Greedy Matrix Completion with Fitting Error and Rank Iterative Minimization

Plant leaf 3D architecture changes during growth and shows sensitive response to environmental stresses. In recent years, acquisition and segmentation methods of leaf point cloud developed rapidly, but 3D modelling leaf point clouds has not gained much attention. In this study, a parametric surface modelling method was proposed for accurately fitting tea leaf point cloud. Firstly, principal component analysis was utilized to adjust posture and position of the point cloud. Then, the point cloud was sliced into multiple sections, and some sections were selected to generate a point set to be fitted (PSF). Finally, the PSF was fitted into non-uniform rational B-spline (NURBS) surface. Two methods were developed to generate the ordered PSF and the unordered PSF, respectively. The PSF was firstly fitted as B-spline surface and then was transformed to NURBS form by minimizing fitting error, which was solved by particle swarm optimization (PSO). The fitting error was specified as weighted sum of the root-mean-square error (RMSE) and the maximum value (MV) of Euclidean distances between fitted surface and a subset of the point cloud. The results showed that the proposed modelling method could be used even if the point cloud is largely simplified (RMSE < 1 mm, MV < 2 mm, without performing PSO). Future studies will model wider range of leaves as well as incomplete point cloud.

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Spectrally Sparse Signal Recovery via Hankel Matrix Completion With Prior Information

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2021.3067738 ◽

2021 ◽

Vol 69 ◽

pp. 2174-2187

Author(s):

Xu Zhang ◽

Yulong Liu ◽

Wei Cui

Keyword(s):

Prior Information ◽

Matrix Completion ◽

Hankel Matrix ◽

Sparse Signal ◽

Sparse Signal Recovery ◽

Signal Recovery

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Foveal Pit Morphology Characterization: A Quantitative Analysis of the Key Methodological Steps

Entropy ◽

10.3390/e23060699 ◽

2021 ◽

Vol 23 (6) ◽

pp. 699

Author(s):

David Romero-Bascones ◽

Maitane Barrenechea ◽

Ane Murueta-Goyena ◽

Marta Galdós ◽

Juan Carlos Gómez-Esteban ◽

...

Keyword(s):

State Of The Art ◽

Parameter Estimates ◽

Human Visual Perception ◽

The Gaze ◽

Center Location ◽

Geometrical Features ◽

Foveal Center ◽

Location Methods ◽

Morphology Characterization ◽

Fitting Error

Disentangling the cellular anatomy that gives rise to human visual perception is one of the main challenges of ophthalmology. Of particular interest is the foveal pit, a concave depression located at the center of the retina that captures light from the gaze center. In recent years, there has been a growing interest in studying the morphology of the foveal pit by extracting geometrical features from optical coherence tomography (OCT) images. Despite this, research has devoted little attention to comparing existing approaches for two key methodological steps: the location of the foveal center and the mathematical modelling of the foveal pit. Building upon a dataset of 185 healthy subjects imaged twice, in the present paper the image alignment accuracy of four different foveal center location methods is studied in the first place. Secondly, state-of-the-art foveal pit mathematical models are compared in terms of fitting error, repeatability, and bias. The results indicate the importance of using a robust foveal center location method to align images. Moreover, we show that foveal pit models can improve the agreement between different acquisition protocols. Nevertheless, they can also introduce important biases in the parameter estimates that should be considered.

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