4,555 research outputs found
The SIMCA algorithm for processing Ground Penetrating Radar data and its use in landmine detection
The main challenge of ground penetrating radar (GPR)
based land mine detection is to have an accurate image
analysis method that is capable of reducing false alarms.
However an accurate image relies on having sufficient spatial
resolution in the received signal. But because the diameter
of an AP mine can be as low as 2cm and many soils
have very high attenuations at frequencies above 3GHz,
the accurate detection of landmines is accomplished using
advanced algorithms. Using image reconstruction and
by carrying out the system level analysis of the issues involved
with recognition of landmines allows the landmine
detection problem to be solved. The SIMCA (’SIMulated
Correlation Algorithm’) is a novel and accurate landmine
detection tool that carries out correlation between a simulated
GPR trace and a clutter1 removed original GPR
trace. This correlation is performed using the MATLAB
R
processing environment. The authors tried using convolution
and correlation. But in this paper the correlated results
are presented because they produced better results.
Validation of the results from the algorithm was done by
an expert GPR user and 4 other general users who predict
the location of landmines. These predicted results are
compared with the ground truth data
The SIMCA algorithm for processing Ground Penetrating Radar data and its use in locating foundations in demolished buildings
Abstract—The main challenge of ground penetrating radar
GPR) based foundation detection is to have an accurate image
analysis method. In order to solve the detection problem a
system level analysis of the issues involved with the recognition of
foundations using image reconstruction is required. The SIMCA
(’SIMulated Correlation Algorithm’) is a technique based on
an area correlation between the trace that would be returned
by an ideal point reflector in the soil conditions at the site
and the actual trace. During an initialization phase, SIMCA
carries out radar simulation using the design parameters of the
radar and soil properties. Then SIMCA takes the raw data as
the radar is scanned over the ground and in real-time uses a
clutter removal technique to remove various clutter such as cross
talk, initial ground reflection and antenna ringing. The trace
which would be returned by a target under these conditions
is then used to form a correlation kernel. The GPR b-scan is
then correlated with the kernel using the Pearson correlation
coefficient, resulting in a correlated image which is brightest at
points most similar to the canonical target. This image is then
raised to an odd power >2 to enhance the target/background
separation. To validate and compare the algorithm, photographs
of the building before it was demolished along with processed data
using the REFLEXW package were used. The results produced
by the SIMCA algorithm were very promising and were able to
locate some features that the REFLEXW package were not able
to identify
Anisotropic Mesh Adaptation for Image Representation
Triangular meshes have gained much interest in image representation and have
been widely used in image processing. This paper introduces a framework of
anisotropic mesh adaptation (AMA) methods to image representation and proposes
a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme.
Different than many other methods that triangulate sample points to form the
mesh, the AMA methods start directly with a triangular mesh and then adapt the
mesh based on a user-defined metric tensor to represent the image. The AMA
methods have clear mathematical framework and provides flexibility for both
image representation and image reconstruction. A mesh patching technique is
developed for the implementation of the GPRAMA method, which leads to an
improved version of the popular GPRFS-ED method. The GPRAMA method can achieve
better quality than the GPRFS-ED method but with lower computational cost.Comment: 25 pages, 15 figure
Learning based automatic face annotation for arbitrary poses and expressions from frontal images only
Statistical approaches for building non-rigid deformable models, such as the active appearance model (AAM), have enjoyed great popularity in recent years, but typically require tedious manual annotation of training images. In this paper, a learning based approach for the automatic annotation of visually deformable objects from a single annotated frontal image is presented and demonstrated on the example of automatically annotating face images that can be used for building AAMs for fitting and tracking. This approach employs the idea of initially learning the correspondences between landmarks in a frontal image and a set of training images with a face in arbitrary poses. Using this learner, virtual images of unseen faces at any arbitrary pose for which the learner was trained can be reconstructed by predicting the new landmark locations and warping the texture from the frontal image. View-based AAMs are then built from the virtual images and used for automatically annotating unseen images, including images of different facial expressions, at any random pose within the maximum range spanned by the virtually reconstructed images. The approach is experimentally validated by automatically annotating face images from three different databases
Documenting Bronze Age Akrotiri on Thera using laser scanning, image-based modelling and geophysical prospection
The excavated architecture of the exceptional prehistoric site of Akrotiri on the Greek island of Thera/Santorini is endangered by gradual decay, damage due to accidents, and seismic shocks, being located on an active volcano in an earthquake-prone area. Therefore, in 2013 and 2014 a digital documentation project has been conducted with support of the National Geographic Society in order to generate a detailed digital model of Akrotiri’s architecture using terrestrial laser scanning and image-based modeling. Additionally, non-invasive geophysical prospection has been tested in order to investigate its potential to explore and map yet buried archaeological remains. This article describes the project and the generated results
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