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Volume: 31 | Article ID: art00011
Edge/Region Fusion Network for Scene Labeling in Infrared Imagery
  DOI :  10.2352/ISSN.2470-1173.2019.8.IMAWM-414  Published OnlineJanuary 2019

Semantic segmentation has been a complex problem in the field of computer vision and is essential for image analysis tasks. Currently, most state-of-the-art algorithms rely on deep convolutional neural networks (DCNN) to perform this task. DCNNs are able to downsample the spatial resolution of the input image into low resolution feature mappings which are then up-sampled to produce the segmented images. However, the reduction of this spatial information causes the high frequency details of the image to be lessened resulting in blurry and inaccurate object boundaries. In order to improve this limitation, we propose combining a DCNN used for semantic segmentation with semantic boundary information. This is done using a multi-task approach by incorporating a boundary detection network into the encoder decoder architecture SegNet. This multi-task approach includes the addition of an edge class to the SegNet architecture. In doing so, the multi-task learning network is provided more information, thus improving segmentation accuracy, specifically boundary delineation. This approach was tested on the RGB-NIR Scene dataset. Compared to using SegNet alone, we observe increased boundary segmentation accuracies using this approach. We are able to show that the addition of a boundary detection information significantly improves the semantic segmentation results of a DCNN.

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Bradley Sorg, Theus Aspiras, Vijayan Asari, "Edge/Region Fusion Network for Scene Labeling in Infrared Imageryin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2019,  pp 414-1 - 414-7,

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