shape similarity for 3d video sequences of people this paper presents a performance evaluation of shape similarity metrics for 3d video sequences of people with unknown temporal correspondence performance of similarity measures is compared by evaluating receiver operator characteristics for classification against ground truth for a comprehensive database of synthetic 3d video sequences comprising animations of fourteen people performing twenty eight motions static shape similarity metrics shape distribution spin image shape histogram and spherical harmonics are evaluated using optimal parameter settings for each approach shape histograms with volume sampling are found to consistently give the best performance for different people and motions static shape similarity is extended over time to eliminate the temporal ambiguity time filtering of the static shape similarity together with two novel shape flow descriptors are evaluated against temporal ground truth this evaluation demonstrates that shape flow with a multi frame alignment of motion sequences achieves the best performance is stable for different people and motions and overcome the ambiguity in static shape similarity time filtering of the static shape histogram similarity measure with a fixed window size achieves marginally lower performance for linear motions with the same computational cost as static shape descriptors performance of the temporal shape descriptors is validated for real 3d video sequence of nine actors performing a variety of movements time filtered shape histograms are shown to reliably identify frames from 3d video sequences with similar shape and motion for people with loose clothing and complex motion

the effect of spatial information characterization on 3d local feature descriptors designing local feature descriptors for 3d objects is a fundamental yet challenging task in 3d computer vision both geometry and spatial information descriptions are critical for a 3d local descriptor while most previous studies concentrate on the former one this paper investigates on how the characterization of spatial information would affect a 3d local descriptor in terms of descriptiveness robustness compactness and efficiency the evaluation process is deployed as follows first based on the analysis of representative spatial information characterization methods of existing local shape descriptors six typical characterization methods with different spatial dimensions and partition principles of spatial information are presented second three geometric attributes i e normal deviation local depth and shape index are respectively assigned to each point in the local surface for local geometry description creating a total of 18 different feature descriptors then a quantitative analysis of performance i e descriptiveness robustness compactness and efficiency for these descriptors is carried out on three benchmark datasets grounded on the experimental outcomes the traits merits and demerits of each spatial information encoding approach are eventually summarized this study reveals that different spatial information encoding approaches would bring significant effect on a local shape descriptor with respect to its discriminative power stability compactness and efficiency highlightsthe effect of spatial information encoding sie on 3d local descriptors is emphasized eighteen types of 3d local descriptors with different sie approaches are proposed quantitative evaluation of sie approaches is performed on three standard datasets the merits demerits and suitable applications of different sie ways are summarized instructive guidance is concluded for the design of a 3d local descriptor

knotted fields and explicit fibrations for lemniscate knots we give an explicit construction of complex maps whose nodal lines have the form of lemniscate knots we review the properties of lemniscate knots defined as closures of braids where all strands follow the same transverse 1 l lissajous figure and are therefore a subfamily of spiral knots generalizing the torus knots we then prove that such maps exist and are in fact fibrations with appropriate choices of parameters we describe how this may be useful in physics for creating knotted fields in quantum mechanics optics and generalizing to rational maps with application to the skyrme faddeev model we also prove how this construction extends to maps with weakly isolated singularities

a method for registration of 3 d shapes the authors describe a general purpose representation independent method for the accurate and computationally efficient registration of 3 d shapes including free form curves and surfaces the method handles the full six degrees of freedom and is based on the iterative closest point icp algorithm which requires only a procedure to find the closest point on a geometric entity to a given point the icp algorithm always converges monotonically to the nearest local minimum of a mean square distance metric and the rate of convergence is rapid during the first few iterations therefore given an adequate set of initial rotations and translations for a particular class of objects with a certain level of shape complexity one can globally minimize the mean square distance metric over all six degrees of freedom by testing each initial registration one important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model prior to shape inspection experimental results show the capabilities of the registration algorithm on point sets curves and surfaces

helicity and the calugareanu invariant the helicity of a localized solenoidal vector field i e the integrated scalar product of the field and its vector potential is known to be a conserved quantity under frozen field distortion of the ambient medium in this paper we present a number of results concerning the helicity of linked and knotted flux tubes particularly as regards the topological interpretation of helicity in terms of the gauss linking number and its limiting form the c lug reanu invariant the helicity of a single knotted flux tube is shown to be intimately related to the c lug reanu invariant and a new and direct derivation of this topological invariant from the invariance of helicity is given helicity is decomposed into writhe and twist contributions the writhe contribution involving the gauss integral for definition see equation 4 8 which admits interpretation in terms of the sum of signed crossings of the knot averaged over all projections part of the twist contribution is shown to be associated with the torsion of the knot and part with what may be described as intrinsic twist of the field lines in the flux tube around the knot see equations 5 13 and 5 15 the generic behaviour associated with the deformation of the knot through a configuration with points of inflexion points at which the curvature vanishes is analysed and the role of the twist parameter is discussed the derivation of the c lug reanu invariant from first principles of fluid mechanics provides a good demonstration of the relevance of fluid dynamical techniques to topological problems

