HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees

@article{Chen2022HOBCNNHO,
  title={HOB-CNN: Hallucination of Occluded Branches with a Convolutional Neural Network for 2D Fruit Trees},
  author={Zijue Chen and Keenan Granland and Rhys Newbury and Chao Chen},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.00002}
}
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References

SHOWING 1-10 OF 19 REFERENCES

Semantic Segmentation for Partially Occluded Apple Trees Based on Deep Learning

Deep learning based segmentation for automated training of apple trees on trellis wires

Fruit Detection, Segmentation and 3D Visualisation of Environments in Apple Orchards

Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN)

Estimating the trajectories of vine cordons in full foliage canopies for automated green shoot thinning in vineyards

Determining grapevine cordon shape for automated green shoot thinning using semantic segmentation-based deep learning networks

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures, including FCN and DeconvNet.

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

A new scaling method is proposed that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient and is demonstrated the effectiveness of this method on scaling up MobileNets and ResNet.

Development and performance evaluation of a machine vision system and an integrated prototype for automated green shoot thinning in vineyards

Green shoot thinning in vineyards is an essential, perennial operation for maintaining canopy health and optimizing yield and quality of wine grapes. Use of mechanized thinning system, which is…

U-Net: Convolutional Networks for Biomedical Image Segmentation

It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.