Numerical Acquisition of Multiple Immunofluorescence Labelling
نویسندگان
چکیده
منابع مشابه
Multi-level Semantic Labelling of Numerical Values
With the success of Open Data a huge amount of tabular data sources became available that could potentially be mapped and linked into the Web of (Linked) Data. Most existing approaches to “semantically label” such tabular data rely on mappings of textual information to classes, properties, or instances in RDF knowledge bases in order to link – and eventually transform – tabular data into RDF. H...
متن کاملSemi-automatic acquisition and labelling of image data using SOMs
Application of neural networks for real world object recognition suffers from the need to acquire large quantities of labeled image data. We propose a solution that acquires images from a domain at random and structures the data in two steps: Data driven mechanisms extract windows of interest, which are clustered by a SOM. Regions of the SOM in which objects form clusters serve as “suggestions”...
متن کاملTracking and Labelling of Interacting Multiple Targets
Successful multi-target tracking requires solving two problems localize the targets and label their identity. An isolated target’s identity can be unambiguously preserved from one frame to the next. However, for long sequences of many moving targets, like a football game, grouping scenarios will occur in which identity labellings cannot be maintained reliably by using continuity of motion or ap...
متن کاملImmuno-gold Labelling of Chlamydia trachomatis
Background Chlamydia trachomatis is considered as an important cause of preventable sexually transmitted diseases worldwide. It is known to be of an obligate intracellular nature and enters its target cells via an endocytic process. As major outer membrane protein (MOMP) is one of the main candidates for the attachment and entry of chlamydia to the host cells we have tried to label the epitopes...
متن کاملMultiple Constraint Acquisition
QUACQ is a constraint acquisition system that assists a non-expert user to model her problem as a constraint network by classifying (partial) examples as positive or negative. For each negative example, QUACQ focuses onto a constraint of the target network. The drawback is that the user may need to answer a great number of such examples to learn all the constraints. In this paper, we provide a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Microscopy Microanalysis Microstructures
سال: 1996
ISSN: 1154-2799
DOI: 10.1051/mmm:1996128