smoothing and matching of 3 d space curves we present a new approach to the problem of matching 3 d curves the approach has a low algorithmic complexity in the number of models and can operate in the presence of noise and partial occlusions our method builds upon the seminal work of kishon et al 1990 where curves are first smoothed using b splines with matching based on hashing using curvature and torsion measures however we introduce two enhancements we present experimental results using synthetic data and also using characteristic curves extracted from 3 d medical images an earlier version of this article was presented at the 2nd european conference on computer vision in italy

registration of multiple range views using the reverse calibration technique abstract in this paper we propose a new registration algorithm the proposed algorithm consists of two steps the first step is to estimate the transformation parameters among multiple range views making use of the eigenvectors of the weighted covariance matrix of the 3 d coordinates of data points the weighting factors are carefully selected to take into account the projection effect caused by different viewpoints the next step is to register the views iteratively with the estimated transformation parameters as initial values to solve the correspondence problem the reverse calibration technique is used which is adapted to the space encoding range finder the object function defined by means of the reverse calibration technique is minimized iteratively experimental results show that the proposed algorithm is very fast and efficient

geometric probing of dense range data a new method is presented for the efficient and reliable pose determination of 3d objects in dense range image data the method is based upon a minimalistic geometric probing strategy that hypothesizes the intersection of the object with some selected image point and searches for additional surface data at locations relative to that point the strategy is implemented in the discrete domain as a binary decision tree classifier the tree leaf nodes represent individual voxel templates of the model with one template per distinct model pose the internal nodes represent the union of the templates of their descendant leaf nodes the union of all leaf node templates is the complete template set of the model over its discrete pose space each internal node also encodes a single voxel which is the most common element of its child node templates traversing the free is equivalent to efficiently matching the large set of templates at a selected image seed location the method was implemented and extensive experiments were conducted for a variety of combinations of tree designs and traversals under isolated cluttered and occluded scene conditions the results demonstrated a tradeoff between efficiency and reliability it was concluded that there exist combinations of tree design and traversal which are both highly efficient and reliable

consensus strategy for clustering using rc images contrary to most of the existing 3d shape clustering methods in which all the objects in a dataset must be classified in clusters in this paper we tackle an incomplete but reliable unsupervised clustering solution the central idea lies in obtaining coherent 3d shape groups using a consensus between different similarity measures which are defined in a common 3d shape representation framework our goal therefore is to extract some consistent groups of objects considering the incomplete classification if this occurs as a natural result the weighted cone curvature wcc is defined as an overall feature which synthesizes a set of curvature levels on the nodes of a standard triangular mesh representation the wcc concept is used to define a master descriptor called an rc image on which up to eight similarity measures are defined a hierarchical clustering process is then carried out for all the measures and evaluated by means of a clustering confidence measure finally a consensus between the best measures is achieved to provide a coherent group of objects the proposed clustering approach has been tested on a set of mesh models belonging to a wide variety of free shape objects yielding promising results the results of our experiments demonstrate that both the 3d shape descriptor used and the clustering strategy proposed might be useful for future developments in the unsupervised grouping field this work eliminates the imposition of a unique metric and a complete clustering rc image is an original 3d shape descriptor which represents the whole object clustering consensus between several similarity metrics under a common framework consistent clusters understanding at the expense of an incomplete classification successful test on a set of mesh models of a wide variety of free shape objects

rotational projection statistics for 3d local surface description and object recognition recognizing 3d objects in the presence of noise varying mesh resolution occlusion and clutter is a very challenging task this paper presents a novel method named rotational projection statistics rops it has three major modules local reference frame lrf definition rops feature description and 3d object recognition we propose a novel technique to define the lrf by calculating the scatter matrix of all points lying on the local surface rops feature descriptors are obtained by rotationally projecting the neighboring points of a feature point onto 2d planes and calculating a set of statistics including low order central moments and entropy of the distribution of these projected points using the proposed lrf and rops descriptor we present a hierarchical 3d object recognition algorithm the performance of the proposed lrf rops descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets our proposed techniques exhibited superior performance compared to existing techniques we also showed that our method is robust with respect to noise and varying mesh resolution our rops based algorithm achieved recognition rates of 100 98 9 95 4 and 96 0 respectively when tested on the bologna uwa queen s and ca foscari venezia datasets

