2,526 research outputs found
Deep Burst Denoising
Noise is an inherent issue of low-light image capture, one which is
exacerbated on mobile devices due to their narrow apertures and small sensors.
One strategy for mitigating noise in a low-light situation is to increase the
shutter time of the camera, thus allowing each photosite to integrate more
light and decrease noise variance. However, there are two downsides of long
exposures: (a) bright regions can exceed the sensor range, and (b) camera and
scene motion will result in blurred images. Another way of gathering more light
is to capture multiple short (thus noisy) frames in a "burst" and intelligently
integrate the content, thus avoiding the above downsides. In this paper, we use
the burst-capture strategy and implement the intelligent integration via a
recurrent fully convolutional deep neural net (CNN). We build our novel,
multiframe architecture to be a simple addition to any single frame denoising
model, and design to handle an arbitrary number of noisy input frames. We show
that it achieves state of the art denoising results on our burst dataset,
improving on the best published multi-frame techniques, such as VBM4D and
FlexISP. Finally, we explore other applications of image enhancement by
integrating content from multiple frames and demonstrate that our DNN
architecture generalizes well to image super-resolution
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Gas Detection and Identification Using Multimodal Artificial Intelligence Based Sensor Fusion
With the rapid industrialization and technological advancements, innovative
engineering technologies which are cost effective, faster and easier to
implement are essential. One such area of concern is the rising number of
accidents happening due to gas leaks at coal mines, chemical industries, home
appliances etc. In this paper we propose a novel approach to detect and
identify the gaseous emissions using the multimodal AI fusion techniques. Most
of the gases and their fumes are colorless, odorless, and tasteless, thereby
challenging our normal human senses. Sensing based on a single sensor may not
be accurate, and sensor fusion is essential for robust and reliable detection
in several real-world applications. We manually collected 6400 gas samples
(1600 samples per class for four classes) using two specific sensors: the
7-semiconductor gas sensors array, and a thermal camera. The early fusion
method of multimodal AI, is applied The network architecture consists of a
feature extraction module for individual modality, which is then fused using a
merged layer followed by a dense layer, which provides a single output for
identifying the gas. We obtained the testing accuracy of 96% (for fused model)
as opposed to individual model accuracies of 82% (based on Gas Sensor data
using LSTM) and 93% (based on thermal images data using CNN model). Results
demonstrate that the fusion of multiple sensors and modalities outperforms the
outcome of a single sensor.Comment: 14 Pages, 9 Figure
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