Who's closest - Factbites

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Topic: Who's closest

Note: these results are not from the primary (high quality) database.

In the News (Tue 22 Dec 09)

Northern Trust

Marvin Harrison

Peter Chernin

Gary Locke

Match Play

Penelope Cruz

Mickey Rourke

AR Rahman

Danny Boyle

Hugh Jackman

	The Development of a 3D Point Distribution Model of the Cortical Surface of the Brain
		Closest points were established using Euclidean distance and aligned using the quaternion method [12].
		The closest point was found on the target for each model point, then a fit was made and the process was repeated.
		Each point is sized to show its relative amplitude of movement and coloured to indicate its direction in global 3D co-ordinates using a combination of red, green, and blue.
	www.bmva.ac.uk /bmvc/1997/papers/105/105.html

	Abstract
		A hybrid Iterative Closest Point (ICP) scheme is proposed to integrate two classical ICP algorithms for fine registration of the two scans.
		This paper describes a procedure for constructing a database of 3D face models and matching this database to 2.5D face scans which are captured from different views, using coordinate system invariant properties of the facial surface.
		The candidate list used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage.
	www.cse.msu.edu /~lvxiaogu/research/abstract.htm

	Iterative parametric point algorithm
		The information from the parametric domain is used to accelerate the closest point search in a comparison to the ICP algorithm.
		In the first one, the corresponding points are determined according to the current values of the parameters and in the second one, the values of the parameters are updated so that the mean of the squares of weighted distances between the corresponding points is minimized.
		10], it is assumed that the precision of the measuring is the same at all points while we extend the weighting to the case where the precision varies within the scene coverage.
	foto.hut.fi /~ojokinen/vtyo/node13.html

	Publications
		The existing approaches to feature point tracking have limited capabilities in handling incomplete trajectories, especially when the number of points and their speeds are large, and trajectory ambiguities are frequent.
		Most of the existing approaches to feature point tracking have limited capabilities in handling incomplete trajectories, especially when the number of points and their speeds are large, and trajectory ambiguities are frequent.
		Synthetic point set motion is used to systematically test the algorithms at various point densities and speeds.
	visual.ipan.sztaki.hu /publ/publ.html

	BMVC '97 paper 060
		Then for each data point: the three nearest model points that form a surface element were found and a plane was fitted to these points, and the perpendicular distance from the data point to this surface was calculated.
		The eigenvectors are of the form of a list of the deviations in the point locations from their mean positions for each independent mode of variation, and the eigenvalues are the variances associated with each mode of variation, as exhibited in the training set.
		Deformable models such as the Point Distribution Model (PDM) have proven to be reliable methods for capturing the statistical variation exhibited in a group of related shapes, and have found many applications in fields such as object recognition and classification [1,2].
	www.bmva.ac.uk /bmvc/1997/papers/060/060.html

	Margrit Betke's Medical Image Analysis Project at Boston University: Registration
		Initial landmark registration: Four points used for registration are shown for each scan: the center of the trachea cross-section in slice A and the centers of the cross-sections of sternum, trachea, and vertebra in slice B in each study.
		On the left, the points in study 2 (blue) are aligned to the points in study 1 (red) by the gold-standard rigid-body transformation that minimizes the sum of squared differences (SSD) between the 42 point pairs.
		On the right, the points in study 2 (blue) are aligned to the points in study 1 (red) using the rigid-body transformation that minimizes the SSD between corresponding point pairs on the lung surfaces using our registration algorithm.
	www.cs.bu.edu /faculty/betke/research/registration-images.html

	Final report about the internship
		However, if no points exist in that region, the region is expanded in every direction by one voxel and the search is repeated till a closest point is found.
		Instead of using the closest point by the nearest neighbor search algorithm, we took the average of the two closest points.
		That number is that pointís distance from its closest point in scan 2.
	www.cra.org /Activities/craw/dmp/awards/2002/thomas/finalreport.html