3d free form surface registration and object recognition a new technique to recognise 3d free form objects via registration is proposed this technique attempts to register a free form surface represented by a set of mathtype mtef 2 1 feaafeart1ev1aaatcvaufebsjuyzl2yd9gzlbvynv2caerbulwbln hiov2dgi1btfmbaexatlxbi9gbaerbd9wdylwzybitldharqqtubsr 4rnchbgeagqivu0je9sqqrpepc0xbbl8f4rqqrffpeea0xe9lq jc9 vqaqpepm0xbba9pwe9q8fs0 yqaqpepae9pg0firpepekkfr0xfr x fr xb9adbaqaaegacigaaiaabeqaamaabaabaagcbagaagomamaala aabagaagymaaqaaiaaikdaaagaamiraaaa 38f8 2 frac 1 2 d sensed data points to the model surface represented by another set of mathtype mtef 2 1 feaafeart1ev1aaatcvaufebsjuyzl2yd9gzlbvynv2caerbulwbln hiov2dgi1btfmbaexatlxbi9gbaerbd9wdylwzybitldharqqtubsr 4rnchbgeagqivu0je9sqqrpepc0xbbl8f4rqqrffpeea0xe9lq jc9 vqaqpepm0xbba9pwe9q8fs0 yqaqpepae9pg0firpepekkfr0xfr x fr xb9adbaqaaegacigaaiaabeqaamaabaabaagcbagaagomamaala aabagaagymaaqaaiaaikdaaagaamiraaaa 38f8 2 frac 1 2 d model data points without prior knowledge of correspondence or view points between the two point sets with an initial assumption that the sensed surface be part of a more complete model surface the algorithm begins by selecting three dispersed reliable points on the sensed surface to find the three corresponding model points the method uses the principal curvatures and the darboux frames to restrict the search over the model space invariably many possible model 3 typles will be found for each hypothesized model 3 tuple the transformation to match the sensed 3 tuple to the model 3 tuple can be determined a heuristic search is proposed to single out the optimal transformation in low order time for realistic object recognition or registration where the two range images are often extracted from different view points of the model the earlier assumption that the sensed surface be part of a more complete model surface cannot be relied on with this the sensed 3 tuple must be chosen such that the three sensed points lie on the common region visible to both the sensed and model views we propose an algorithm to select a minimal non redundant set of 3 tuples such that at least one of the 3 tuples will lie on the overlap applying the previous algorithm to each 3 tuple within this set the optimal transformation can be determined experiments using data obtained from a range finder have indicated fast registration for relatively complex test cases if the optimal registrations between the sensed data candidate and each of a set of model data are found then for 3d object recognition purposes the minimal best fit error can be used as the decision rule

surface signatures an orientation independent free form surface representation scheme for the purpose of objects registration and matching this paper introduces anew free form surface representation scheme for the purpose of fast and accurate registration and matching accurate registration of surfaces is a common task in computer vision the proposed representation scheme captures the surface curvature information seen from certain points and produces images called surface signatures at these points matching signatures of different surfaces enables the recovery of the transformation parameters between these surfaces we propose using template matching to compare the signature images to enable partial matching another criterion the overlap ratio is used this representation scheme can be used as a global representation of the surface as well as a local one and performs near real time registration we show that the signature representation can be used to recover scaling transformation as well as matching objects in 3d scenes in the presence of clutter and occlusion applications presented include free form object matching multimodal medical volumes registration and dental teeth reconstruction from intraoral images

segmentation through variable order surface fitting the solution of the segmentation problem requires a mechanism for partitioning the image array into low level entities based on a model of the underlying image structure a piecewise smooth surface model for image data that possesses surface coherence properties is used to develop an algorithm that simultaneously segments a large class of images into regions of arbitrary shape and approximates image data with bivariate functions so that it is possible to compute a complete noiseless image reconstruction based on the extracted functions and regions surface curvature sign labeling provides an initial coarse image segmentation which is refined by an iterative region growing method based on variable order surface fitting experimental results show the algorithm s performance on six range images and three intensity images

registering multiview range data to create 3d computer objects concerns the problem of range image registration for the purpose of building surface models of 3d objects the registration task involves finding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these partial surfaces an integrated surface representation of the object the registration task is expressed as an optimization problem we define a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters this function computes a sum of euclidean distances from control points on one surfaces to corresponding points on the other the strength of this approach is in the method used to determine point correspondences it reverses the rangefinder calibration process resulting in equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in 3d space a stochastic optimization technique very fast simulated reannealing vfsr is used to minimize the cost function dual view registration experiments yielded excellent results in very reasonable time a multiview registration experiment took a long time a complete surface model was then constructed from the integration of multiple partial views the effectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3d objects must be constructed

on the repeatability and quality of keypoints for local feature based 3d object retrieval from cluttered scenes 3d object recognition from local features is robust to occlusions and clutter however local features must be extracted from a small set of feature rich keypoints to avoid computational complexity and ambiguous features we present an algorithm for the detection of such keypoints on 3d models and partial views of objects the keypoints are highly repeatable between partial views of an object and its complete 3d model we also propose a quality measure to rank the keypoints and select the best ones for extracting local features keypoints are identified at locations where a unique local 3d coordinate basis can be derived from the underlying surface in order to extract invariant features we also propose an automatic scale selection technique for extracting multi scale and scale invariant features to match objects at different unknown scales features are projected to a pca subspace and matched to find correspondences between a database and query object each pair of matching features gives a transformation that aligns the query and database object these transformations are clustered and the biggest cluster is used to identify the query object experiments on a public database revealed that the proposed quality measure relates correctly to the repeatability of keypoints and the multi scale features have a recognition rate of over 95 for up to 80 occluded objects

