100,061 research outputs found
Enabling Pedestrian Safety using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash
Human lives are important. The decision to allow self-driving vehicles
operate on our roads carries great weight. This has been a hot topic of debate
between policy-makers, technologists and public safety institutions. The recent
Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has
strengthened the argument that autonomous vehicle technology is still not ready
for deployment on public roads. In this work, we analyze the Uber car crash and
shed light on the question, "Could the Uber Car Crash have been avoided?". We
apply state-of-the-art Computer Vision models to this highly practical
scenario. More generally, our experimental results are an evaluation of various
image enhancement and object recognition techniques for enabling pedestrian
safety in low-lighting conditions using the Uber crash as a case study.Comment: 10 pages, 8 figures, 3 table
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the length of the text. We propose a sequence-to-sequence, purely
convolutional and deconvolutional autoencoding framework that is free of the
above issue, while also being computationally efficient. The proposed method is
simple, easy to implement and can be leveraged as a building block for many
applications. We show empirically that compared to RNNs, our framework is
better at reconstructing and correcting long paragraphs. Quantitative
evaluation on semi-supervised text classification and summarization tasks
demonstrate the potential for better utilization of long unlabeled text data.Comment: Accepted by NIPS 201
Learning Digital Camera Pipeline for Extreme Low-Light Imaging
In low-light conditions, a conventional camera imaging pipeline produces
sub-optimal images that are usually dark and noisy due to a low photon count
and low signal-to-noise ratio (SNR). We present a data-driven approach that
learns the desired properties of well-exposed images and reflects them in
images that are captured in extremely low ambient light environments, thereby
significantly improving the visual quality of these low-light images. We
propose a new loss function that exploits the characteristics of both
pixel-wise and perceptual metrics, enabling our deep neural network to learn
the camera processing pipeline to transform the short-exposure, low-light RAW
sensor data to well-exposed sRGB images. The results show that our method
outperforms the state-of-the-art according to psychophysical tests as well as
pixel-wise standard metrics and recent learning-based perceptual image quality
measures
Extreme Low-Light Imaging with Multi-granulation Cooperative Networks
Low-light imaging is challenging since images may appear to be dark and
noised due to low signal-to-noise ratio, complex image content, and the variety
in shooting scenes in extreme low-light condition. Many methods have been
proposed to enhance the imaging quality under extreme low-light conditions, but
it remains difficult to obtain satisfactory results, especially when they
attempt to retain high dynamic range (HDR). In this paper, we propose a novel
method of multi-granulation cooperative networks (MCN) with bidirectional
information flow to enhance extreme low-light images, and design an
illumination map estimation function (IMEF) to preserve high dynamic range
(HDR). To facilitate this research, we also contribute to create a new
benchmark dataset of real-world Dark High Dynamic Range (DHDR) images to
evaluate the performance of high dynamic preservation in low light environment.
Experimental results show that the proposed method outperforms the
state-of-the-art approaches in terms of both visual effects and quantitative
analysis
Image declipping with deep networks
We present a deep network to recover pixel values lost to clipping. The
clipped area of the image is typically a uniform area of minimum or maximum
brightness, losing image detail and color fidelity. The degree to which the
clipping is visually noticeable depends on the amount by which values were
clipped, and the extent of the clipped area. Clipping may occur in any (or all)
of the pixel's color channels. Although clipped pixels are common and occur to
some degree in almost every image we tested, current automatic solutions have
only partial success in repairing clipped pixels and work only in limited cases
such as only with overexposure (not under-exposure) and when some of the color
channels are not clipped. Using neural networks and their ability to model
natural images allows our neural network, DeclipNet, to reconstruct data in
clipped regions producing state of the art results.Comment: 5 page
Machine Learning Based Real Bogus System for HSC-SSP Moving Object Detecting Pipeline
Machine learning techniques are widely applied in many modern optical sky
surveys, e.q. Pan-STARRS1, PTF/iPTF and Subaru/Hyper Suprime-Cam survey, to
reduce human intervention for data verification. In this study, we have
established a machine learning based real-bogus system to reject the false
detections in the Subaru/Hyper-Suprime-Cam StrategicSurvey Program (HSC-SSP)
source catalog. Therefore the HSC-SSP moving object detection pipeline can
operate more effectively due to the reduction of false positives. To train the
real-bogus system, we use the stationary sources as the real training set and
the "flagged" data as the bogus set. The training set contains 47 features,
most of which are photometric measurements and shape moments generated from the
HSC image reduction pipeline (hscPipe). Our system can reach a true positive
rate (tpr) ~96% with a false positive rate (fpr) ~ 1% or tpr ~99% at fpr ~5%.
Therefore we conclude that the stationary sources are decent real training
samples, and using photometry measurements and shape moments can reject the
false positives effectively.Comment: Ver.2, 18 pages, 6 figures, submitted to PASJ HSC special issu
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Three-dimensional (3D) fluorescence microscopy in general requires axial
scanning to capture images of a sample at different planes. Here we demonstrate
that a deep convolutional neural network can be trained to virtually refocus a
2D fluorescence image onto user-defined 3D surfaces within the sample volume.
