46,281 research outputs found
Towards Accurate Camera Geopositioning by Image Matching
In this work, we present a camera geopositioning system based on matching a
query image against a database with panoramic images. For matching, our system
uses memory vectors aggregated from global image descriptors based on
convolutional features to facilitate fast searching in the database. To speed
up searching, a clustering algorithm is used to balance geographical
positioning and computation time. We refine the obtained position from the
query image using a new outlier removal algorithm. The matching of the query
image is obtained with a recall@5 larger than 90% for panorama-to-panorama
matching. We cluster available panoramas from geographically adjacent locations
into a single compact representation and observe computational gains of
approximately 50% at the cost of only a small (approximately 3%) recall loss.
Finally, we present a coordinate estimation algorithm that reduces the median
geopositioning error by up to 20%
Where and Who? Automatic Semantic-Aware Person Composition
Image compositing is a method used to generate realistic yet fake imagery by
inserting contents from one image to another. Previous work in compositing has
focused on improving appearance compatibility of a user selected foreground
segment and a background image (i.e. color and illumination consistency). In
this work, we instead develop a fully automated compositing model that
additionally learns to select and transform compatible foreground segments from
a large collection given only an input image background. To simplify the task,
we restrict our problem by focusing on human instance composition, because
human segments exhibit strong correlations with their background and because of
the availability of large annotated data. We develop a novel branching
Convolutional Neural Network (CNN) that jointly predicts candidate person
locations given a background image. We then use pre-trained deep feature
representations to retrieve person instances from a large segment database.
Experimental results show that our model can generate composite images that
look visually convincing. We also develop a user interface to demonstrate the
potential application of our method.Comment: 10 pages, 9 figure
High-resolution transport-of-intensity quantitative phase microscopy with annular illumination
For quantitative phase imaging (QPI) based on transport-of-intensity equation
(TIE), partially coherent illumination provides speckle-free imaging,
compatibility with brightfield microscopy, and transverse resolution beyond
coherent diffraction limit. Unfortunately, in a conventional microscope with
circular illumination aperture, partial coherence tends to diminish the phase
contrast, exacerbating the inherent noise-to-resolution tradeoff in TIE
imaging, resulting in strong low-frequency artifacts and compromised imaging
resolution. Here, we demonstrate how these issues can be effectively addressed
by replacing the conventional circular illumination aperture with an annular
one. The matched annular illumination not only strongly boosts the phase
contrast for low spatial frequencies, but significantly improves the practical
imaging resolution to near the incoherent diffraction limit. By incorporating
high-numerical aperture (NA) illumination as well as high-NA objective, it is
shown, for the first time, that TIE phase imaging can achieve a transverse
resolution up to 208 nm, corresponding to an effective NA of 2.66. Time-lapse
imaging of in vitro Hela cells revealing cellular morphology and subcellular
dynamics during cells mitosis and apoptosis is exemplified. Given its
capability for high-resolution QPI as well as the compatibility with widely
available brightfield microscopy hardware, the proposed approach is expected to
be adopted by the wider biology and medicine community.Comment: This manuscript was originally submitted on 20 Feb. 201
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