34,015 research outputs found
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
Efficient and Effective Handling of Exceptions in Java Points-To Analysis
A joint points-to and exception analysis has been shown to yield benefits in both precision and performance. Treating exceptions as regular objects,
however, incurs significant and rather unexpected overhead. We show that in a
typical joint analysis most of the objects computed to flow in and out of a method
are due to exceptional control-flow and not normal call-return control-flow. For
instance, a context-insensitive analysis of the Antlr benchmark from the DaCapo
suite computes 4-5 times more objects going in or out of a method due to exceptional control-flow than due to normal control-flow. As a consequence, the
analysis spends a large amount of its time considering exceptions.
We show that the problem can be addressed both e
ectively and elegantly by
coarsening the representation of exception objects. An interesting find is that, instead of recording each distinct exception object, we can collapse all exceptions
of the same type, and use one representative object per type, to yield nearly identical precision (loss of less than 0.1%) but with a boost in performance of at least
50% for most analyses and benchmarks and large space savings (usually 40% or
more)
- …