Scene parsing is the important part of computer vision research. And the deep coding-decoding network is widely applied to scene parsing. However, there are still some problems, such as ambiguity of object edge segmen...
详细信息
ISBN:
(纸本)9781728113128
Scene parsing is the important part of computer vision research. And the deep coding-decoding network is widely applied to scene parsing. However, there are still some problems, such as ambiguity of object edge segmentation and uncertainty when segmenting small-size-objects in scene analysis. In this paper, we propose Superpixel Pyramid network for Scene Parsing. First, a deep coding-decoding network is used to learn image features. Then, multi-scale spatial pyramid pooling structure is employed to enhance the performance of small-size-objects. Next, the Superpixel Segmentation is also applied to cope with the problem of ambiguity of object edge. Finally, a two-layer neural network classifier is applied to identify the fused features pixel-by-pixel. Extensive experimental results over ADE20K, PASCAL VOC 2012, and Camvid, demonstrated that the proposed method can obtain better performance counterparts than other.
Scene parsing is the important part of computer vision research. And the deep coding-decoding network is widely applied to scene parsing. However, there are still some problems, such as ambiguity of object edge segmen...
详细信息
Scene parsing is the important part of computer vision research. And the deep coding-decoding network is widely applied to scene parsing. However, there are still some problems, such as ambiguity of object edge segmentation and uncertainty when segmenting small-size-objects in scene analysis. In this paper, we propose Superpixel Pyramid network for Scene Parsing. First, a deep coding-decoding network is used to learn image features. Then, multi-scale spatial pyramid pooling structure is employed to enhance the performance of small-size-objects. Next, the Superpixel Segmentation is also applied to cope with the problem of ambiguity of object edge. Finally, a two-layer neural network classifier is applied to identify the fused features pixel-by-pixel. Extensive experimental results over ADE20K, PASCAL VOC 2012, and Camvid, demonstrated that the proposed method can obtain better performance counterparts than other.
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