not always be readily accessible or practical to implement. In scenarios where CT imaging is not an option, reconstructing CT scans from X-rays can offer a
Externí odkaz:
http://arxiv.org/abs/2408.09731
In light of the inherent entailment relations between images and text, hyperbolic point vector embeddings, leveraging the hierarchical modeling advantages of hyperbolic space, have been utilized for visual semantic representation learning. However, p
Externí odkaz:
http://arxiv.org/abs/2408.09715
Autor:
Liu, Xuhui, Qiao, Zhi, Liu, Runkun, Li, Hong, Zhang, Juan, Zhen, Xiantong, Qian, Zhen, Zhang, Baochang
Computed tomography (CT) is widely utilized in clinical settings because it delivers detailed 3D images of the human body. However, performing CT scans is not always feasible due to radiation exposure and limitations in certain surgical environments.
Externí odkaz:
http://arxiv.org/abs/2407.13545
Nowadays, scene text recognition has attracted more and more attention due to its diverse applications. Most state-of-the-art methods adopt an encoder-decoder framework with the attention mechanism, autoregressively generating text from left to right
Externí odkaz:
http://arxiv.org/abs/2312.11923
Scene Text Recognition (STR) is difficult because of the variations in text styles, shapes, and backgrounds. Though the integration of linguistic information enhances models' performance, existing methods based on either permuted language modeling (P
Externí odkaz:
http://arxiv.org/abs/2305.16172
In this technical report, we briefly introduce the solution of our team ''summer'' for Atomospheric Turbulence Mitigation in UG$^2$+ Challenge in CVPR 2022. In this task, we propose a unified end-to-end framework to reconstruct a high quality image f
Externí odkaz:
http://arxiv.org/abs/2210.16847
Autor:
Abulshohoud, Omar, Poudyal, Ishwor, McChesney, Jessica, Zhang, Zhan, Qiao, Zhi, Welp, Ulrich, Islam, Zahir
Dark-field x-ray microscopy utilizes Bragg diffraction to collect full-field x-ray images of "mesoscale" structure of ordered materials. Information regarding the structural heterogeneities and their physical implications is gleaned through the quant
Externí odkaz:
http://arxiv.org/abs/2210.15757
A pump-probe dark-field X-ray microscopy (DFXM) experiment was carried out at the Advanced Photon Source (APS) with nanosecond drive laser pulses and hybrid mode X-ray probe pulses. We observe a thermal decay due to laser-induced heat diffusion in a
Externí odkaz:
http://arxiv.org/abs/2210.06243