tracking by detection of 3d human shapes from surfaces to volumes 3d human shape tracking consists in fitting a template model to temporal sequences of visual observations it usually comprises an association step that finds correspondences between the model and the input data and a deformation step that fits the model to the observations given correspondences most current approaches follow the iterative closest point icp paradigm where the association step is carried out by searching for the nearest neighbors it fails when large deformations occur and errors in the association tend to propagate over time in this paper we propose a discriminative alternative for the association that leverages random forests to infer correspondences in one shot regardless the choice of shape parameterizations being surface or volumetric meshes we convert 3d shapes to volumetric distance fields and thereby design features to train the forest we investigate two ways to draw volumetric samples voxels of regular grids and cells from centroidal voronoi tessellation cvt while the former consumes considerable memory and in turn limits us to learn only subject specific correspondences the latter yields much less memory footprint by compactly tessellating the interior space of a shape with optimal discretization this facilitates the use of larger cross subject training databases generalizes to different human subjects and hence results in less overfitting and better detection the discriminative correspondences are successfully integrated to both surface and volumetric deformation frameworks that recover human shape poses which we refer to as tracking by detection of 3d human shapes it allows for large deformations and prevents tracking errors from being accumulated when combined with icp for refinement it proves to yield better accuracy in registration and more stability when tracking over time evaluations on existing datasets demonstrate the benefits with respect to the state of the art

an orientation reliability matrix for the iterative closest point algorithm this paper proposes a matrix which represents the reliability of the rotation components of the iterative closest point icp algorithm in range image registration we show that the reliability of the icp algorithm depends on the surface normal vectors of the object

shrinkage estimation in two stage adaptive designs with midtrial treatment selection we consider the problem of estimation in adaptive two stage designs with selection of a single treatment arm at an interim analysis it is well known that the standard maximum likelihood estimator of the selected treatment is biased we prove that selection bias of the maximum likelihood estimator is maximal when all treatment effects are equal and the most promising treatment is selected furthermore we consider shrinkage estimation as a solution for the selection bias problem we thereby extend previous work of hwang on lindley s estimator for single stage multi armed trials with four or more treatments and post trial treatment selection following hwang s ideas we show that a simple two stage version of lindley s estimator has uniformly smaller bayes risk than the maximum likelihood estimator when assuming an empirical bayesian framework with independent normal priors for the group means for designs that start with two or three treatment groups we suggest using a two stage version of the common estimator of the best linear unbiased predicator of the corresponding random effects model we show by an extensive simulation study that the shrinkage estimators perform well compared with maximum likelihood and previously suggested bias adjusted estimators in terms of selection bias and mean squared error

registration of surfaces minimizing error propagation for a one shot multi slit hand held scanner we propose an algorithm for the on line automatic registration of multiple 3d surfaces acquired in a sequence by a new hand held laser scanner the laser emitter is coupled with an optical lens that spreads the light forming 19 parallel slits that are projected to the scene and acquired with subpixel accuracy by a camera splines are used to interpolate the acquired profiles to increase the sample of points and delaunay triangulation is used to obtain the normal vectors at every point a point to plane pair wise registration method is proposed to align the surfaces in pairs while they are acquired conforming paths and eventually cycles that are minimized once detected the algorithm is specially designed for on line applications and can be classified as a closing the loop technique where there are not that many competing methods though it has been compared to the literature experiments providing qualitative and quantitative evaluation are shown by means of synthetic and real data and we demonstrated the reliability of our technique

a novel local surface feature for 3d object recognition under clutter and occlusion this paper presents a highly distinctive local surface feature called the trisi feature for recognizing 3d objects in the presence of clutter and occlusion for a feature point we first construct a unique and repeatable local reference frame lrf using the implicit geometrical information of neighboring triangular faces we then generate three signatures from the three orthogonal coordinate axes of the lrf these signatures are concatenated and then compressed into a trisi feature finally we propose an effective 3d object recognition algorithm based on hierarchical feature matching we tested our trisi feature on two popular datasets rigorous experimental results show that the trisi feature was highly descriptive and outperformed existing algorithms under all levels of gaussian noise laplacian noise shot noise varying mesh resolutions occlusion and clutter moreover we tested our trisi based 3d object recognition algorithm on four standard datasets the experimental results show that our algorithm achieved the best overall recognition results on these datasets

