We introduce an interactive image segmentation and visualization framework
for identifying, inspecting, and editing tiny objects (just a few pixels wide)
in large multi-megapixel high-dynamic-range (HDR) images. Detecting cosmic rays
(CRs) in astronomical observations is a cumbersome workflow that requires
multiple tools, so we developed an interactive toolkit that unifies model
inference, HDR image visualization, segmentation mask inspection and editing
into a single graphical user interface. The feature set, initially designed for
astronomical data, makes this work a useful research-supporting tool for
human-in-the-loop tiny-object segmentation in scientific areas like
biomedicine, materials science, remote sensing, etc., as well as computer
vision. Our interface features mouse-controlled, synchronized, dual-window
visualization of the image and the segmentation mask, a critical feature for
locating tiny objects in multi-megapixel images. The browser-based tool can be
readily hosted on the web to provide multi-user access and GPU acceleration for
any device. The toolkit can also be used as a high-precision annotation tool,
or adapted as the frontend for an interactive machine learning framework. Our
open-source dataset, CR detection model, and visualization toolkit are
available at this https URL

介绍了一个互动的图像分割和可视化框架，用于识别、检查和编辑大型多兆像素高动态范围（HDR）图像中微小物体 (仅有几个像素宽)，特征集不仅适用于天文数据，而且可以成为科学领域 (如生物医学、材料科学、遥感等) 和计算机视觉中人机协同小物体分割的有用研究支持工具。该工具可以方便地托管在 Web 上，为任何设备提供多用户访问和 GPU 加速。

多兆像素图像中微小物体的交互式分割和可视化

Interactive Segmentation and Visualization for Tiny Objects in  Multi-megapixel Images

Network capacity calls for significant increase for 5G cellular systems. A
promising multi-user access scheme, non-orthogonal multiple access (NOMA) with
successive interference cancellation (SIC), is currently under consideration.
In NOMA, spectrum efficiency is improved by allowing more than one user to
simultaneously access the same frequency-time resource and separating
multi-user signals by SIC at the receiver. These render resource allocation and
optimization in NOMA different from orthogonal multiple access in 4G. In this
paper, we provide theoretical insights and algorithmic solutions to jointly
optimize power and channel allocation in NOMA. For utility maximization, we
mathematically formulate NOMA resource allocation problems. We characterize and
analyze the problems' tractability under a range of constraints and utility
functions. For tractable cases, we provide polynomial-time solutions for global
optimality. For intractable cases, we prove the NP-hardness and propose an
algorithmic framework combining Lagrangian duality and dynamic programming
(LDDP) to deliver near-optimal solutions. To gauge the performance of the
obtained solutions, we also provide optimality bounds on the global optimum.
Numerical results demonstrate that the proposed algorithmic solution can
significantly improve the system performance in both throughput and fairness
over orthogonal multiple access as well as over a previous NOMA resource
allocation scheme.

本文提供了在 NOMA 多用户接入系统中，对功率和信道分配进行联合优化的理论和算法解决方案。我们构建了数学模型，分析了各种约束和效用函数下的可解和不可解情况，并提出了 LDDP 算法框架来实现近似最优解。与正交多址接入和以前的 NOMA 资源分配方案相比，数值结果表明 proposed algorithmic solution 能够显著地提高系统的吞吐量和公平性。