	The Tom Bearden Website
		The closest known prior art to the invention’s deliberate engineering and use of time-reversal zones is the cold fusion experiments conducted by Pons, Fleischmann, and others in the mid 1990s.
		We point out that both "movements through time" of the operating cells and their internal parts continue to exist vectorially; the body's mass energy is still moving through forward time at the same velocity, but is simultaneously moving through reversed time at a greater velocity.
		It is further pointed out that, in a curved spacetime, the photon may be said to have mass, as shown by Lehnert and Roy [57].
	www.cheniere.org /patent%20application/claim.htm

	Efficient Variants of the ICP Algorithm
		The ICP (Iterative Closest Point) algorithm is widely used for geometric alignment of three-dimensional models when an initial estimate of the relative pose is known.
		Many variants of ICP have been proposed, affecting all phases of the algorithm from the selection and matching of points to the minimization strategy.
		We enumerate and classify many of these variants, and evaluate their effect on the speed with which the correct alignment is reached.
	www.cs.princeton.edu /~smr/papers/fasticp

	美国专利申请公开说明书 20030190091 - Dual bootstrap iterative closest point method and algorithm for image registration
		Dual bootstrap iterative closest point method and algorithm for image registration
		An iterative method and associated algorithm for performing image registration to map features in a first image to corresponding features in a second image in accordance with a transformation model.
		Upon convergence of a parameter vector associated with the model, a current bootstrap region includes and exceeds an initial bootstrap region that is initially established.
	cxp.paterra.com /uspregrant20030190091cn.html

	Kurt3D - An Autonomous Mobile Robot for Modelling the World in 3D
		The time-consuming search within the ICP algorithm is replaced by direct calculation of the closest point and the transformation is efficiently calculated by the use of quaternions.
		Data points form the model set and their projections to the plane to form the data set for each iteration of the ICP algorithm.
		An ICP based optimization is started, if more than a certain number of data points exist in an epsilon area spanned by the estimated plane.
	www.ercim.org /publication/Ercim_News/enw55/nuechter.html

	CVSSP 3DVision: Introduction to Model Building from 3D Surface Measurements
		ICP is 'a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces'.
		For example if we have three overlapping points sets and we compute the pairwise registration between sets one and two followed by pairwise registration between sets one and three then we do not necessarily minimise the mean square distance between sets two and three.
		Nearest point correspondences are not used if either point is on the mesh boundary or the distance exceeds a constant threshold.
	www.ee.surrey.ac.uk /Research/VSSP/3DVision/model_building/model.html

	Haitao Zhang
		Abstract This paper presents a powerful variant of the ICP (Iterative Closest Point) algorithm for registering range images using a probability field.
		The point parameterization method is used first to color most of the points, and the point neighborhood matching method is then applied to the points belonging to the gaps between the parameterized patches to minimize the discontinuity.
		The first method, the point parameterization method, uses a fast distortion-bounded parameterization algorithm to flatten the point model’s surface into one or more 2D patches.
	www.cs.sunysb.edu /~haitao

	Landmark Detection in the Chest and
		Four points used for registration are shown for each scan: the center of the trachea cross-section in slice A and the centers of the cross-sections of sternum, trachea, and vertebra in slice B in each study.
		The surface transformation is applied to align nodules in the initial CT scan with nodules in the follow-up scan.
		The landmarks in study 1 (green) are then being matched to the landmarks in study 2 (red).
	cs-people.bu.edu /hhong/research.htm

	Computer Aided Surgery Exercise 3
		What is the breakdown point of the algorithm, for what rotations/translations does it converge to a minimum which is not the global one, assuming that the initial transformation is the identity.
		Point pairing in each iteration is done using your kd-tree implementation.
		For the analysis use the point sets I gave you (cloud.pnt, cloudSubSet.pnt).
	www.cs.huji.ac.il /course/2002/cas/exercises/ex3