snapshots a novel local surface descriptor and matching algorithm for robust 3d surface alignment in this paper a novel local surface descriptor is proposed and applied to the problem of aligning partial views of a 3d object the descriptor is based on taking snapshots of the surface over each point using a virtual camera oriented perpendicularly to the surface this representation has the advantage of imposing minimal loss of information be robust to self occlusions and also be very efficient to compute then we describe an efficient search technique to deal with the rotation ambiguity of our representation and experimentally demonstrate the benefits of our approaches which are pronounced especially when we align views with small overlap

the combinatorics of local constraints in model based recognition and localization from sparse data the problem of recognizing what objects are where in the workspace of a robot can be cast as one of searching for a consistent matching between sensory data elements and equivalent model elements in principle this search space is enormous and to control the potential combinatorial explosion constraints between the data and model elements are needed a set of constraints for sparse sensory data that are applicable to a wide variety of sensors are derived and their characteristics are examined known bounds on the complexity of constraint satisfaction problems are used together with explicit estimates of the effectiveness of the constraints derived for the case of sparse noisy three dimensional sensory data to obtain general theoretical bounds on the number of interpretations expected to be consistent with the data it is shown that these bounds are consistent with empirical results reported previously the results are used to demonstrate the graceful degradation of the recognition technique with the presence of noise in the data and to predict the number of data points needed in general to uniquely determine the object being sensed

point signatures a new representation for 3d object recognition few systems capable of recognizing complex objects with free form sculptured surfaces have been developed the apparent lack of success is mainly due to the lack of a competent modelling scheme for representing such complex objects in this paper a new form of point representation for describing 3d free form surfaces is proposed this representation which we call the point signature serves to describe the structural neighbourhood of a point in a more complete manner than just using the 3d coordinates of the point being invariant to rotation and translation the point signature can be used directly to hypothesize the correspondence to model points with similar signatures recognition is achieved by matching the signatures of data points representing the sensed surface to the signatures of data points representing the model surface the use of point signatures is not restricted to the recognition of a single object scene to a small library of models instead it can be extended naturally to the recognition of scenes containing multiple partially overlapping objects which may also be juxtaposed with each other against a large model library no preliminary phase of segmenting the scene into the component objects is required in searching for the appropriate candidate model recognition need not proceed in a linear order which can become prohibitive for a large model library for a given scene signatures are extracted at arbitrarily spaced seed points each of these signatures is used to vote for models that contain points having similar signatures inappropriate models with low votes can be rejected while the remaining candidate models are ordered according to the votes they received in this way efficient verification of the hypothesized candidates can proceed by testing the most likely model first experiments using real data obtained from a range finder have shown fast recognition from a library of fifteen models whose complexities vary from that of simple piecewise quadric shapes to complicated face masks results from the recognition of both single object and multiple object scenes are presented

toldi an effective and robust approach for 3d local shape description abstract feature description for the 3d local shape in the presence of noise varying mesh resolutions clutter and occlusion is a quite challenging task in 3d computer vision this paper tackles the problem by proposing a new local reference frame lrf together with a novel triple orthogonal local depth images toldi representation forming the toldi method for local shape description compared with previous methods toldi manages to perform efficient distinctive and robust description for the 3d local surface simultaneously under various feature matching contexts the proposed lrf differs from many prior ones in its calculation of the z axis and x axis the z axis is calculated using the normal of the keypoint and the x axis is computed by aggregating the weighted projection vectors of the radius neighbors toldi feature descriptors are then obtained by concatenating three local depth images ldi captured from three orthogonal view planes in the lrf into feature vectors the performance of our toldi approach is rigorously evaluated on several public datasets which contain three major surface matching scenarios namely shape retrieval object recognition and 3d registration experimental results and comparisons with the state of the arts validate the effectiveness robustness high efficiency and overall superiority of our method our method is also applied to aligning 3d object and indoor scene point clouds obtained by different devices i e lidar and kinect the accurate outcomes further confirm the effectiveness of our method

a performance evaluation of local descriptors in this paper we compare the performance of descriptors computed for local interest regions as for example extracted by the harris affine detector mikolajczyk k and schmid c 2004 many different descriptors have been proposed in the literature it is unclear which descriptors are more appropriate and how their performance depends on the interest region detector the descriptors should be distinctive and at the same time robust to changes in viewing conditions as well as to errors of the detector our evaluation uses as criterion recall with respect to precision and is carried out for different image transformations we compare shape context belongie s et al april 2002 steerable filters freeman w and adelson e setp 1991 pca sift ke y and sukthankar r 2004 differential invariants koenderink j and van doorn a 1987 spin images lazebnik s et al 2003 sift lowe d g 1999 complex filters schaffalitzky f and zisserman a 2002 moment invariants van gool l et al 1996 and cross correlation for different types of interest regions we also propose an extension of the sift descriptor and show that it outperforms the original method furthermore we observe that the ranking of the descriptors is mostly independent of the interest region detector and that the sift based descriptors perform best moments and steerable filters show the best performance among the low dimensional descriptors

