109,665 research outputs found
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
Identifying complex neural circuitry from electron microscopic (EM) images
may help unlock the mysteries of the brain. However, identifying this circuitry
requires time-consuming, manual tracing (proofreading) due to the size and
intricacy of these image datasets, thus limiting state-of-the-art analysis to
very small brain regions. Potential avenues to improve scalability include
automatic image segmentation and crowd sourcing, but current efforts have had
limited success. In this paper, we propose a new strategy, focused
proofreading, that works with automatic segmentation and aims to limit
proofreading to the regions of a dataset that are most impactful to the
resulting circuit. We then introduce a novel workflow, which exploits
biological information such as synapses, and apply it to a large dataset in the
fly optic lobe. With our techniques, we achieve significant tracing speedups of
3-5x without sacrificing the quality of the resulting circuit. Furthermore, our
methodology makes the task of proofreading much more accessible and hence
potentially enhances the effectiveness of crowd sourcing
Tracing technological development trajectories: A genetic knowledge persistence-based main path approach
The aim of this paper is to propose a new method to identify main paths in a
technological domain using patent citations. Previous approaches for using main
path analysis have greatly improved our understanding of actual technological
trajectories but nonetheless have some limitations. They have high potential to
miss some dominant patents from the identified main paths; nonetheless, the
high network complexity of their main paths makes qualitative tracing of
trajectories problematic. The proposed method searches backward and forward
paths from the high-persistence patents which are identified based on a
standard genetic knowledge persistence algorithm. We tested the new method by
applying it to the desalination and the solar photovoltaic domains and compared
the results to output from the same domains using a prior method. The empirical
results show that the proposed method overcomes the aforementioned drawbacks
defining main paths that are almost 10x less complex while containing more of
the relevant important knowledge than the main path networks defined by the
existing method.Comment: 20 pages, 7 figure
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