Modeling the Bionic Compound Eye Vision System Based on Graph Neural Networks

A bionic compound eye (CE) vision system is inspired by examples from nature, such as the eyes of dragonflies, mollusks, and other beings. It is used for visual measurements and 3-D reconstruction at close range due to the large number of overlapping miniaturized subeyes, which allow such systems to...

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Vydáno v:IEEE sensors journal Ročník 25; číslo 14; s. 26748 - 26755
Hlavní autoři: Arngold, Artem, Li, Yuan, Ren, Xuemei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 15.07.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Autonomous navigation
Bionics
Calibration
Cameras
Compound eye (CE)
Compounds
Computational modeling
Euclidean geometry
Eye (anatomy)
graph neural network (GNN)
Graph neural networks
Image reconstruction
Lenses
Machine vision
modeling
Modelling
Mollusks
Multilayer perceptrons
Neural networks
Pinhole cameras
Solid modeling
Three-dimensional displays
Transformers
Vision systems
visual measurement
Visualization
ISSN:1530-437X, 1558-1748
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Shrnutí:A bionic compound eye (CE) vision system is inspired by examples from nature, such as the eyes of dragonflies, mollusks, and other beings. It is used for visual measurements and 3-D reconstruction at close range due to the large number of overlapping miniaturized subeyes, which allow such systems to be applied in robot navigation, autonomous vehicles, medical endoscopy, and others. The calibration of the CE is difficult due to distortions and the large number of optimized parameters. This work proposes a new method for CE modeling based on graph neural networks (GNNs). This model creates a 2-D to 3-D correspondence solving the problem of missing values that appears when an object is not captured in all subeyes. The obtained results verified better performance of the proposed model in the estimation of 3-D object coordinates and in visual measurement of Euclidean distance between objects, compared to a traditional calibration approach based on pinhole camera model as well as a method based on multilayer perceptron (MLP) model, where missing values are filled with zeros. Comparative analysis is done to validate a design of the proposed GNN-based model.
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3575172