A Lightweight Keypoint Matching Framework for Morphometric Landmark Detection
Research Square (Research Square)(2021)
摘要
Geometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. Shape could be digitized as a set of landmarks from specimen images. However, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. Motivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. While still archiving comparable accuracy to that of the state-of-the-art method, our framework requires less initial annotated data to build prediction model and runs faster. It is lightweight also in terms of implementation, in which a four-step workflow is provided with user-friendly graphical interfaces to produce correct landmark coordinates both by model prediction and manual correction. The utility iMorph is freely available at https://github.com/ha-usth/WingLanmarkPredictor, currently supporting Windows, MacOS, and Linux.
更多查看译文
关键词
morphometric landmark detection,lightweight keypoint matching framework
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要