146 research outputs found
HPV16 E6 gene variations in invasive cervical squamous cell carcinoma and cancer in situ from Russian patients
HPV16 is frequently seen in invasive cervical cancer (ICC) and cervical intraepithelial neoplasia (CIN). Its E6 gene has frequent sequence variations. Although some E6 variants have been reported to have different biochemical or biological properties, they do not show geographical identity. Moreover, the definition of ‘variant’ has been a source of confusion because it has been based on all departures from the ‘prototype’ once isolated randomly from an ICC case. We amplified the HPV16 E6 gene by PCR from fresh-frozen tissue of 104 cases of ICC and CIN from Russian patients and sequenced it in positive cases. We found that 32 of 55 (58.2%) ICC cases and 18 of 49 (36.7%) CIN cases were HPV 16-positive and we could identify 3 groups of E6 variants: group A was characterized by G at nt 350 where group B had T, and group M was a heterogeneous mixture of unique E6 variants; no significant difference existed in the distribution of the different groups between ICC and CIN; the clinically malignant (as defined by FIGO stage) order between the groups was M > A > B in ICC; in the cases with a single HPV16 E6 sequence, coexisting ICC, CIN and normal epithelium in the same patient shared the E6 variant; and 4 cases of ICC had double/multiple E6 variants. The results did not show any importance of E6 variants for ICC progression in Russian women. The results also indicated that the original HPV16 variant persisted during ICC progression, and that at a low frequency, double infections and/or mutation of variants might occur. © 2001 Cancer Research Campaign http://www.bjcancer.co
Автоматизированное рабочее место партнера ОАО «Гомсельмаш»
This paper describes a system for structure-and-motion estimation for real-time navigation and obstacle avoidance. We demonstrate it technique to increase the efficiency of the 5-point solution to the relative pose problem. This is achieved by a novel sampling scheme, where We add a distance constraint on the sampled points inside the RANSAC loop. before calculating the 5-point solution. Our setup uses the KLT tracker to establish point correspondences across tone in live video We also demonstrate how an early outlier rejection in the tracker improves performance in scenes with plenty of occlusions. This outlier rejection scheme is well Slated to implementation on graphics hardware. We evaluate the proposed algorithms using real camera sequences with fine-tuned bundle adjusted data as ground truth. To strenghten oar results we also evaluate using sequences generated by a state-of-the-art rendering software. On average we are able to reduce the number of RANSAC iterations by half and thereby double the speed.DIPLEC
Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle
The internal calibration of a pinhole camera is given by five parameters that
are combined into an upper-triangular calibration matrix. If the
skew parameter is zero and the aspect ratio is equal to one, then the camera is
said to have Euclidean image plane. In this paper, we propose a non-iterative
self-calibration algorithm for a camera with Euclidean image plane in case the
remaining three internal parameters --- the focal length and the principal
point coordinates --- are fixed but unknown. The algorithm requires a set of point correspondences in two views and also the measured relative
rotation angle between the views. We show that the problem generically has six
solutions (including complex ones).
The algorithm has been implemented and tested both on synthetic data and on
publicly available real dataset. The experiments demonstrate that the method is
correct, numerically stable and robust.Comment: 13 pages, 7 eps-figure
Incremental Non-Rigid Structure-from-Motion with Unknown Focal Length
The perspective camera and the isometric surface prior have recently gathered
increased attention for Non-Rigid Structure-from-Motion (NRSfM). Despite the
recent progress, several challenges remain, particularly the computational
complexity and the unknown camera focal length. In this paper we present a
method for incremental Non-Rigid Structure-from-Motion (NRSfM) with the
perspective camera model and the isometric surface prior with unknown focal
length. In the template-based case, we provide a method to estimate four
parameters of the camera intrinsics. For the template-less scenario of NRSfM,
we propose a method to upgrade reconstructions obtained for one focal length to
another based on local rigidity and the so-called Maximum Depth Heuristics
(MDH). On its basis we propose a method to simultaneously recover the focal
length and the non-rigid shapes. We further solve the problem of incorporating
a large number of points and adding more views in MDH-based NRSfM and
efficiently solve them with Second-Order Cone Programming (SOCP). This does not
require any shape initialization and produces results orders of times faster
than many methods. We provide evaluations on standard sequences with
ground-truth and qualitative reconstructions on challenging YouTube videos.
These evaluations show that our method performs better in both speed and
accuracy than the state of the art.Comment: ECCV 201
Non-Parametric Sequential Frame Decimation for Scene Reconstruction in Low-Memory Streaming Environments
Abstract not provide
A -adic RanSaC algorithm for stereo vision using Hensel lifting
A -adic variation of the Ran(dom) Sa(mple) C(onsensus) method for solving
the relative pose problem in stereo vision is developped. From two 2-adically
encoded images a random sample of five pairs of corresponding points is taken,
and the equations for the essential matrix are solved by lifting solutions
modulo 2 to the 2-adic integers. A recently devised -adic hierarchical
classification algorithm imitating the known LBG quantisation method classifies
the solutions for all the samples after having determined the number of
clusters using the known intra-inter validity of clusterings. In the successful
case, a cluster ranking will determine the cluster containing a 2-adic
approximation to the "true" solution of the problem.Comment: 15 pages; typos removed, abstract changed, computation error remove
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
A New Solution to the Relative Orientation Problem using only 3 Points and the Vertical Direction
This paper presents a new method to recover the relative pose between two
images, using three points and the vertical direction information. The vertical
direction can be determined in two ways: 1- using direct physical measurement
like IMU (inertial measurement unit), 2- using vertical vanishing point. This
knowledge of the vertical direction solves 2 unknowns among the 3 parameters of
the relative rotation, so that only 3 homologous points are requested to
position a couple of images. Rewriting the coplanarity equations leads to a
simpler solution. The remaining unknowns resolution is performed by an
algebraic method using Grobner bases. The elements necessary to build a
specific algebraic solver are given in this paper, allowing for a real-time
implementation. The results on real and synthetic data show the efficiency of
this method
EglN3 hydroxylase stabilizes BIM-EL linking VHL type 2C mutations to pheochromocytoma pathogenesis and chemotherapy resistance
Model-free Consensus Maximization for Non-Rigid Shapes
Many computer vision methods use consensus maximization to relate
measurements containing outliers with the correct transformation model. In the
context of rigid shapes, this is typically done using Random Sampling and
Consensus (RANSAC) by estimating an analytical model that agrees with the
largest number of measurements (inliers). However, small parameter models may
not be always available. In this paper, we formulate the model-free consensus
maximization as an Integer Program in a graph using `rules' on measurements. We
then provide a method to solve it optimally using the Branch and Bound (BnB)
paradigm. We focus its application on non-rigid shapes, where we apply the
method to remove outlier 3D correspondences and achieve performance superior to
the state of the art. Our method works with outlier ratio as high as 80\%. We
further derive a similar formulation for 3D template to image matching,
achieving similar or better performance compared to the state of the art.Comment: ECCV1
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