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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...

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Related Experiment Video

Updated: May 14, 2026

Robotized Testing of Camera Positions to Determine Ideal Configuration for Stereo 3D Visualization of Open-Heart Surgery
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Edge_MVSFormer: Edge-Aware Multi-View Stereo Plant Reconstruction Based on Transformer Networks.

Yang Cheng1, Zhen Liu1, Gongpu Lan1

  • 1Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528000, China.

Sensors (Basel, Switzerland)
|April 12, 2025
PubMed
Summary
This summary is machine-generated.

Edge_MVSFormer improves 3D plant reconstruction by enhancing edge details. This method significantly reduces errors in depth images and point clouds, crucial for botanical research.

Keywords:
depth imageedge detectionedge regionsedge-aware loss functionmulti-view stereoplant 3D reconstructionpoint cloudreconstruction errortransformer

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Botanical Research

Background:

  • Multi-view stereo (MVS) using RGB cameras is a cost-effective botanical research tool.
  • Existing MVS methods struggle with plant texture and fine edge reconstruction, limiting accuracy.

Purpose of the Study:

  • To enhance the accuracy of plant leaf edge reconstruction in MVS.
  • To develop a novel MVS model focusing on edge information for botanical applications.

Main Methods:

  • Proposed Edge_MVSFormer, building upon TransMVSNet.
  • Integrated an edge detection algorithm to provide edge information as input.
  • Introduced an edge-aware loss function to prioritize edge region accuracy.

Main Results:

  • Reduced edge error by 2.20 ± 0.36 mm and overall error by 0.46 ± 0.07 mm in depth images.
  • Reduced edge error by 0.13 ± 0.02 mm and overall error by 0.05 ± 0.02 mm in point clouds.
  • Demonstrated significant improvements on 10 test model plants.

Conclusions:

  • Edge information is critical for precise plant MVS data reconstruction.
  • Edge_MVSFormer offers a substantial advancement in MVS for botanical studies.
  • The method effectively addresses limitations in reconstructing intricate plant structures.