In this paper, we demonstrate a novel algorithm that uses ellipse fitting to
estimate the bounding box rotation angle and size with the segmentation(mask)
on the target for online and real-time visual object tracking. Our method,
SiamMask_E, improves the bounding box fitting procedure of the state-of-the-art
object tracking algorithm SiamMask and still retains a fast-tracking frame rate
(80 fps) on a system equipped with GPU (GeForce GTX 1080 Ti or higher). We
tested our approach on the visual object tracking datasets (VOT2016, VOT2018,
and VOT2019) that were labeled with rotated bounding boxes. By comparing with
the original SiamMask, we achieved an improved Accuracy of 0.652 and 0.309 EAO
on VOT2019, which is 0.056 and 0.026 higher than the original SiamMask. The
implementation is available on GitHub:
this https URL

本文提出了一种新的算法，使用椭圆拟合来估计带有分割（掩膜）的目标的边界框旋转角度和大小，即 SiamMask_E 算法，在保持快速跟踪帧率 (80 fps) 的前提下，改善了现有 SiamMask 算法的边框拟合过程。在使用旋转边框标记的视觉对象跟踪数据集上测试，与原始 SiamMask 相比，我们实现了 0.652 和 0.309 的提升 EAO 的精度，在 VOT2019 上分别比原始 SiamMask 高 0.056 和 0.026。

旋转边界框快速视觉目标跟踪

Fast Visual Object Tracking with Rotated Bounding Boxes

We propose a deep learning-based technique for detection and quantification
of abdominal aortic aneurysms (AAAs). The condition, which leads to more than
10,000 deaths per year in the United States, is asymptomatic, often detected
incidentally, and often missed by radiologists. Our model architecture is a
modified 3D U-Net combined with ellipse fitting that performs aorta
segmentation and AAA detection. The study uses 321 abdominal-pelvic CT
examinations performed by Massachusetts General Hospital Department of
Radiology for training and validation. The model is then further tested for
generalizability on a separate set of 57 examinations with differing patient
demographics and acquisition characteristics than the original dataset. DeepAAA
achieves high performance on both sets of data (sensitivity/specificity
0.91/0.95 and 0.85 / 1.0 respectively), on contrast and non-contrast CT scans
and works with image volumes with varying numbers of images. We find that
DeepAAA exceeds literature-reported performance of radiologists on incidental
AAA detection. It is expected that the model can serve as an effective
background detector in routine CT examinations to prevent incidental AAAs from
being missed.

本研究提出了一种基于深度学习的技术，用于检测和量化腹主动脉瘤，该模型架构是一个改进的 3D U-Net 算法与椭圆拟合相结合，可用于主动脉分割和腹主动脉瘤检测，模型经过 321 个腹部 - 盆腔 CT 检查的训练和验证，在 57 个新的检查数据上也具备很高的性能。

DeepAAA：利用深度学习进行临床可应用和普适性的腹主动脉瘤检测

DeepAAA: clinically applicable and generalizable detection of abdominal  aortic aneurysm using deep learning

Iris centre localization in low-resolution visible images is a challenging
problem in computer vision community due to noise, shadows, occlusions, pose
variations, eye blinks, etc. This paper proposes an efficient method for
determining iris centre in low-resolution images in the visible spectrum. Even
low-cost consumer-grade webcams can be used for gaze tracking without any
additional hardware. A two-stage algorithm is proposed for iris centre
localization. The proposed method uses geometrical characteristics of the eye.
In the first stage, a fast convolution based approach is used for obtaining the
coarse location of iris centre (IC). The IC location is further refined in the
second stage using boundary tracing and ellipse fitting. The algorithm has been
evaluated in public databases like BioID, Gi4E and is found to outperform the
state of the art methods.

本研究提出了一种针对低分辨率可见光图像的虹膜中心定位问题的高效方法，使用几何特征和两阶段算法确定虹膜中心位置，并结合边界跟踪和椭圆拟合进行细化，该算法可在 BioID 和 Gi4E 等公共数据库上验证超越先进方法，即使使用低成本的消费级网络摄像头也可以实现注视跟踪。