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 https://github.com/cy-xu/cosmic-conn.
翻译:我们引入了一个互动的图像分割和视觉化框架,用于识别、检查和编辑大型多感应高射程(HDR)图像中的微小物体(仅几个像素宽度),在大型多象像素高射程图像中(HDR),检测天文观测中的宇宙射线(CRs)是一个繁琐的工作流程,需要多种工具,因此我们开发了一个互动工具包,将模型推断、《人类发展报告》图像可视化、分解遮蔽检查和编辑统一到一个图形用户界面中。最初为天文数据设计的功能集集,使这项工作成为在生物医学、材料科学、遥感等科学领域以及计算机视觉中支持人类在流动极小物体分割的研究支持工具。我们的界面特征包括鼠控控、同步、图像双视窗化,这是在多象素图像中定位微小物体的关键特征。基于浏览器的工具可以很容易地在网站上托管,为任何设备提供多使用者访问和GPUP加速功能。工具包还可以用作高感知/直观/图像检测工具的模型。