LEARNING-BASED APPROACH TO BOOST DETECTION RATE AND LOCALISATION ACCURACY IN SINGLE MOLECULE LOCALISATION MICROSCOPY

被引：0

作者：

Colabrese, Silvia ^{[1
]}

Castello, Marco ^{[1
]}

Vicidomini, Giuseppe ^{[1
]}

Del Bue, Alessio ^{[1
]}

机构：

[1] Ist Italiano Tecnol, Via Morego 30, I-16163 Genoa, Italy

来源：

2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2016年

关键词：

SML microscopy; super-resolution; SVM; PARTICLE IMAGE VELOCIMETRY; SUPERRESOLUTION MICROSCOPY; RESOLUTION; NANOSCOPY; EMITTERS; STORM;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces a novel system for the analysis of super resolution microscopy images using a learning based approach boosting performance and simplicity of use. Key component of single-molecule-localisation (SML) microscopy techniques is the ability to localise single emitting molecules in a stack of noisy images with a high degree of accuracy. To this end, we propose a SVM-based detector coupled with a weighted Least Squares fitting approach that favourably compares with the state of the art on established evaluation protocols.

引用

页码：3184 / 3188

页数：5

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