@article{fdi:010085328,
  title = {{A} lightweight keypoint matching framework for insect wing morphometric landmark detection},
  author = {{N}guyen, {H}. {H}. and {H}o, {B}. {H}. and {L}ai, {H}. {P}. and {T}ran, {H}. {T}. and {B}anuls, {A}nne-{L}aure and {P}rudhomme, {J}. and {L}e, {H}. {T}.},
  editor = {},
  language = {{ENG}},
  abstract = {{G}eometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. {S}hape could be digitized as a set of landmarks from specimen images. {H}owever, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. {M}otivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. {W}hile 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. {I}t 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. {T}he utility i{M}orph is freely available at https://github.com/ha-usth/{I}nsect{W}ing{L}andmark, currently supporting {W}indows, {M}ac{OS}, and {L}inux.},
  keywords = {{L}andmark ; {M}orphometric ; {K}eypoint matching ; {O}pen-source},
  booktitle = {},
  journal = {{E}cological {I}nformatics},
  volume = {70},
  numero = {},
  pages = {101694 [9 p.]},
  ISSN = {1574-9541},
  year = {2022},
  DOI = {10.1016/j.ecoinf.2022.101694},
  URL = {https://www.documentation.ird.fr/hor/fdi:010085328},
}