singular filaments organize chemical waves in three dimensions iv wave taxonomy abstract this is the last of four papers on the anatomy of three dimensional organizing centers in excitable media these organizing centers are made of scroll ring waves with twists and knots in them diversely linked together each such aggregate of linked rings is characterized by a handful of topological quantum numbers which partition the family of organizing centers into discrete classes or atoms we derive the rules which govern nuclear structure in this periodic table as well as the selection rules for transmutation

dense 3d face correspondence we present an algorithm that automatically establishes dense correspondences between a large number of 3d faces starting from automatically detected sparse correspondences on the outer boundary of 3d faces the algorithm triangulates existing correspondences and expands them iteratively by matching points of distinctive surface curvature along the triangle edges after exhausting keypoint matches further correspondences are established by generating evenly distributed points within triangles by evolving level set geodesic curves from the centroids of large triangles a deformable model k3dm is constructed from the dense corresponded faces and an algorithm is proposed for morphing the k3dm to fit unseen faces this algorithm iterates between rigid alignment of an unseen face followed by regularized morphing of the deformable model we have extensively evaluated the proposed algorithms on synthetic data and real 3d faces from the frgcv2 bosphorus bu3dfe and und ear databases using quantitative and qualitative benchmarks our algorithm achieved dense correspondences with a mean localisation error of 1 28 mm on synthetic faces and detected 14 anthropometric landmarks on unseen real faces from the frgcv2 database with 3 mm precision furthermore our deformable model fitting algorithm achieved 98 5 percent face recognition accuracy on the frgcv2 and 98 6 percent on bosphorus database our dense model is also able to generalize to unseen datasets

knotted and linked phase singularities in monochromatic waves exact solutions of the helmholtz equation are constructed possessing wavefront dislocation lines phase singularities in the form of knots or links where the wave function vanishes knotted nothings the construction proceeds by making a nongeneric structure with a strength n dislocation loop threaded by a strength m dislocation line and then perturbing this in the resulting unfolded stable structure the dislocation loop becomes an m n torus knot if m and n are coprime and n linked rings or knots if m and n have a common factor n the loop or rings are threaded by an m stranded helix in our explicit implementation the wave is a superposition of bessel beams accessible to experiment paraxially the construction fails

using spin images for efficient object recognition in cluttered 3d scenes we present a 3d shape based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion recognition is based on matching surfaces by matching points using the spin image representation the spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes we present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models furthermore we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes

pairwise matching of 3d fragments using cluster trees we propose a novel and efficient surface matching approach for reassembling broken solids as well as for matching assembly components using cluster trees of oriented points the method rapidly scans through the space of all possible contact poses of the fragments to be re assembled using a tree search strategy which neither relies on any surface features nor requires an initial solution the new method first decomposes each point set into a binary tree structure using a hierarchical clustering algorithm subsequently the fragments are matched pairwise by descending the cluster trees simultaneously in a depth first fashion in contrast to the reassemblage of pottery and thin walled artifacts this paper addresses the problem of matching broken 3d solids on the basis of their 2 5d fracture surfaces which are assumed to be reasonable large our proposed contact area maximization is a powerful common basis for most surface matching tasks which can be adapted to numerous special applications the suggested approach is very robust and offers an outstanding efficiency

a generalized dunnett test for multi arm multi stage clinical studies with treatment selection we generalize the dunnett test to derive efficacy and futility boundaries for a flexible multi arm multi stage clinical trial for a normally distributed endpoint with known variance we show that the boundaries control the familywise error rate in the strong sense the method is applicable for any number of treatment arms number of stages and number of patients per treatment per stage it can be used for a wide variety of boundary types or rules derived from spending functions additionally we show how sample size can be computed under a least favourable configuration power requirement and derive formulae for expected sample sizes

spatial evolution of cylindrical beams in complex media the spatial evolution of vector electromagnetic fields in bianisotropic cylindrically symmetric structures is studied we introduce evolution operators characteristic matrices impedance tensors reflection and transmission operators of cylindrical beams which are convenient for describing waves in layered media we predict the existence of fractional bessel beams with integer topological charge and cylindrically symmetric beams which can be presented as the product of the bessel function and the exponent we investigate energy and polarization characteristics of such beams

a 3d scene registration method via covariance descriptors and an evolutionary stable strategy game theory solver in this paper we provide an integrated approach for matching patterns in scenes combining 3d and visual information for local definition of points we propose a descriptor based on the notion of covariance of features for fusion of shape and color information of 3d surfaces so called multi scale covariance descriptor mcov the intrinsic properties of this descriptor are many it is invariant to spatial rigid transformations and robust to noise and resolution changes it can also be used for characteristic point detection and lies on top of a manifold topology which allows the use of analytical metric properties this descriptor is complemented with a game theoretic approach for solving the matching correspondences under global geometric constraints this layer offers a comprehensive understanding of the scene and avoids possible mismatches due to repeated areas or symmetries which would be impossibly identified by the detector solely at a local level our solution is able to accurately match different views of a scene even under spatial transformations high noise levels and with small overlap between views outperforming state of the art approaches results are validated by comparing mcov against other state of the art 3d point descriptor methods and matching complex 3d and color scenes under several challenging conditions

