A Deformable Shape Model for Automatic and Real-Time Dendrometry
We present a stereo image-based algorithm for tree stem diameter measurement and form analysis. The algorithm uses planar parametric curves to represent two-dimensional projections of tree stems in stereo images. The curves evolve according to an energy formulation based on the gradients of the imag...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-11-01
|
| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/14/12/2299 |
| _version_ | 1797381057335525376 |
|---|---|
| author | Lucas A. Wells Woodam Chung |
| author_facet | Lucas A. Wells Woodam Chung |
| author_sort | Lucas A. Wells |
| collection | DOAJ |
| description | We present a stereo image-based algorithm for tree stem diameter measurement and form analysis. The algorithm uses planar parametric curves to represent two-dimensional projections of tree stems in stereo images. The curves evolve according to an energy formulation based on the gradients of the images and inductive priors related to biomechanics and morphology of tree stems. After energy minimization, the curves are reconstructed to three dimensions, allowing for diameter measurements at any point along the height of the stem. We describe the algorithm and report the validation test results comparing predicted diameter measurements to external observations. Our findings demonstrate that the algorithm can automatically estimate diameters for trees within 20 m of the camera with an error of 5.52%. We highlight how this method can aid product value optimization through taper analysis and sweep or crook detection. A run-time analysis shows that the algorithm can estimate dendrometric variables for ten trees simultaneously at 15 frames per second on a consumer-grade computer. Furthermore, we discuss the opportunity to produce training data for machine learning algorithms that generalize across domains and eliminate the need to manually tune parameters. |
| first_indexed | 2024-03-08T20:45:48Z |
| format | Article |
| id | doaj.art-807cadf81a9f49498c0dc5527f267d79 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-08T20:45:48Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-807cadf81a9f49498c0dc5527f267d792023-12-22T14:09:18ZengMDPI AGForests1999-49072023-11-011412229910.3390/f14122299A Deformable Shape Model for Automatic and Real-Time DendrometryLucas A. Wells0Woodam Chung1Department of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, Corvallis, OR 97331, USADepartment of Forest Engineering, Resources and Management, College of Forestry, Oregon State University, Corvallis, OR 97331, USAWe present a stereo image-based algorithm for tree stem diameter measurement and form analysis. The algorithm uses planar parametric curves to represent two-dimensional projections of tree stems in stereo images. The curves evolve according to an energy formulation based on the gradients of the images and inductive priors related to biomechanics and morphology of tree stems. After energy minimization, the curves are reconstructed to three dimensions, allowing for diameter measurements at any point along the height of the stem. We describe the algorithm and report the validation test results comparing predicted diameter measurements to external observations. Our findings demonstrate that the algorithm can automatically estimate diameters for trees within 20 m of the camera with an error of 5.52%. We highlight how this method can aid product value optimization through taper analysis and sweep or crook detection. A run-time analysis shows that the algorithm can estimate dendrometric variables for ten trees simultaneously at 15 frames per second on a consumer-grade computer. Furthermore, we discuss the opportunity to produce training data for machine learning algorithms that generalize across domains and eliminate the need to manually tune parameters.https://www.mdpi.com/1999-4907/14/12/2299diameter measurementstem form analysisstereo cameracomputer visionactive contour model |
| spellingShingle | Lucas A. Wells Woodam Chung A Deformable Shape Model for Automatic and Real-Time Dendrometry Forests diameter measurement stem form analysis stereo camera computer vision active contour model |
| title | A Deformable Shape Model for Automatic and Real-Time Dendrometry |
| title_full | A Deformable Shape Model for Automatic and Real-Time Dendrometry |
| title_fullStr | A Deformable Shape Model for Automatic and Real-Time Dendrometry |
| title_full_unstemmed | A Deformable Shape Model for Automatic and Real-Time Dendrometry |
| title_short | A Deformable Shape Model for Automatic and Real-Time Dendrometry |
| title_sort | deformable shape model for automatic and real time dendrometry |
| topic | diameter measurement stem form analysis stereo camera computer vision active contour model |
| url | https://www.mdpi.com/1999-4907/14/12/2299 |
| work_keys_str_mv | AT lucasawells adeformableshapemodelforautomaticandrealtimedendrometry AT woodamchung adeformableshapemodelforautomaticandrealtimedendrometry AT lucasawells deformableshapemodelforautomaticandrealtimedendrometry AT woodamchung deformableshapemodelforautomaticandrealtimedendrometry |