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231225s2018 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2756825
|2 doi
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|a pubmed24n0920.xml
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|a DE-627
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|c DE-627
|e rakwb
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| 041 |
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|a eng
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| 100 |
1 |
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|a Wang, Xiang
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
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|c 2018
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 30.07.2018
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|a Date Revised 30.07.2018
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance
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| 650 |
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|a Journal Article
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| 650 |
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|a Feature extraction
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| 650 |
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|a Image edge detection
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| 650 |
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|a Image segmentation
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| 650 |
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|a Neural networks
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| 650 |
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|a Object detection
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| 650 |
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|a Semantics
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| 700 |
1 |
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|a Ma, Huimin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Xiaozhi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a You, Shaodi
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 27(2018), 1 vom: 27. Jan., Seite 121-134
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|x 1941-0042
|7 nnns
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| 773 |
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|g volume:27
|g year:2018
|g number:1
|g day:27
|g month:01
|g pages:121-134
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2017.2756825
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