Eye Detection for Electronic Map Control Application

Author(s):  
Liangjun Zhang ◽  
Kaiyue Lu ◽  
Chengyi Pan ◽  
Siyu Xia
1998 ◽  
Author(s):  
Heather Pringle ◽  
Christopher D. Wickens ◽  
Patricia M. Ververs
Keyword(s):  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Antje Wulff ◽  
◽  
Claas Baier ◽  
Sarah Ballout ◽  
Erik Tute ◽  
...  

AbstractThe spread of multidrug resistant organisms (MDRO) is a global healthcare challenge. Nosocomial outbreaks caused by MDRO are an important contributor to this threat. Computer-based applications facilitating outbreak detection can be essential to address this issue. To allow application reusability across institutions, the various heterogeneous microbiology data representations needs to be transformed into standardised, unambiguous data models. In this work, we present a multi-centric standardisation approach by using openEHR as modelling standard. Data models have been consented in a multicentre and international approach. Participating sites integrated microbiology reports from primary source systems into an openEHR-based data platform. For evaluation, we implemented a prototypical application, compared the transformed data with original reports and conducted automated data quality checks. We were able to develop standardised and interoperable microbiology data models. The publicly available data models can be used across institutions to transform real-life microbiology reports into standardised representations. The implementation of a proof-of-principle and quality control application demonstrated that the new formats as well as the integration processes are feasible. Holistic transformation of microbiological data into standardised openEHR based formats is feasible in a real-life multicentre setting and lays the foundation for developing cross-institutional, automated outbreak detection systems.


2021 ◽  
Author(s):  
Hyungwook Kim ◽  
Young Jae Jung ◽  
Jungkyu K. Lee

We developed a novel strategy for signal amplification strategy using a visible light-induced photopolymerization, initiated by a selective turn-on photoredox catalyst. As photoredox catalysts, fluorescein derivatives are able to initiate...


2021 ◽  
Vol 328 ◽  
pp. 112776
Author(s):  
Yogendra Singh Solanki ◽  
Priya Yadav ◽  
Madhu Agarwal ◽  
Ragini Gupta ◽  
Sanjeev Gupta ◽  
...  

Author(s):  
JEFFREY HUANG ◽  
HARRY WECHSLER

The eyes are important facial landmarks, both for image normalization due to their relatively constant interocular distance, and for post processing due to the anchoring on model-based schemes. This paper introduces a novel approach for the eye detection task using optimal wavelet packets for eye representation and Radial Basis Functions (RBFs) for subsequent classification ("labeling") of facial areas as eye versus non-eye regions. Entropy minimization is the driving force behind the derivation of optimal wavelet packets. It decreases the degree of data dispersion and it thus facilitates clustering ("prototyping") and capturing the most significant characteristics of the underlying (eye regions) data. Entropy minimization is thus functionally compatible with the first operational stage of the RBF classifier, that of clustering, and this explains the improved RBF performance on eye detection. Our experiments on the eye detection task prove the merit of this approach as they show that eye images compressed using optimal wavelet packets lead to improved and robust performance of the RBF classifier compared to the case where original raw images are used by the RBF classifier.


Sign in / Sign up

Export Citation Format

Share Document