registration of 3d points using geometric algebra and tensor voting we address the problem of finding the correspondences of two point sets in 3d undergoing a rigid transformation using these correspondences the motion between the two sets can be computed to perform registration our approach is based on the analysis of the rigid motion equations as expressed in the geometric algebra framework through this analysis it was apparent that this problem could be cast into a problem of finding a certain 3d plane in a different space that satisfies certain geometric constraints in order to find this plane in a robust way the tensor voting methodology was used unlike other common algorithms for point registration like the iterated closest points algorithm ours does not require an initialization works equally well with small and large transformations it cannot be trapped in local minima and works even in the presence of large amounts of outliers we also show that this algorithm is easily extended to account for multiple motions and certain non rigid or elastic transformations

interval estimation for drop the losers designs summary in the first stage of a two stage drop the losers design a candidate for the best treatment is selected at the second stage additional observations are collected to decide whether the candidate is actually better than the control the design also allows the investigator to stop the trial for ethical reasons at the end of the first stage if there is already strong evidence of futility or superiority two types of tests have recently been developed one based on the combined means and the other based on the combined p values but corresponding interval estimators are unavailable except in special cases the problem is that in most cases the interval estimators depend on the mean configuration of all treatments in the first stage which is unknown in this paper we prove a basic stochastic ordering lemma that enables us to bridge the gap between hypothesis testing and interval estimation the proposed confidence intervals achieve the nominal confidence level in certain special cases simulations show that decisions based on our intervals are usually more powerful than those based on existing methods

point estimation in adaptive enrichment designs adaptive enrichment designs are an attractive option for clinical trials that aim at demonstrating efficacy of therapies which may show different benefit for the full patient population and a prespecified subgroup in these designs based on interim data either the subgroup or the full population is selected for further exploration when selection is based on efficacy data this introduces bias to the commonly used maximum likelihood estimator for the situation of two stage designs with a single prespecified subgroup we present six alternative estimators and investigate their performance in a simulation study the most consistent reduction of bias over the range of scenarios considered was achieved by a method combining the uniformly minimum variance conditionally unbiased estimator with a conditional moment estimator application of the methods is illustrated by a clinical trial example

curved boundary corrections to nodal line statistics in chaotic billiards a gaussian random wavefunction that satisfies dirichlet and neumann conditions locally on a convex circular boundary is introduced the average of the square of the wavefunction and its derivatives are computed and their asymptotics studied in the semi classical limit the mean nodal line length l is calculated and the first order boundary effect shown to be of order log k where k is the wavenumber in the limit of vanishing boundary curvature large boundary radius these results are shown to approach those for a straight wall boundary

real time object recognition in sparse range images using error surface embedding a novel object recognition algorithm is introduced to identify objects and recover their pose from sparse range data the method is based upon comparing the 7 d error surfaces of objects in various poses which result from the registration error function between two convolved surfaces the objects and their pose values are encoded by a small set of feature vectors extracted from the minima of the error surfaces the problem of object recognition is thus reduced to comparing these feature vectors to find the corresponding error surfaces between the runtime data and a preprocessed database the algorithm called potential well space embedding pwse has been implemented and tested on both simulated and real data the experimental results show the technique to be both effective and efficient executing at 122 frames per second on standard hardware and with recognition rates exceeding 97 for a database of 60 objects the performance of pwse on the large size database was also evaluated on the princeton shape benchmark containing 1 814 objects in addition it functions well with very sparse data possibly comprising only hundreds of points per image and is shown to be robust to measurement error and outliers with some small modifications we applied pwse to the problem of object class recognition in experiments with the princeton shape benchmark pwse is able to provides better classification rates than the previous methods in terms of nearest neighbour classification

statistics of nodal points of in plane random waves in elastic media we consider the nodal points nps u 0 and v 0 of the in plane vectorial displacements u u v which obey the navier cauchy equation similar to the berry conjecture of quantum chaos we present the in plane eigenstates of chaotic billiards as the real part of the superposition of longitudinal and transverse plane waves with random phases by an average over random phases we derive the mean density and correlation function of nps consequently we consider the distribution of the nearest distances between nps

empirical bayes estimation of the selected treatment mean for two stage drop the loser trials a meta analytic approach point estimation for the selected treatment in a two stage drop the loser trial is not straightforward because a substantial bias can be induced in the standard maximum likelihood estimate mle through the first stage selection process research has generally focused on alternative estimation strategies that apply a bias correction to the mle however such estimators can have a large mean squared error carreras and brannath stat med 32 1677 90 have recently proposed using a special form of shrinkage estimation in this context given certain assumptions their estimator is shown to dominate the mle in terms of mean squared error loss which provides a very powerful argument for its use in practice in this paper we suggest the use of a more general form of shrinkage estimation in drop the loser trials that has parallels with model fitting in the area of meta analysis several estimators are identified and are shown to perform favourably to carreras and brannath s original estimator and the mle however they necessitate either explicit estimation of an additional parameter measuring the heterogeneity between treatment effects or a quite unnatural prior distribution for the treatment effects that can only be specified after the first stage data has been observed shrinkage methods are a powerful tool for accurately quantifying treatment effects in multi arm clinical trials and further research is needed to understand how to maximise their utility

