93,921 research outputs found
Spectral mixture analysis of EELS spectrum-images
Recent advances in detectors and computer science have enabled the
acquisition and the processing of multidimensional datasets, in particular in
the field of spectral imaging. Benefiting from these new developments, earth
scientists try to recover the reflectance spectra of macroscopic materials
(e.g., water, grass, mineral types...) present in an observed scene and to
estimate their respective proportions in each mixed pixel of the acquired
image. This task is usually referred to as spectral mixture analysis or
spectral unmixing (SU). SU aims at decomposing the measured pixel spectrum into
a collection of constituent spectra, called endmembers, and a set of
corresponding fractions (abundances) that indicate the proportion of each
endmember present in the pixel. Similarly, when processing spectrum-images,
microscopists usually try to map elemental, physical and chemical state
information of a given material. This paper reports how a SU algorithm
dedicated to remote sensing hyperspectral images can be successfully applied to
analyze spectrum-image resulting from electron energy-loss spectroscopy (EELS).
SU generally overcomes standard limitations inherent to other multivariate
statistical analysis methods, such as principal component analysis (PCA) or
independent component analysis (ICA), that have been previously used to analyze
EELS maps. Indeed, ICA and PCA may perform poorly for linear spectral mixture
analysis due to the strong dependence between the abundances of the different
materials. One example is presented here to demonstrate the potential of this
technique for EELS analysis.Comment: Manuscript accepted for publication in Ultramicroscop
SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis
Synthesizing realistic images from human drawn sketches is a challenging
problem in computer graphics and vision. Existing approaches either need exact
edge maps, or rely on retrieval of existing photographs. In this work, we
propose a novel Generative Adversarial Network (GAN) approach that synthesizes
plausible images from 50 categories including motorcycles, horses and couches.
We demonstrate a data augmentation technique for sketches which is fully
automatic, and we show that the augmented data is helpful to our task. We
introduce a new network building block suitable for both the generator and
discriminator which improves the information flow by injecting the input image
at multiple scales. Compared to state-of-the-art image translation methods, our
approach generates more realistic images and achieves significantly higher
Inception Scores.Comment: Accepted to CVPR 201
Cross-View Image Matching for Geo-localization in Urban Environments
In this paper, we address the problem of cross-view image geo-localization.
Specifically, we aim to estimate the GPS location of a query street view image
by finding the matching images in a reference database of geo-tagged bird's eye
view images, or vice versa. To this end, we present a new framework for
cross-view image geo-localization by taking advantage of the tremendous success
of deep convolutional neural networks (CNNs) in image classification and object
detection. First, we employ the Faster R-CNN to detect buildings in the query
and reference images. Next, for each building in the query image, we retrieve
the nearest neighbors from the reference buildings using a Siamese network
trained on both positive matching image pairs and negative pairs. To find the
correct NN for each query building, we develop an efficient multiple nearest
neighbors matching method based on dominant sets. We evaluate the proposed
framework on a new dataset that consists of pairs of street view and bird's eye
view images. Experimental results show that the proposed method achieves better
geo-localization accuracy than other approaches and is able to generalize to
images at unseen locations
Quantifying loopy network architectures
Biology presents many examples of planar distribution and structural networks
having dense sets of closed loops. An archetype of this form of network
organization is the vasculature of dicotyledonous leaves, which showcases a
hierarchically-nested architecture containing closed loops at many different
levels. Although a number of methods have been proposed to measure aspects of
the structure of such networks, a robust metric to quantify their hierarchical
organization is still lacking. We present an algorithmic framework, the
hierarchical loop decomposition, that allows mapping loopy networks to binary
trees, preserving in the connectivity of the trees the architecture of the
original graph. We apply this framework to investigate computer generated
graphs, such as artificial models and optimal distribution networks, as well as
natural graphs extracted from digitized images of dicotyledonous leaves and
vasculature of rat cerebral neocortex. We calculate various metrics based on
the Asymmetry, the cumulative size distribution and the Strahler bifurcation
ratios of the corresponding trees and discuss the relationship of these
quantities to the architectural organization of the original graphs. This
algorithmic framework decouples the geometric information (exact location of
edges and nodes) from the metric topology (connectivity and edge weight) and it
ultimately allows us to perform a quantitative statistical comparison between
predictions of theoretical models and naturally occurring loopy graphs.Comment: 17 pages, 8 figures. During preparation of this manuscript the
authors became aware of the work of Mileyko at al., concurrently submitted
for publicatio
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Fostering Open Sensemaking Communities by Combining Knowledge Maps and Videoconferencing
In this paper, our aim is to investigate the role of Compendium maps for both learners and educators to share and debate interpretations in FlashMeetingTM (FM) videoconferences in the context of OpenLearn, an online environment for open learning. This work is based on a qualitative study of knowledge maps and web videoconferencing interactions, and quantitative data presented in diagnostic reports about both tools. Our theoretical approach is based on the sensemaking concept and an existing framework for three learning scenarios. Our findings describe four applications of knowledge maps in videoconferencing: (i) Mind Maps for a FM virtual lecture (transmission scenario); (ii) Learning Path Map which integrates a FM conference (studio scenario); (iii) Concept Maps during a peer-to-peer event (negotiation scenario) and (iv) Web Maps for a FM replay (assessment scenario)
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