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

TL;DR

This paper presents an interactive, browser-based toolkit for detecting, visualizing, and editing tiny objects in large multi-megapixel HDR images, supporting scientific research and annotation tasks.

Contribution

The work introduces a unified, web-based interactive framework tailored for tiny-object segmentation in large images, adaptable for scientific and computer vision applications.

Findings

Supports multi-user, GPU-accelerated visualization

Enables precise inspection and editing of tiny objects

Provides open-source dataset and tools for community use

Abstract

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.