28,596 research outputs found
Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications
In this paper we present a semi-automatic 2D-3D local registration pipeline
capable of coloring 3D models obtained from 3D scanners by using uncalibrated
images. The proposed pipeline exploits the Structure from Motion (SfM)
technique in order to reconstruct a sparse representation of the 3D object and
obtain the camera parameters from image feature matches. We then coarsely
register the reconstructed 3D model to the scanned one through the Scale
Iterative Closest Point (SICP) algorithm. SICP provides the global scale,
rotation and translation parameters, using minimal manual user intervention. In
the final processing stage, a local registration refinement algorithm optimizes
the color projection of the aligned photos on the 3D object removing the
blurring/ghosting artefacts introduced due to small inaccuracies during the
registration. The proposed pipeline is capable of handling real world cases
with a range of characteristics from objects with low level geometric features
to complex ones
Non-iterative RGB-D-inertial Odometry
This paper presents a non-iterative solution to RGB-D-inertial odometry
system. Traditional odometry methods resort to iterative algorithms which are
usually computationally expensive or require well-designed initialization. To
overcome this problem, this paper proposes to combine a non-iterative front-end
(odometry) with an iterative back-end (loop closure) for the RGB-D-inertial
SLAM system. The main contribution lies in the novel non-iterative front-end,
which leverages on inertial fusion and kernel cross-correlators (KCC) to match
point clouds in frequency domain. Dominated by the fast Fourier transform
(FFT), our method is only of complexity , where is
the number of points. Map fusion is conducted by element-wise operations, so
that both time and space complexity are further reduced. Extensive experiments
show that, due to the lightweight of the proposed front-end, the framework is
able to run at a much faster speed yet still with comparable accuracy with the
state-of-the-arts
Distributed Representation of Geometrically Correlated Images with Compressed Linear Measurements
This paper addresses the problem of distributed coding of images whose
correlation is driven by the motion of objects or positioning of the vision
sensors. It concentrates on the problem where images are encoded with
compressed linear measurements. We propose a geometry-based correlation model
in order to describe the common information in pairs of images. We assume that
the constitutive components of natural images can be captured by visual
features that undergo local transformations (e.g., translation) in different
images. We first identify prominent visual features by computing a sparse
approximation of a reference image with a dictionary of geometric basis
functions. We then pose a regularized optimization problem to estimate the
corresponding features in correlated images given by quantized linear
measurements. The estimated features have to comply with the compressed
information and to represent consistent transformation between images. The
correlation model is given by the relative geometric transformations between
corresponding features. We then propose an efficient joint decoding algorithm
that estimates the compressed images such that they stay consistent with both
the quantized measurements and the correlation model. Experimental results show
that the proposed algorithm effectively estimates the correlation between
images in multi-view datasets. In addition, the proposed algorithm provides
effective decoding performance that compares advantageously to independent
coding solutions as well as state-of-the-art distributed coding schemes based
on disparity learning
A hybrid 3d reconstruction/registration algorithm for correction of head motion in emission tomography
Even with head restraint, small head movements can occur during data acquisition for emission tomography, sufficiently large to result in detectable artifacts in the final reconstruction. Direct measurement of motion can be cumbersome and difficult to implement, whereas previous attempts to correct for motion based on measured projections have been limited to simple translation orthogonal to the projection. A fully 3D algorithm is proposed that estimates the patient orientation at any time based on the projection of motion-corrupted data, with incorporation of the measured motion within subsequent OSEM sub-iterations. Preliminary studies have been performed using a digital version of the Hoffman brain phantom. Movement was simulated by constructing a mixed set of projections in two discrete positions of the phantom. The algorithm determined the phantom orientation that best aligned each constructed projection with its corresponding, measured projection. In the case of simulated movement of 24 of 64 projections, all mis-positioned projections were correctly identified. The algorithm resulted in a reduction of mean square difference (MSD) between motion corrected and motion-free reconstructions compared to the MSD between uncorrected and motion-free reconstructions by a factor of 2.7
2D-3D registration of CT vertebra volume to fluoroscopy projection: A calibration model assessment (doi:10.1155/2010/806094)
This study extends a previous research concerning intervertebral motion registration by means of 2D dynamic fluoroscopy to obtain a more comprehensive 3D description of vertebral kinematics. The problem of estimating the 3D rigid pose of a CT volume of a vertebra from its 2D X-ray fluoroscopy projection is addressed. 2D-3D registration is obtained maximising a measure of similarity between Digitally Reconstructed Radiographs (obtained from the CT volume) and real fluoroscopic projection. X-ray energy correction was performed. To assess the method a calibration model was realised a sheep dry vertebra was rigidly fixed to a frame of reference including metallic markers. Accurate measurement of 3D orientation was obtained via single-camera calibration of the markers and held as true 3D vertebra position; then, vertebra 3D pose was estimated and results compared. Error analysis revealed accuracy of the order of 0.1 degree for the rotation angles of about 1?mm for displacements parallel to the fluoroscopic plane, and of order of 10?mm for the orthogonal displacement.<br/
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