A new semantic segmentation approach of 3D mesh using the stereoscopic image colors

Taime, Abderazzak; Saaidi, Abderrahim; Satori, Khalid

doi:10.1007/s11042-018-5911-y

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…tation tasks in combination with CNNs or graph convolutions [40,8,18,35]. Voxel representation can be described as a cubical volume divided into a regular grid (cf.…”

Section: O-netmentioning

confidence: 99%

Scalable 3D Semantic Segmentation for Gun Detection in CT Scans

Memmel¹,

Reich²,

Wagner³

et al. 2021

Preprint

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With the increased availability of 3D data, the need for solutions processing those also increased rapidly. However, adding dimension to already reliably accurate 2D approaches leads to immense memory consumption and higher computational complexity. These issues cause current hardware to reach its limitations, with most methods forced to reduce the input resolution drastically. Our main contribution is a novel deep 3D semantic segmentation method for gun detection in baggage CT scans that enables fast training and low video memory consumption for high-resolution voxelized volumes. We introduce a moving pyramid approach that utilizes multiple forward passes at inference time for segmenting an instance.

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“…tation tasks in combination with CNNs or graph convolutions [40,8,18,35]. Voxel representation can be described as a cubical volume divided into a regular grid (cf.…”

Section: O-netmentioning

confidence: 99%

Scalable 3D Semantic Segmentation for Gun Detection in CT Scans

Memmel¹,

Reich²,

Wagner³

et al. 2021

Preprint

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“…In order to make full use of context information, dilated convolution is used in the convolution layer of some weakly bottleneck modules. It can not only maintain the image spatial information, but also increase the local receptive field of the network, thereby improving the accuracy of semantic segmentation [36,37].…”

Section: Asymmetric Residual Modulementioning

confidence: 99%

Image semantic segmentation method based on improved ERFNet model

Han

2021

The Journal of Engineering

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In order to solve the problems in the existing image semantic segmentation methods, such as the poor segmentation accuracy of small target object and the difficulty in segmentation of small target area, an image semantic segmentation method based on improved ERFNet model is proposed. Firstly, combining the asymmetric residual module and the weak bottleneck module, the ERFNet network model is improved to improve the running speed and reduce the loss of precision. Then, global pooling is used to fuse the feature channels after pyramid pooling to preserve more important feature information. Finally, the network model is implemented based on PyTorch deep learning framework, and the proposed method is demonstrated by experiments, in which the model retraining method is adopted to learn and train it. The experimental results show that the proposed method improves the segmentation ability of small-scale objects and reduces the possibility of misclassification. The average pixel accuracy (MPA) and average intersection merge ratio (MIOU) of the proposed method are higher than those of other contrast methods.

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