Fully convolutional networks for semantic segmentation bibtex

TY - GEN. T1 - Hybrid connection network for semantic segmentation. AU - Liang, Xiao. AU - Kamata, Sei Ichiro. PY - 2018/1/1. Y1 - 2018/1/1. N2 - In recent years, deep convolutional neural networks like ResNet and DenseNet with short cut connected to each layer can be more accurate and easier to train. In this paper, we propose a novel fully convolutional network to accomplish the two tasks simultaneously, in a semantic labeling fashion, i.e., to label every pixel of the image into 3 classes ...

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Aiming at reducing time consuming, both the detection and segmentation procedure share the convolutional features of a deep VGG-16 network . As for the share convolutional features, we utilize a VGG-16 networks with 13 convolution layers where each convolution layer is followed by a ReLU layer but only four pooling layers are placed right after the convolution layer to reduce the spatial dimension.

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A U-Net structure built with our PFCNN framework used for the human body segmentation task. Our surface convolution fully supports various CNN structures like ResNet and U-Net. The feature maps for different cover space branches are in parallel and finally reduced into one map before output. In this figure, N is the number of branches (or frame ... Semantic segmentation via highly fused convolutional network with multiple soft cost functions. Tao Yang, Yan Wu, Junqiao Zhao and Linting Guan. Cognitive Systems Research 53, 2019, pp. 20 - 30. BibTeX SPIE Digital Library Proceedings. CONFERENCE PROCEEDINGS Papers Presentations


Convolutional Neural Networks on Surfaces via Seamless Toric Covers. Haggai Maron, Meirav Galun, Noam Aigerman, Miri Trope, Nadav Dym, Ersin Yumer, Vladimir G. Kim, Yaron Lipman. ACM Transactions on Graphics (SIGGRAPH 2017). BibTeX PDF Code The One Hundred Layers Tiramisu: Fully Convolutional DenseNets (FC-DenseNet) for Semantic Segmentation In this paper[6] authors utilized ideas from the DenseNets[7] to deal with the problem of semantic segmentation. The network is composed of a downsampling path responsible for extracting coarse semantic features, We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an ...

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