In this paper, with the goal of quantifying the qualitative image outputs of
a Vision-based Tactile Sensor (VTS), we present the design, fabrication, and
characterization of a novel Quantitative Surface Tactile Sensor (called QS-TS).
QS-TS directly estimates the sensor's gel layer deformation in real-time
enabling safe and autonomous tactile manipulation and servoing of delicate
objects using robotic manipulators. The core of the proposed sensor is the
utilization of miniature 1.5 mm x 1.5 mm synthetic square markers with inner
binary patterns and a broad black border, called ArUco Markers. Each ArUco
marker can provide real-time camera pose estimation that, in our design, is
used as a quantitative measure for obtaining deformation of the QS-TS gel
layer. Moreover, thanks to the use of ArUco markers, we propose a unique
fabrication procedure that mitigates various challenges associated with the
fabrication of the existing marker-based VTSs and offers an intuitive and
less-arduous method for the construction of the VTS. Remarkably, the proposed
fabrication facilitates the integration and adherence of markers with the gel
layer to robustly and reliably obtain a quantitative measure of deformation in
real-time regardless of the orientation of ArUco Markers. The performance and
efficacy of the proposed QS-TS in estimating the deformation of the sensor's
gel layer were experimentally evaluated and verified. Results demonstrate the
phenomenal performance of the QS-TS in estimating the deformation of the gel
layer with a relative error of <5%.

本文介绍了一种新型量化表面触觉传感器（称为 QS-TS），该传感器利用 ArUco 标记实时估计凝胶层的变形，能够安全和自主地使用机器人操纵器进行触觉操作和伺服精细物体。实验结果展示了 QS-TS 在估计凝胶层变形方面出色的性能，相对误差小于 5%。

基于 ArUco 标记的表面触觉传感器的设计与开发

Towards Design and Development of an ArUco Markers-Based Quantitative  Surface Tactile Sensor

The Transformer structures have been widely used in computer vision and have
recently made an impact in the area of medical image registration. However, the
use of Transformer in most registration networks is straightforward. These
networks often merely use the attention mechanism to boost the feature learning
as the segmentation networks do, but do not sufficiently design to be adapted
for the registration task. In this paper, we propose a novel motion
decomposition Transformer (ModeT) to explicitly model multiple motion
modalities by fully exploiting the intrinsic capability of the Transformer
structure for deformation estimation. The proposed ModeT naturally transforms
the multi-head neighborhood attention relationship into the multi-coordinate
relationship to model multiple motion modes. Then the competitive weighting
module (CWM) fuses multiple deformation sub-fields to generate the resulting
deformation field. Extensive experiments on two public brain magnetic resonance
imaging (MRI) datasets show that our method outperforms current
state-of-the-art registration networks and Transformers, demonstrating the
potential of our ModeT for the challenging non-rigid deformation estimation
problem. The benchmarks and our code are publicly available at
this https URL

本文提出一种新颖的运动分解 Transformer (ModeT), 用于像医学图像配准中的非刚性变形估计，出色地超越了当前最先进的配准网络和 Transformer, 并展示了我们的 ModeT 的潜力。

通过运动分解 Transformer 学习可变形图像配准

ModeT: Learning Deformable Image Registration via Motion Decomposition  Transformer

Brain midline shift (MLS) is one of the most critical factors to be
considered for clinical diagnosis and treatment decision-making for
intracranial hemorrhage. Existing computational methods on MLS quantification
not only require intensive labeling in millimeter-level measurement but also
suffer from poor performance due to their dependence on specific landmarks or
simplified anatomical assumptions. In this paper, we propose a novel
semi-supervised framework to accurately measure the scale of MLS from head CT
scans. We formulate the MLS measurement task as a deformation estimation
problem and solve it using a few MLS slices with sparse labels. Meanwhile, with
the help of diffusion models, we are able to use a great number of unlabeled
MLS data and 2793 non-MLS cases for representation learning and regularization.
The extracted representation reflects how the image is different from a non-MLS
image and regularization serves an important role in the sparse-to-dense
refinement of the deformation field. Our experiment on a real clinical brain
hemorrhage dataset has achieved state-of-the-art performance and can generate
interpretable deformation fields.

本文提出了一个新的半监督框架，通过使用稀疏标记的 MLS 切片和扩散模型对大量未标记的 MLS 数据和 2793 个非 MLS 案例进行表征学习和正则化，准确测量头部 CT 扫描中 MLS 的规模，从而实现了对临床脑出血数据集的最新成果性能的实验，并能够生成可解释的形变场。