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Related Experiment Video

Updated: Jan 5, 2026

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

Published on: January 21, 2013

Contour analysis for interpretable leaf shape category discovery.

Jorge Victorino^1,2, Francisco Gómez³

¹1Departament of System Engineering, Universidad Central, Bogotá, 110311 Colombia.

|October 19, 2019

Summary

This study introduces an automated method to discover leaf shape categories without expert input, achieving over 92% accuracy. The approach enhances biological morphology analysis in exploratory research.

Keywords:

Category discovery Contour analysis Interpretability Leaf shape Morphological description

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LeafJ: An ImageJ Plugin for Semi-automated Leaf Shape Measurement

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Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands

Published on: August 29, 2019

Area of Science:

Botany
Ecology
Paleobotany

Background:

Leaf shape categorization is crucial for ecological, taxonomic, and paleobotanical studies.
Expert-driven classification systems can be ambiguous or limited in novel scenarios.
Automated methods are needed to overcome limitations in current leaf shape descriptions.

Purpose of the Study:

To develop a novel strategy for automatically discovering leaf shape categories from unlabeled data.
To address the challenge of categorizing leaf shapes across different plant species and biological settings.
To provide a data-driven approach for shape classification in exploratory biological domains.

Main Methods:

Utilized leaf-shape information exclusively for category discovery.
Developed a novel strategy for unsupervised shape category inference.
Applied the method to datasets from different plant species and biological contexts.

Main Results:

Successfully inferred unknown underlying leaf shape categories.
Achieved a high F-score exceeding 92% for shape category discovery.
Demonstrated the method's effectiveness across diverse biological settings.

Conclusions:

The automated approach offers high visual interpretability, vital for biological object description.
This method supports morphological analysis in exploratory biological research.
Provides a robust, data-driven alternative to expert-based leaf shape classification.