87,859 research outputs found
LabelFusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes
Deep neural network (DNN) architectures have been shown to outperform
traditional pipelines for object segmentation and pose estimation using RGBD
data, but the performance of these DNN pipelines is directly tied to how
representative the training data is of the true data. Hence a key requirement
for employing these methods in practice is to have a large set of labeled data
for your specific robotic manipulation task, a requirement that is not
generally satisfied by existing datasets. In this paper we develop a pipeline
to rapidly generate high quality RGBD data with pixelwise labels and object
poses. We use an RGBD camera to collect video of a scene from multiple
viewpoints and leverage existing reconstruction techniques to produce a 3D
dense reconstruction. We label the 3D reconstruction using a human assisted
ICP-fitting of object meshes. By reprojecting the results of labeling the 3D
scene we can produce labels for each RGBD image of the scene. This pipeline
enabled us to collect over 1,000,000 labeled object instances in just a few
days. We use this dataset to answer questions related to how much training data
is required, and of what quality the data must be, to achieve high performance
from a DNN architecture
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
Maintenance of Automated Test Suites in Industry: An Empirical study on Visual GUI Testing
Context: Verification and validation (V&V) activities make up 20 to 50
percent of the total development costs of a software system in practice. Test
automation is proposed to lower these V&V costs but available research only
provides limited empirical data from industrial practice about the maintenance
costs of automated tests and what factors affect these costs. In particular,
these costs and factors are unknown for automated GUI-based testing.
Objective: This paper addresses this lack of knowledge through analysis of
the costs and factors associated with the maintenance of automated GUI-based
tests in industrial practice.
Method: An empirical study at two companies, Siemens and Saab, is reported
where interviews about, and empirical work with, Visual GUI Testing is
performed to acquire data about the technique's maintenance costs and
feasibility.
Results: 13 factors are observed that affect maintenance, e.g. tester
knowledge/experience and test case complexity. Further, statistical analysis
shows that developing new test scripts is costlier than maintenance but also
that frequent maintenance is less costly than infrequent, big bang maintenance.
In addition a cost model, based on previous work, is presented that estimates
the time to positive return on investment (ROI) of test automation compared to
manual testing.
Conclusions: It is concluded that test automation can lower overall software
development costs of a project whilst also having positive effects on software
quality. However, maintenance costs can still be considerable and the less time
a company currently spends on manual testing, the more time is required before
positive, economic, ROI is reached after automation
Cross-Modal Data Programming Enables Rapid Medical Machine Learning
Labeling training datasets has become a key barrier to building medical
machine learning models. One strategy is to generate training labels
programmatically, for example by applying natural language processing pipelines
to text reports associated with imaging studies. We propose cross-modal data
programming, which generalizes this intuitive strategy in a
theoretically-grounded way that enables simpler, clinician-driven input,
reduces required labeling time, and improves with additional unlabeled data. In
this approach, clinicians generate training labels for models defined over a
target modality (e.g. images or time series) by writing rules over an auxiliary
modality (e.g. text reports). The resulting technical challenge consists of
estimating the accuracies and correlations of these rules; we extend a recent
unsupervised generative modeling technique to handle this cross-modal setting
in a provably consistent way. Across four applications in radiography, computed
tomography, and electroencephalography, and using only several hours of
clinician time, our approach matches or exceeds the efficacy of
physician-months of hand-labeling with statistical significance, demonstrating
a fundamentally faster and more flexible way of building machine learning
models in medicine
Empirical mode decomposition-based facial pose estimation inside video sequences
We describe a new pose-estimation algorithm via integration of the strength in both empirical mode decomposition (EMD) and mutual information. While mutual information is exploited to measure the similarity between facial images to estimate poses, EMD is exploited to decompose input facial images into a number of intrinsic mode function (IMF) components, which redistribute the effect of noise, expression changes, and illumination variations as such that, when the input facial image is described by the selected IMF components, all the negative effects can be minimized. Extensive experiments were carried out in comparisons to existing representative techniques, and the results show that the proposed algorithm achieves better pose-estimation performances with robustness to noise corruption, illumination variation, and facial expressions
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