Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees
@article{Simek2016BranchingGP, title={Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees}, author={Kyle Simek and Ravishankar Palanivelu and Kobus Barnard}, journal={ArXiv}, year={2016}, volume={abs/1608.04045} }
We propose a robust method for estimating dynamic 3D curvilinear branching structure from monocular images. While 3D reconstruction from images has been widely studied, estimating thin structure has received less attention. This problem becomes more challenging in the presence of camera error, scene motion, and a constraint that curves are attached in a branching structure. We propose a new general-purpose prior, a branching Gaussian processes (BGP), that models spatial smoothness and temporal…
5 Citations
Segmentation and 3D reconstruction of rose plants from stereoscopic images
- Computer ScienceComput. Electron. Agric.
- 2020
Computer Vision Problems in 3D Plant Phenotyping
- Computer Science
- 2017
A novel system for automated and non-invasive/non-contact plant growth measurement, exploit the recent advancements of sophisticated robotic technologies and near infrared laser scanners to build a 3D imaging system and use state-of-the-art Computer Vision algorithms to fully automate growth measurement.
3D Phenotyping of Plants
- Computer Science
- 2020
This chapter presents a broad overview of computer vision based 3D plant Phenotyping techniques, and some open challenges of vision-based plant phenotyping are discussed, followed by conclusion and some hands on exercises.
BGP: Branched Gaussian processes for identifying gene-specific branching dynamics in single cell data
- BiologybioRxiv
- 2017
The branching Gaussian process (BGP) is developed, a non-parametric model that is able to identify branching dynamics for individual genes and provides an estimate of branching times for each gene with an associated credible region.
BGP: identifying gene-specific branching dynamics from single-cell data with a branching Gaussian process
- BiologyGenome Biology
- 2018
The branching Gaussian process (BGP) is developed, a non-parametric model that is able to identify branching dynamics for individual genes and provide an estimate of branching times for each gene with an associated credible region.
References
SHOWING 1-10 OF 40 REFERENCES
Automated reconstruction of tree structures using path classifiers and Mixed Integer Programming
- Computer Science2012 IEEE Conference on Computer Vision and Pattern Recognition
- 2012
This paper formulate the delineation problem as one of solving a Quadratic Mixed Integer Program (Q-MIP) in a graph of potential paths, which can be done optimally up to a very small tolerance, and proposes a novel approach to weighting these paths which results in a Q-Mip solution that accurately matches the ground truth.
Modeling Complex Unfoliaged Trees from a Sparse Set of Images
- Computer ScienceComput. Graph. Forum
- 2010
A novel image‐based technique for modeling complex unfoliaged trees by faithfully recovering real instead of realistically‐looking tree geometry from a sparse set of images and directly integrates 2D/3D tree topology as shape priors into the modeling process.
Reconstructing Evolving Tree Structures in Time Lapse Sequences
- Computer Science2014 IEEE Conference on Computer Vision and Pattern Recognition
- 2014
This work proposes an approach to reconstructing tree structures that evolve over time in 2D images and 3D image stacks such as neuronal axons or plant branches and shows that this problem can be formulated as a Quadratic Mixed Integer Program and solved efficiently.
Thin Structure Estimation with Curvature Regularization
- Computer Science, Mathematics2015 IEEE International Conference on Computer Vision (ICCV)
- 2015
This work develops a novel algorithm that is able to perform joint optimization of location and detection variables more effectively and applies to quadratic or absolute curvature of the center-lines or surfaces.
Multiview reconstruction of space curves
- Computer ScienceProceedings Ninth IEEE International Conference on Computer Vision
- 2003
A generative model of curves is introduced which has two key components: a prior distribution of general space curves and an image formation model which describes how 3D curves are projected onto the image plane, and a fully automatic algorithm for solving the inverse problem.
Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors
- Computer ScienceNeuroinformatics
- 2011
A novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks that uses the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement.
Robust non-rigid registration of 2D and 3D graphs
- Computer Science2012 IEEE Conference on Computer Vision and Pattern Recognition
- 2012
In the absence of appearance information, this approach iteratively establish correspondences between graph nodes, update the structure accordingly, and use the current mapping estimate to find the most likely correspondences that will be used in the next iteration, which makes the computation tractable.
Modeling and generating moving trees from video
- Computer ScienceACM Trans. Graph.
- 2011
This work presents a probabilistic approach for the automatic production of tree models with convincing 3D appearance and motion, and provides a generative model that creates multiple trees in 3D, given a single example model.
Shape from Silhouette Probability Maps: Reconstruction of Thin Objects in the Presence of Silhouette Extraction and Calibration Error
- Computer Science2013 IEEE Conference on Computer Vision and Pattern Recognition
- 2013
An algorithm for an approximate solution using local minimum search for reconstructing the shape of thin, texture-less objects such as leafless trees when there is noise or deterministic error in the silhouette extraction step or there are small errors in camera calibration.
Nonrigid shape recovery by Gaussian process regression
- Computer Science2009 IEEE Conference on Computer Vision and Pattern Recognition
- 2009
A novel Gaussian process regression approach to the nonrigid shape recovery problem, which does not require to involve a predefined triangulated mesh model and is able to handle large deformations and outliers.