TY - JOUR
AU - 
AB - SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla, Senior Member, IEEE, Abstract—We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network [1]. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN [2] and also with the well known DeepLab-LargeFOV [3], DeconvNet [4] architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. 
TI - SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
DO - 10.1109/tpami.2016.2644615
DA - 2017-12-01
UR - https://www.deepdyve.com/lp/unpaywall/segnet-a-deep-convolutional-encoder-decoder-architecture-for-image-sA2IYqpBCH
DP - DeepDyve
ER -