632 research outputs found
Implication of Air pollution on health effects in Nepal: Lessons from global research
The Nepal Health Research Council and recent National Health Policy of Nepal (2015/16) have included ‘air pollution’ as a priority research/public health agenda that is guaranteed by the Constitution. There is an urgent need to organise the future policies and actions to ensure the commitments to reduce air pollution
Evaluation of Color Anomaly Detection in Multispectral Images For Synthetic Aperture Sensing
In this article, we evaluate unsupervised anomaly detection methods in
multispectral images obtained with a wavelength-independent synthetic aperture
sensing technique, called Airborne Optical Sectioning (AOS). With a focus on
search and rescue missions that apply drones to locate missing or injured
persons in dense forest and require real-time operation, we evaluate runtime
vs. quality of these methods. Furthermore, we show that color anomaly detection
methods that normally operate in the visual range always benefit from an
additional far infrared (thermal) channel. We also show that, even without
additional thermal bands, the choice of color space in the visual range already
has an impact on the detection results. Color spaces like HSV and HLS have the
potential to outperform the widely used RGB color space, especially when color
anomaly detection is used for forest-like environments.Comment: 12 pages, 6 figures, 3 table
Attending to Discriminative Certainty for Domain Adaptation
In this paper, we aim to solve for unsupervised domain adaptation of
classifiers where we have access to label information for the source domain
while these are not available for a target domain. While various methods have
been proposed for solving these including adversarial discriminator based
methods, most approaches have focused on the entire image based domain
adaptation. In an image, there would be regions that can be adapted better, for
instance, the foreground object may be similar in nature. To obtain such
regions, we propose methods that consider the probabilistic certainty estimate
of various regions and specify focus on these during classification for
adaptation. We observe that just by incorporating the probabilistic certainty
of the discriminator while training the classifier, we are able to obtain state
of the art results on various datasets as compared against all the recent
methods. We provide a thorough empirical analysis of the method by providing
ablation analysis, statistical significance test, and visualization of the
attention maps and t-SNE embeddings. These evaluations convincingly demonstrate
the effectiveness of the proposed approach.Comment: CVPR 2019 Accepted, Project: https://delta-lab-iitk.github.io/CADA
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