Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation | Paper Citation Record

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This paper cites The validation of the mixedwood growth model (mgm) for use in forest management decision making,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation The validation of the mixedwood growth model (mgm) for use in forest management decision making,

Reference 1

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Observation 81a9838e-76d9-427a-bb0c-8cf1e0a751be · outbound

This paper cites Develop- ment of crown ratio and height to crown base models for masson pine in southern china,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Develop- ment of crown ratio and height to crown base models for masson pine in southern china,

Reference 2

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This paper cites Tree crown delineation al- gorithm based on a convolutional neural network,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Tree crown delineation al- gorithm based on a convolutional neural network,

Reference 3

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This paper cites Uav re- mote sensing monitoring of pine forest diseases based on im- proved mask r-cnn,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Uav re- mote sensing monitoring of pine forest diseases based on im- proved mask r-cnn,

Reference 4

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This paper cites Tree extraction from multi-scale uav images us- ing mask r-cnn with fpn,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Tree extraction from multi-scale uav images us- ing mask r-cnn with fpn,

Reference 5

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This paper cites An improved res-unet model for tree species classification using airborne high-resolution images,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation An improved res-unet model for tree species classification using airborne high-resolution images,

Reference 6

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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation U-net: Con- volutional networks for biomedical image segmentation,

Reference 7

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This paper cites Extraction of olive crown based on uav visible images and the u2-net deep learning model,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Extraction of olive crown based on uav visible images and the u2-net deep learning model,

Reference 8

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This paper cites Segmenting purple rapeseed leaves in the field from uav rgb imagery using deep learning as an auxiliary means for nitrogen stress detection,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Segmenting purple rapeseed leaves in the field from uav rgb imagery using deep learning as an auxiliary means for nitrogen stress detection,

Reference 9

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This paper cites Using the u-net convolutional network to map forest types and disturbance in the atlantic rainforest with very high resolution images,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Using the u-net convolutional network to map forest types and disturbance in the atlantic rainforest with very high resolution images,

Reference 10

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This paper cites Mapping forest tree species in high resolution uav-based rgb-imagery by means of convolu- tional neural networks,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Mapping forest tree species in high resolution uav-based rgb-imagery by means of convolu- tional neural networks,

Reference 11

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This paper cites Self-supervised vision transformers for land-cover segmen- tation and classification,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Self-supervised vision transformers for land-cover segmen- tation and classification,

Reference 12

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This paper cites A novel multitask transformer deep learning archi- tecture for joint classification and segmentation of horti- culture plantations using very high-resolution satellite im- agery,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation A novel multitask transformer deep learning archi- tecture for joint classification and segmentation of horti- culture plantations using very high-resolution satellite im- agery,

Reference 13

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This paper cites An efficient deep learning mechanism for the recognition of olive trees in jouf region,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation An efficient deep learning mechanism for the recognition of olive trees in jouf region,

Reference 14

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This paper cites Spatial pyramid pool- ing in deep convolutional networks for visual recognition,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Spatial pyramid pool- ing in deep convolutional networks for visual recognition,

Reference 15

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This paper cites Pyramid scene parsing network,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Pyramid scene parsing network,

Reference 16

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This paper cites Neocognitron: A self- organizing neural network model for a mechanism of visual pattern recognition,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Neocognitron: A self- organizing neural network model for a mechanism of visual pattern recognition,

Reference 17

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This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 18

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This paper cites Seasonal domain shift in the global south: Dataset and deep features analysis,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Seasonal domain shift in the global south: Dataset and deep features analysis,

Reference 19

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This paper cites Biological con- volutions improve dnn robustness to noise and generalisa- tion,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Biological con- volutions improve dnn robustness to noise and generalisa- tion,

Reference 20

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This paper cites Theory of communication. part 1: The analy- sis of information,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Theory of communication. part 1: The analy- sis of information,

Reference 21

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This paper cites Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two- dimensional visual cortical filters,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two- dimensional visual cortical filters,

Reference 22

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This paper cites Texture image retrieval based on log-gabor features,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Texture image retrieval based on log-gabor features,

Reference 23

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This paper cites On the choice of band-pass quadrature filters,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation On the choice of band-pass quadrature filters,

Reference 24

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This paper cites Simulation of neural contour mechanisms: from simple to end-stopped cells,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Simulation of neural contour mechanisms: from simple to end-stopped cells,

Reference 25

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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation On idempotence and related requirements in edge detection,

Reference 26

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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Unresolved cited work

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This paper cites Self-invertible 2d log-gabor wavelets,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Self-invertible 2d log-gabor wavelets,

Reference 28

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This paper cites Gabor con- volutional networks,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Gabor con- volutional networks,

Reference 29

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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Gabornet: Gabor filters with learnable parameters in deep convolutional neural network,

Reference 30

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This paper cites Road extraction by deep residual u-net,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Road extraction by deep residual u-net,

Reference 31

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This paper cites Deep residual learning for image recognition,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Deep residual learning for image recognition,

Reference 32

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This paper cites Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

Reference 33

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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully con- nected crfs,

Reference 34

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Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Re- visiting dilated convolution: A simple approach for weakly- and semi-supervised semantic segmentation,

Reference 35

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This paper cites An im- age is worth 16x16 words: Transformers for image recogni- tion at scale,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation An im- age is worth 16x16 words: Transformers for image recogni- tion at scale,

Reference 36

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This paper cites Influence of temperate forest autumn leaf phenology on segmentation of tree species from uav imagery using deep learning,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Influence of temperate forest autumn leaf phenology on segmentation of tree species from uav imagery using deep learning,

Reference 37

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This paper cites Landcover.ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Landcover.ai: Dataset for automatic mapping of buildings, woodlands, water and roads from aerial imagery,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:09.585804Z

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This paper cites Uavid: A semantic segmentation dataset for uav im- agery,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Uavid: A semantic segmentation dataset for uav im- agery,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:09.350410Z

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This paper cites Learning deep features for discriminative localization,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Learning deep features for discriminative localization,

Reference 40

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This paper cites Multiattention network for semantic seg- mentation of fine-resolution remote sensing images,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Multiattention network for semantic seg- mentation of fine-resolution remote sensing images,

Reference 41

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This paper cites Encoder-decoder with atrous separable convolution for se- mantic image segmentation,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Encoder-decoder with atrous separable convolution for se- mantic image segmentation,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:08.865053Z

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This paper cites Unetr: Transformers for 3d medical image segmentation,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Unetr: Transformers for 3d medical image segmentation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:08.705659Z

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This paper cites Swin-unet: Unet-like pure transformer for med- ical image segmentation,.

Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation Swin-unet: Unet-like pure transformer for med- ical image segmentation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:08.463180Z

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