.png){kind=icon}
返回
Contour detection via stacking random forest learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2017.11.046.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Contour detection is an important and fundamental problem in computer vision which finds numerous applications. Despite significant progress has been made in the past decades, contour detection from natural images remains a challenging task due to the difficulty of clearly distinguishing between edges of objects and surrounding backgrounds. To address this problem, we first capture multi-scale features from pixel-level to segment-level using local and global information. These features are mapped to a space where discriminative information is captured by computing posterior divergence of Gaussian mixture models and sufficient statistics based on deep Boltzmann machine. Then we introduce a stacking random forest learning framework for contour detection. We evaluate the proposed algorithm against leading methods in the literature on the Berkeley segmentation and Weizmann horse data sets. Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Contour dectection
Image processing
Feature mapping
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Saliency driven vasculature segmentation with infinite perimeter active contour model.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏
查看更多内容