Typically, tennis court line detection is done by running
Hough-Line-Detection to find straight lines in the image, and then computing a
transformation matrix from the detected lines to create the final court
structure. We propose numerous improvements and enhancements to this algorithm,
including using pretrained State-of-the-Art shadow-removal and object-detection
ML models to make our line-detection more robust. Compared to the original
algorithm, our method can accurately detect lines on amateur, dirty courts.
When combined with a robust ball-tracking system, our method will enable
accurate, automatic refereeing for amateur and professional tennis matches
alike.

我们提出了改进和增强的算法，通过使用预训练的最先进的阴影去除和物体检测机器学习模型，使我们的线检测更加稳健，可以准确检测业余、脏乱的网球场地。结合强大的球追踪系统，我们的方法将能够为业余和职业网球比赛提供准确的自动裁判。

业余网球比赛的准确场地线检测

Accurate Tennis Court Line Detection on Amateur Recorded Matches

Articulated objects (e.g., doors and drawers) exist everywhere in our life.
Different from rigid objects, articulated objects have higher degrees of
freedom and are rich in geometries, semantics, and part functions. Modeling
different kinds of parts and articulations with nerual networks plays an
essential role in articulated object understanding and manipulation, and will
further benefit 3D vision and robotics communities. To model articulated
objects, most previous works directly encode articulated objects into feature
representations, without specific designs for parts, articulations and part
motions. In this paper, we introduce a novel framework that explicitly
disentangles the part motion of articulated objects by predicting the
transformation matrix of points on the part surface, using spatially continuous
neural implicit representations to model the part motion smoothly in the space.
More importantly, while many methods could only model a certain kind of joint
motion (such as the revolution in the clockwise order), our proposed framework
is generic to different kinds of joint motions in that transformation matrix
can model diverse kinds of joint motions in the space. Quantitative and
qualitative results of experiments over diverse categories of articulated
objects demonstrate the effectiveness of our proposed framework.

通过神经网络模型，本研究提出一种新的框架来明确分解关节物体的部分运动，通过预测部分表面上的点的转换矩阵，使用空间连续的神经隐式表示来平滑地建模部分运动，在对不同类别的关节物体进行实验的数量化和质量化结果证明了我们的方法的有效性。

利用空间连续的神经隐式表示学习关节物体的分部运动

Learning Part Motion of Articulated Objects Using Spatially Continuous  Neural Implicit Representations

Given a random pair of images, an arbitrary style transfer method extracts
the feel from the reference image to synthesize an output based on the look of
the other content image. Recent arbitrary style transfer methods transfer
second order statistics from reference image onto content image via a
multiplication between content image features and a transformation matrix,
which is computed from features with a pre-determined algorithm. These
algorithms either require computationally expensive operations, or fail to
model the feature covariance and produce artifacts in synthesized images.
Generalized from these methods, in this work, we derive the form of
transformation matrix theoretically and present an arbitrary style transfer
approach that learns the transformation matrix with a feed-forward network. Our
algorithm is highly efficient yet allows a flexible combination of multi-level
styles while preserving content affinity during style transfer process. We
demonstrate the effectiveness of our approach on four tasks: artistic style
transfer, video and photo-realistic style transfer as well as domain
adaptation, including comparisons with the state-of-the-art methods.

本研究提出了一种基于前向网络学习转换矩阵的任意风格转换方法，该方法高效且具有灵活组合多级风格的能力，同时保持风格转移过程中的内容亲和性，成功应用于艺术风格转换、视频和照片逼真的风格转换以及领域适应等四种任务，并与最先进的方法进行了比较。