Isobe, S. and Arai, S. (2017) Deep Convolutional Encoder-Decoder Network with Model Uncertainty for Semantic Segmentation. 2017 IEEE International Conference on Innovations in Intelligent Systems and Applications, Gdynia, 3-5 July 2017, 365-370.
https://doi.org/10.1109/INISTA.2017.8001187

has been cited by the following article:

• TITLE: An Image Segmentation Algorithm Based on a Local Region Conditional Random Field Model

  AUTHORS: Xiao Jiang, Haibin Yu, Shuaishuai Lv

  KEYWORDS: Image Segmentation, Local Region Condition Random Field Model, Deep Neural Network, Consecutive Shooting Traffic Scene

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.13 No.9, September 30, 2020

  ABSTRACT: To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.