	Iterative parametric point (IPP) algorithm
		This distance image is used in the standard iterative closest point (ICP) algorithm [Besl and McKay, 1992].
		which has been defined as the corresponding point by the closest point operation.
		For high accuracy, the interpolation errors should be as equal as possible within the overlapping areas.
	foto.hut.fi /~ojokinen/plovdiv_lecture/node10.html

	Sexual Paradox: Chaos
		Closest spins have the most powerful neighbouring interaction with the strength falling off e.g.
		The logistic map: (a) As r increases from 2 to 4 the attracting final state is initially a single curve (an equilibrium point for each r) but then it repeatedly subdivides (pitchfork bifurcations) to make a rich and lean year (period 2) then periods 4, 8 etc., finally entering chaos (stippled bands).
		For small r the system tends to a fixed equilibrium point then becomes repelling bifurcating to form a flip-flop (period 2), subsequently period doubling to form periods 4, 8, 16 etc. through to infinity.
	www.dhushara.com /paradoxhtm/chaos.htm

	IRIS-Vision Seminars
		In this work I analyze the problem of the registration of multiple triangulated surfaces, through the implementation of the ICP (Iterative Closest Point) algorithm, in a version adapted to this specific input format.
		In this paper I will evaluate ways to adapt the ICP algorithm to the specific data input of triangulated surfaces, which is one of the most common ways to represent a 3D surface.
		The registration of multiple data sets is necessary when a 3D model of a real object is built by merging reconstructions from several data acquitisions taken from different points of view.
	iris.usc.edu /Information/seminars/corsi.html

	Registration
		The problem of finding the nearest point q on a geometric shape Q to a given point p, where Q is represented either by a set of segments, implicit curves, parametric curves, triangular faceted surfaces, implicit surfaces, or parametric surfaces can be solved using the Iterative Closest Point (ICP) technique.
		The iterative registration algorithm uses an initial estimate of the 3-D rigid transformation between the two partially overlapping range images, P and Q, to determine the final estimate of the 3D rigid transformation.
		Another problem with the ICP algorithm is that it requires that every point in one surface has a corresponding point on the other.
	sir.jrc.it /3d/3DReconstruction/Registration/Registration.htm

	Biometrics: Project Abstracts
		This alignment is used by the well-known iterative closest point algorithm (ICP) to compute a transformation matrix that defines the spatial relationship between the two impressions.
		Identical twins have the closest genetics-based relationship and, therefore, the maximum similarity between fingerprints is expected to be found among identical twins.
		It iteratively moves the vertices of the mesh model to smoothen the non-feature areas, and uses the 2.5D active contours to refine feature boundaries.
	biometrics.cse.msu.edu /abstracts.html

	The Parallel Iterative Closest Point Algorithm
		This paper describes a parallel implementation developed to improve the time performance of the Iterative Closest Point Algorithm.
		We also found that thinning the image by randomly removing a certain percentage of the points did not improve the performance, when viewed as the progression of mse with time.
		Randomly distributing the points among the processor resources resulted in a better load balancing, which further improved time performance.
	csdl.computer.org /comp/proceedings/3dim/2001/0984/00/09840195abs.htm

	Funding Proposal
		Some of the referees have pointed out a missing dimension from the proposed study; namely, the question of whether concepts such as "wildness" and "natural ecosystems" refer to conditions that are objectively defined and determinable, or whether, on the other hand, they reflect historical and cultural factors, or even political agendas.
		As the NSF guidelines correctly point out, the promise that "we will review the literature and then proceed" is a lame and vacuous statement of method.
		It should be noted that these tasks are iterative - that is, advancement on the "higher levels" will "feed back" to enrich content of the lower levels.
	gadfly.igc.org /ecology/proposal.htm

	Algorithms Seminar - Fall 2004
		The Iterative Closest Point (ICP) algorithm finds a locally optimal matching and alignment of two shapes with no prior knowledge of the correspondence.
		We use this to permute points in 3d and 4d for faster incremental computation of Delaunay diagrams, but believe that it has other applications in databases and geographic information systems.
		I will also point out some current challenges related to extending and analyzing ICP, highlighting the direction of some ongoing research.
	www.cs.duke.edu /~adanner/algorithms/fall04/fall04.html

