.png){kind=icon}
返回
Small object detection in cluttered image using a correlation based active contour model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2011.11.012.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a novel energy function for active contour models based on autocorrelation function, which is capable of detecting small objects against a cluttered background. In the proposed method, image features are calculated using a combination of short-term autocorrelations (STA) computed from the image pixels to represent region information. The obtained features are exploited to define an energy function for the localized region-based active contour model called normalized accumulated short-term autocorrelation (NASTA). Minimizing this energy function, we can accurately detect small objects in images containing cluttered and textured backgrounds. Moreover, the proposed method provides high robustness against random noise and can precisely locate small objects in noisy backgrounds, difficult to be detected with naked eye. Experimental results indicate remarkable advantages of our approach comparing to existing methods. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Small object detection
Active contour model
Cluttered image
Correlation
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:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
.png){kind=icon}
Active contours with selective local or global segmentation: A new formulation and level set method
PREAI
分享收藏
Distance Regularized Level Set Evolution and Its Application to Image Segmentation距离正则化水平集演化及其在图像分割中的应用
PREAI
分享收藏
查看更多内容