5 research outputs found
Automated Image Registration And Mosaicking For Multi-Sensor Images Acquired By A Miniature Unmanned Aerial Vehicle Platform
Algorithms for automatic image registration and mosaicking are developed for a miniature Unmanned Aerial Vehicle (MINI-UAV) platform, assembled by Air-O-Space International (AOSI) L.L.C.. Three cameras onboard this MINI-UAV platform acquire images in a single frame simultaneously at green (550nm), red (650 nm), and near infrared (820nm) wavelengths, but with shifting and rotational misalignment. The area-based method is employed in the developed algorithms for control point detection, which is applicable when no prominent feature details are present in image scenes. Because the three images to be registered have different spectral characteristics, region of interest determination and control point selection are the two key steps that ensure the quality of control points. Affine transformation is adopted for spatial transformation, followed by bilinear interpolation for image resampling. Mosaicking is conducted between adjacent frames after three-band co-registration. Pre-introducing the rotation makes the area-based method feasible when the rotational misalignment cannot be ignored. The algorithms are tested on three image sets collected at Stennis Space Center, Greenwood, and Oswalt in Mississippi. Manual evaluation confirms the effectiveness of the developed algorithms. The codes are converted into a software package, which is executable under the Microsoft Windows environment of personal computer platforms without the requirement of MATLAB or other special software support for commercial-off-the-shelf (COTS) product. The near real-time decision-making support is achievable with final data after its installation into the ground control station. The final products are color-infrared (CIR) composite and normalized difference vegetation index (NDVI) images, which are used in agriculture, forestry, and environmental monitoring
A new asymmetrical corner detector(ACD) for a semi-automatic image co-registration scheme
Co-registration of multi-sensor and multi-temporal images is essential for remote
sensing applications. In the image co-registration process, automatic Ground Control
Points (GCPs) selection is a key technical issue and the accuracy of GCPs localization
largely accounts for the final image co-registration accuracy. In this thesis, a novel
Asymmetrical Corner Detector (ACD) algorithm based on auto-correlation is
presented and a semi-automatic image co-registration scheme is proposed.
The ACD is designed with the consideration of the fact that asymmetrical corner
points are the most common reality in remotely sensed imagery data. The ACD selects
points more favourable to asymmetrical points rather than symmetrical points to avoid
incorrect selection of flat points which are often highly symmetrical. The experimental
results using images taken by different sensors indicate that the ACD has obtained
excellent performance in terms of point localization and computation efficiency. It is
more capable of selecting high quality GCPs than some well established corner
detectors favourable to symmetrical corner points such as the Harris Corner Detector
(Harris and Stephens, 1988).
A semi-automatic image co-registration scheme is then proposed, which employs the
ACD algorithm to extract evenly distributed GCPs across the overlapped area in the
reference image. The scheme uses three manually selected pairs of GCPs to determine
the initial transformation model and the overlapped area. Grid-control and nonmaximum
suppression methods are used to secure the high quality and spread
distribution of GCPs selected. It also involves the FNCC (fast normalised crosscorrelation)
algorithm (Lewis, 1995) to refine the corresponding point locations in the
input image and thus the GCPs are semi-automatically selected to proceed to the
polynomial fitting image rectification. The performance of the proposed coregistration
scheme has been demonstrated by registering multi-temporal, multi-sensor
and multi-resolution images taken by Landsat TM, ETM+ and SPOT sensors.
Experimental results show that consistent high registration accuracy of less than 0.7
pixels RMSE has been achieved.
Keywords: Asymmetrical corner points, image co-registration, AC
Automatic reconstruction from serial sections
In many experiments in biological and medical research, serial sectioning of biological
material is the only way to reveal the three dimensional (3D) structure and function.
For a number of reasons other 3D imaging techniques, such as CT, MRI, and confocal
microscopy, are not always adequate because they cannot provide the necessary
resolution or contrast, or because the specimen is too large, or because the staining
techniques require sectioning. Therefore for the foreseeable future reconstruction from
serial sections will remain the only method for 3D investigations in many biomedical
fields. Reconstruction is a difficult problem due to the loss of 3D alignment as the
sections are cut and, more seriously, the systematic and random distortion caused by
the sectioning and preparation processes.Many authors have reported how serial sections can be registered by means of fiducial
markers or otherwise, but there have been only a few studies of automated correction
of the sectioning distortions. In this thesis solutions to the registration problem are
reviewed and discussed, and a solution to the warping problem, based on image pro¬
cessing techniques and the finite element method (FEM), is presented. The aim of this
project was to develop a fully automatic method of reconstruction in order to provide a
3D atlas of mouse development as part of a gene expression database. For this purpose
it is not necessary to warp the object so that it is identical to the original object, but
to correct local distortions in the sections in order to produce a smooth representative
mouse embryo. Furthermore the use of fiducial markers was not possible because the
reconstructions were from already sectioned material.In this thesis we demonstrate a new method for warping serial sections. The sections
are warped by applying forces to each section, where each section is modelled as a thin
elastic plate. The deformation forces are determined from correspondences between
sections which are calculated by combining match strengths and positional information.
The equilibrium state which represents the reconstructed 3D image is calculated using
the finite element method. Results of the application of these methods to paraffin wax
and resin embedded sections of the mouse embryo are presented
Bayesian Hierarchical Model for Combining Two-resolution Metrology Data
This dissertation presents a Bayesian hierarchical model to combine two-resolution
metrology data for inspecting the geometric quality of manufactured parts. The high-
resolution data points are scarce, and thus scatter over the surface being measured,
while the low-resolution data are pervasive, but less accurate or less precise. Combining the two datasets could supposedly make a better prediction of the geometric
surface of a manufactured part than using a single dataset. One challenge in combining the metrology datasets is the misalignment which exists between the low- and
high-resolution data points.
This dissertation attempts to provide a Bayesian hierarchical model that can
handle such misaligned datasets, and includes the following components: (a) a Gaussian process for modeling metrology data at the low-resolution level; (b) a heuristic
matching and alignment method that produces a pool of candidate matches and
transformations between the two datasets; (c) a linkage model, conditioned on a
given match and its associated transformation, that connects a high-resolution data
point to a set of low-resolution data points in its neighborhood and makes a combined
prediction; and finally (d) Bayesian model averaging of the predictive models in (c)
over the pool of candidate matches found in (b). This Bayesian model averaging
procedure assigns weights to different matches according to how much they support
the observed data, and then produces the final combined prediction of the surface based on the data of both resolutions.
The proposed method improves upon the methods of using a single dataset as
well as a combined prediction without addressing the misalignment problem. This
dissertation demonstrates the improvements over alternative methods using both simulated data and the datasets from a milled sine-wave part, measured by two coordinate
measuring machines of different resolutions, respectively