List of Articles Semantic Segmentation

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  1 - Improvement in Accuracy and Speed of Image Semantic Segmentation via Convolution Neural Network Encoder-Decoder
  Hanieh Zamanian Hassan Farsi Sajad Mohammadzadeh
  10.7508/jist.2018.03.002
  Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predic More

  Recent researches on pixel-wise semantic segmentation use deep neural networks to improve accuracy and speed of these networks in order to increase the efficiency in practical applications such as automatic driving. These approaches have used deep architecture to predict pixel tags, but the obtained results seem to be undesirable. The reason for these unacceptable results is mainly due to the existence of max pooling operators, which reduces the resolution of the feature maps. In this paper, we present a convolutional neural network composed of encoder-decoder segments based on successful SegNet network. The encoder section has a depth of 2, which in the first part has 5 convolutional layers, in which each layer has 64 filters with dimensions of 3×3. In the decoding section, the dimensions of the decoding filters are adjusted according to the convolutions used at each step of the encoding. So, at each step, 64 filters with the size of 3×3 are used for coding where the weights of these filters are adjusted by network training and adapted to the educational data. Due to having the low depth of 2, and the low number of parameters in proposed network, the speed and the accuracy improve compared to the popular networks such as SegNet and DeepLab. For the CamVid dataset, after a total of 60,000 iterations, we obtain the 91% for global accuracy, which indicates improvements in the efficiency of proposed method. Manuscript profile
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  2 - Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN
  Nafiseh Sadeghi Homayoun Mahdavi-Nasab Mansoor Zeinali Hossein Pourghasem
  10.61186/jist.39680.11.44.359
  Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a giv More

  Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class. Manuscript profile