.png){kind=icon}
返回
Contextual performance prediction for low-level image analysis algorithms
.png){kind=icon}
PRE
AI
DOI：10.1109/83.931098.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 explores a generic approach to predict the output accuracy of an algorithm without running it, by a careful examination of the local context. Such a performance prediction will allow to qualify the appropriateness of an algorithm to treat Images with given properties (contrast, resolution, noise, richness in details, contours or textures, etc.) resulting either from experimental acquisition conditions or from a specific type of scene. We have to answer the Following question: a context c being given at any site, what will be the performance? In our experiments, c is described by three contextual variables: Gabor components, entropy and signal/noise ratio, As initially proposed in the related work [8], the prediction function is determined from training using a logistic regression model. This technique Is illustrated on aerial infrared images for two types of algorithm: edge detection and displacement estimation.
Keyword:
arial infrared image
contextual measurement
logistic regression model
performance prediction
reliability
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:
13.7
论文数:
1.0W
被引数:
8.4W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
分享收藏
查看更多内容