	Nonlinear Science FAQ
		More precisely: A map f is chaotic on a compact invariant set S if (i) f is transitive on S (there is a point x whose orbit is dense in S), and (ii) f exhibits sensitive dependence on S (see [2.10]).
		The neighborhood of points that eventually approach the attractor is the basin of attraction for the attractor.
		If we start the ball at a point in the bowl with a velocity too small to reach the edge of the bowl, then eventually the ball will settle down to the bottom of the bowl with zero velocity: thus this equilibrium point is an attractor.
	www.faqs.org /faqs/sci/nonlinear-faq

	Department of Energy Information Bridge - full-text scientific and technical reports (gray literature)
		A commonly used technique, iterative closest point, is efficient but is unable to deal with outliers or avoid local minima.
		Therefore, the algorithm developed in this paper is a hybrid algorithm that combines the speed of iterative closest point with the robustness of simulated annealing.
		Another commonly used optimization algorithm, simulated annealing, is effective at dealing with local minima but is very slow.
	www.osti.gov /bridge/product.biblio.jsp?osti_id=756076

	Paul Thompson's Research Publications
		They are matched with their counterparts in a target brain using (1) an iterative closest point algorithm, which finds candidate lines for matching, and (2) topological criteria to enforce one-to-one matching of curves and to ensure that their internal points are matched in a consistent, serial order.
		A specification of correspondences at point landmarks can be extended to produce a deformation field for the full volume in a variety of ways, each consistent with the displacements assigned at the point landmarks.
		Association of points on each surface with the same mesh coordinate produces a dense correspondence vector field between surface points in different subjects.
	www.loni.ucla.edu /%7Ethompson/detailed_warp2.html

	Registration of SPECT and CT Brain Images
		The Iterative Closest Point (ICP) algorithm [3] is used for the registration.
		These markers are visible in both modalities and form a set of known corresponding points from which a three dimensional transformation can be derived [1,2].
		The points defined from the CT slices are termed the base and remain fixed.
	www.scs.leeds.ac.uk /comir/research/brains/brains.html

	Dense Surface Point Distribution Models of the Human Face
		Quite often, many ICP iterations are required for the template to converge with the target - it would be inappropriate for the ASM to deform the mesh before at least an initial convergence was attained.
		The dense surface point distribution model is more sensitive than the landmark model to correlated facial characteristics such as gender, age and the presence of congenital abnormalities.
		The intention is that the amount of deformation allowed in the ASM model corresponds to the degree of convergence of the ICP fit, to prevent the template deforming to fit the surface too closely before a good overall correspondence has been established.
	www.eastman.ucl.ac.uk /~dmi/Papers/mmbia2001_html/hutton_tj.html

	Papers by F. F. Leymarie et al. on Shape
		Most practical known methods are based on the Iterative Closest Point (ICP) algorithm, which requires an initial alignment close to the globally optimal solution to ensure convergence to a valid solution.
		The second point of the paper is to propose the use of a computational framework based on the Contour-based Euclidean Distance Transform (CEDT) for shock detection, classification, labeling, as well as for simulating interpenetrating waves and multiple generation shocks described above.
		We address the problem of representing 3D shapes when partial and unorganized data is obtained as an input, such as clouds of point samples on the surface of a face, statue, solid, etc., of regular or arbitrary complexity (free-form), as is commonly produced by photogrammetry, laser scanners, computerized tomography, and so on.
	www.lems.brown.edu /vision/people/leymarie/Refs/CompVision/Skel/LeymarieF.html

	Publications - Graphics Group
		The Iterative Closest Point (ICP) algorithm is a widely used method for aligning three-dimensional point sets.
		If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data.
		In this paper, we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potential unstable transformations.
	www.cs.princeton.edu /gfx/pubs/Gelfand_2003_GSS/index.php

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Topic: Who's closest

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In the News (Tue 22 Dec 09)