With this data-driven computational microscopy framework, we imaged the neuron
activity of a Caenorhabditis elegans worm in 3D using a time-sequence of
fluorescence images acquired at a single focal plane, digitally increasing the
depth-of-field of the microscope by 20-fold without any axial scanning,
additional hardware, or a trade-off of imaging resolution or speed.
Furthermore, we demonstrate that this learning-based approach can correct for
sample drift, tilt, and other image aberrations, all digitally performed after
the acquisition of a single fluorescence image. This unique framework also
cross-connects different imaging modalities to each other, enabling 3D
refocusing of a single wide-field fluorescence image to match confocal
microscopy images acquired at different sample planes. This deep learning-based
3D image refocusing method might be transformative for imaging and tracking of
3D biological samples, especially over extended periods of time, mitigating
photo-toxicity, sample drift, aberration and defocusing related challenges
associated with standard 3D fluorescence microscopy techniques.Comment: 47 pages, 5 figures (main text
Improving Galaxy Clustering Measurements with Deep Learning: analysis of the DECaLS DR7 data
Robust measurements of cosmological parameters from galaxy surveys rely on
our understanding of systematic effects that impact the observed galaxy density
field. In this paper we present, validate, and implement the idea of adopting
the systematics mitigation method of Artificial Neural Networks for modeling
the relationship between the target galaxy density field and various
observational realities including but not limited to Galactic extinction,
seeing, and stellar density. Our method by construction allows a wide class of
models and alleviates over-training by performing k-fold cross-validation and
dimensionality reduction via backward feature elimination. By permuting the
choice of the training, validation, and test sets, we construct a selection
mask for the entire footprint. We apply our method on the extended Baryon
Oscillation Spectroscopic Survey (eBOSS) Emission Line Galaxies (ELGs)
selection from the Dark Energy Camera Legacy Survey (DECaLS) Data Release 7 and
show that the spurious large-scale contamination due to imaging systematics can
be significantly reduced by up-weighting the observed galaxy density using the
selection mask from the neural network and that our method is more effective
than the conventional linear and quadratic polynomial functions. We perform
extensive analyses on simulated mock datasets with and without systematic
effects. Our analyses indicate that our methodology is more robust to
overfitting compared to the conventional methods. This method can be utilized
in the catalog generation of future spectroscopic galaxy surveys such as eBOSS
and Dark Energy Spectroscopic Instrument (DESI) to better mitigate
observational systematics.Comment: 31 pages, 25 figures, accepted for publication in MNRAS. Moderate
revision throughout the paper. The new version includes a quantitative
evaluation of the remaining systematic effects in the DECaLS DR7 data in
Summary and Discussion and in Conclusion. Pipeline available at
https://github.com/mehdirezaie/SYSNe
Fractional Multiscale Fusion-based De-hazing
This report presents the results of a proposed multi-scale fusion-based
single image de-hazing algorithm, which can also be used for underwater image
enhancement. Furthermore, the algorithm was designed for very fast operation
and minimal run-time. The proposed scheme is the faster than existing
algorithms for both de-hazing and underwater image enhancement and amenable to
digital hardware implementation. Results indicate mostly consistent and good
results for both categories of images when compared with other algorithms from
the literature.Comment: 23 pages, 13 figures, 2 table
Neural Imaging Pipelines - the Scourge or Hope of Forensics?
Forensic analysis of digital photographs relies on intrinsic statistical
traces introduced at the time of their acquisition or subsequent editing. Such
traces are often removed by post-processing (e.g., down-sampling and
re-compression applied upon distribution in the Web) which inhibits reliable
provenance analysis. Increasing adoption of computational methods within
digital cameras further complicates the process and renders explicit
mathematical modeling infeasible. While this trend challenges forensic analysis
even in near-acquisition conditions, it also creates new opportunities. This
paper explores end-to-end optimization of the entire image acquisition and
distribution workflow to facilitate reliable forensic analysis at the end of
the distribution channel, where state-of-the-art forensic techniques fail. We
demonstrate that a neural network can be trained to replace the entire photo
development pipeline, and jointly optimized for high-fidelity photo rendering
and reliable provenance analysis. Such optimized neural imaging pipeline
allowed us to increase image manipulation detection accuracy from approx. 45%
to over 90%. The network learns to introduce carefully crafted artifacts, akin
to digital watermarks, which facilitate subsequent manipulation detection.
Analysis of performance trade-offs indicates that most of the gains can be
obtained with only minor distortion. The findings encourage further research
towards building more reliable imaging pipelines with explicit
provenance-guaranteeing properties.Comment: Manuscript + supplement; currently under review; compressed figures
to minimize file size. arXiv admin note: text overlap with arXiv:1812.0151
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