49,109 research outputs found
CLAD: A Complex and Long Activities Dataset with Rich Crowdsourced Annotations
This paper introduces a novel activity dataset which exhibits real-life and
diverse scenarios of complex, temporally-extended human activities and actions.
The dataset presents a set of videos of actors performing everyday activities
in a natural and unscripted manner. The dataset was recorded using a static
Kinect 2 sensor which is commonly used on many robotic platforms. The dataset
comprises of RGB-D images, point cloud data, automatically generated skeleton
tracks in addition to crowdsourced annotations. Furthermore, we also describe
the methodology used to acquire annotations through crowdsourcing. Finally some
activity recognition benchmarks are presented using current state-of-the-art
techniques. We believe that this dataset is particularly suitable as a testbed
for activity recognition research but it can also be applicable for other
common tasks in robotics/computer vision research such as object detection and
human skeleton tracking
Empirical Methodology for Crowdsourcing Ground Truth
The process of gathering ground truth data through human annotation is a
major bottleneck in the use of information extraction methods for populating
the Semantic Web. Crowdsourcing-based approaches are gaining popularity in the
attempt to solve the issues related to volume of data and lack of annotators.
Typically these practices use inter-annotator agreement as a measure of
quality. However, in many domains, such as event detection, there is ambiguity
in the data, as well as a multitude of perspectives of the information
examples. We present an empirically derived methodology for efficiently
gathering of ground truth data in a diverse set of use cases covering a variety
of domains and annotation tasks. Central to our approach is the use of
CrowdTruth metrics that capture inter-annotator disagreement. We show that
measuring disagreement is essential for acquiring a high quality ground truth.
We achieve this by comparing the quality of the data aggregated with CrowdTruth
metrics with majority vote, over a set of diverse crowdsourcing tasks: Medical
Relation Extraction, Twitter Event Identification, News Event Extraction and
Sound Interpretation. We also show that an increased number of crowd workers
leads to growth and stabilization in the quality of annotations, going against
the usual practice of employing a small number of annotators.Comment: in publication at the Semantic Web Journa
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Spectral imaging in preclinical research and clinical pathology.
Spectral imaging methods are attracting increased interest from researchers and practitioners in basic science, pre-clinical and clinical arenas. A combination of better labeling reagents and better optics creates opportunities to detect and measure multiple parameters at the molecular and cellular level. These tools can provide valuable insights into the basic mechanisms of life, and yield diagnostic and prognostic information for clinical applications. There are many multispectral technologies available, each with its own advantages and limitations. This chapter will present an overview of the rationale for spectral imaging, and discuss the hardware, software and sample labeling strategies that can optimize its usefulness in clinical settings
A digital microarray using interferometric detection of plasmonic nanorod labels
DNA and protein microarrays are a high-throughput technology that allow the
simultaneous quantification of tens of thousands of different biomolecular
species. The mediocre sensitivity and dynamic range of traditional fluorescence
microarrays compared to other techniques have been the technology's Achilles'
Heel, and prevented their adoption for many biomedical and clinical diagnostic
applications. Previous work to enhance the sensitivity of microarray readout to
the single-molecule ('digital') regime have either required signal amplifying
chemistry or sacrificed throughput, nixing the platform's primary advantages.
Here, we report the development of a digital microarray which extends both the
sensitivity and dynamic range of microarrays by about three orders of
magnitude. This technique uses functionalized gold nanorods as single-molecule
labels and an interferometric scanner which can rapidly enumerate individual
nanorods by imaging them with a 10x objective lens. This approach does not
require any chemical enhancement such as silver deposition, and scans arrays
with a throughput similar to commercial fluorescence devices. By combining
single-nanoparticle enumeration and ensemble measurements of spots when the
particles are very dense, this system achieves a dynamic range of about one
million directly from a single scan
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