Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs

Li, Wei; Gu, Jun; Dong, Yongfeng; Dong, Yao; Han, Jungong

doi:10.1007/s11042-019-07882-w

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…FCN became the cornerstone of deep learning to solve the segmentation problem. Since then, numerous studies have been conducted to improve FCNs from different perspectives, specifically enhancing contextual links [ 27 , 28 , 29 ], adding boundary information [ 30 , 31 , 32 , 33 ], etc. These approaches were proposed to boost the performance of brain tumor segmentation.…”

Section: Related Workmentioning

confidence: 99%

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

et al. 2022

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Due to the complexity of medical imaging techniques and the high heterogeneity of glioma surfaces, image segmentation of human gliomas is one of the most challenging tasks in medical image analysis. Current methods based on convolutional neural networks concentrate on feature extraction while ignoring the correlation between local and global. In this paper, we propose a residual mix transformer fusion net, namely RMTF-Net, for brain tumor segmentation. In the feature encoder, a residual mix transformer encoder including a mix transformer and a residual convolutional neural network (RCNN) is proposed. The mix transformer gives an overlapping patch embedding mechanism to cope with the loss of patch boundary information. Moreover, a parallel fusion strategy based on RCNN is utilized to obtain local–global balanced information. In the feature decoder, a global feature integration (GFI) module is applied, which can enrich the context with the global attention feature. Extensive experiments on brain tumor segmentation from LGG, BraTS2019 and BraTS2020 demonstrated that our proposed RMTF-Net is superior to existing state-of-art methods in subjective visual performance and objective evaluation.

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Section: Related Workmentioning

confidence: 99%

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

et al. 2022

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“…Due to the limited information contained in each single pixel, high-level knowledge have been added to support object recognition, such as local geometry information [12,13] and context knowledge [14,10]. For example, an image is segmented into equal-sized cells for labeling [15].…”

Section: Related Workmentioning

confidence: 99%

Locop: Local Collaborative Object Presence For Semantic Labeling Via Score Map Re-Inference

Guo

Fan

2021

2021 IEEE International Conference on Image Processing (ICIP)

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Recent research has focused on end-to-end networks for indoor scene semantic labeling. However, in addition to learning bottom-up features, high-level knowledge could be implemented to guide the local classification. In this paper, we take advantage of trained semantic labeling networks by using the intermediate layer output as a per-category local detector and implement the context information in a network structure to boost the semantic segmentation performance. A deep learning-based re-inferencing frame work is proposed to boost any pixel-level labeling outputs using our local collaborative object presence (LoCOP) feature as the global-to-local guidance. Experimental results show that the detection accuracy is improved with our re-inference approach.

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Holistic indoor scene understanding by context-supported instance segmentation

Guo

Fan

2021

Multimed Tools Appl

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Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs

Cited by 8 publications

References 24 publications

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

RMTF-Net: Residual Mix Transformer Fusion Net for 2D Brain Tumor Segmentation

Locop: Local Collaborative Object Presence For Semantic Labeling Via Score Map Re-Inference

Holistic indoor scene understanding by context-supported instance segmentation

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