iterative point matching for registration of free form curves and surfaces a heuristic method has been developed for registering two sets of 3 d curves obtained by using an edge based stereo system or two dense 3 d maps obtained by using a correlation based stereo system geometric matching in general is a difficult unsolved problem in computer vision fortunately in many practical applications some a priori knowledge exists which considerably simplifies the problem in visual navigation for example the motion between successive positions is usually approximately known from this initial estimate our algorithm computes observer motion with very good precision which is required for environment modeling e g building a digital elevation map objects are represented by a set of 3 d points which are considered as the samples of a surface no constraint is imposed on the form of the objects the proposed algorithm is based on iteratively matching points in one set to the closest points in the other a statistical method based on the distance distribution is used to deal with outliers occlusion appearance and disappearance which allows us to do subset subset matching a least squares technique is used to estimate 3 d motion from the point correspondences which reduces the average distance between points in the two sets both synthetic and real data have been used to test the algorithm and the results show that it is efficient and robust and yields an accurate motion estimate

an efficient multimodal 2d 3d hybrid approach to automatic face recognition we present a fully automatic face recognition algorithm and demonstrate its performance on the frgc v2 0 data our algorithm is multimodal 2d and 3d and performs hybrid feature based and holistic matching in order to achieve efficiency and robustness to facial expressions the pose of a 3d face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the hotelling transform a novel 3d spherical face representation sfr is used in conjunction with the scale invariant feature transform sift descriptor to form a rejection classifier which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries the remaining faces are then verified using a novel region based matching approach which is robust to facial expressions this approach automatically segments the eyes forehead and the nose regions which are relatively less sensitive to expressions and matches them separately using a modified iterative closest point icp algorithm the results of all the matching engines are fused at the metric level to achieve higher accuracy we use the frgc benchmark to compare our results to other algorithms that used the same database our multimodal hybrid algorithm performed better than others by achieving 99 74 percent and 98 31 percent verification rates at a 0 001 false acceptance rate far and identification rates of 99 02 percent and 95 37 percent for probes with a neutral and a nonneutral expression respectively

a fast and robust local descriptor for 3d point cloud registration this paper proposes a novel local feature descriptor called a local feature statistics histogram lfsh for efficient 3d point cloud registration an lfsh forms a comprehensive description of local shape geometries by encoding their statistical properties on local depth point density and angles between normals the sub features in the lfsh descriptor are low dimensional and quite efficient to compute in addition an optimized sample consensus osac algorithm is developed to iteratively estimate the optimum transformation from point correspondences osac can handle the challenging cases of matching highly self similar models based on the proposed lfsh and osac a coarse to fine algorithm can be formed for 3d point cloud registration experiments and comparisons with the state of the art descriptors demonstrate that lfsh is highly discriminative robust and significantly faster than other descriptors meanwhile the proposed coarse to fine registration algorithm is demonstrated to be robust to common nuisances including noise and varying point cloud resolutions and can achieve high accuracy on both model data and scene data

replicator dynamics in the iterative process for accurate range image matching iterative algorithms are often used for range image matching in this paper we treat the iterative process of range image matching as a live biological system evolving from one generation to another whilst different generations of the population are regarded as range images captured at different viewpoints the iterative process is simulated using time the well known replicator equations in theoretical biology are then adapted to estimate the probabilities of possible correspondences established using the traditional closest point criterion to reduce the effect of image resolutions on the final results for efficient and robust overlapping range image matching the relative fitness difference rather than the absolute fitness difference is employed in the replicator equations in order to model the probability change of possible correspondences being real over successive iterations the fitness of a possible correspondence is defined as the negative of a power of its squared euclidean distance while the replicator dynamics penalize those individuals with low fitness they are further penalised with a parameter since distant points are often unlikely to represent their real replicators while the replicator equations assume that all individuals are equally likely to meet each other and thus treat them equally we penalise those individuals competing for the same points as their possible replicators the estimated probabilities of possible correspondences being real are finally embedded into the powerful deterministic annealing scheme for global optimization resulting in the camera motion parameters being estimated in the weighted least squares sense a comparative study based on real range images with partial overlap has shown that the proposed algorithm is promising for automatic matching of overlapping range images

keypoints based surface representation for 3d modeling and 3d object recognition the three dimensional 3d modeling and recognition of 3d objects have been traditionally performed using local features to represent the underlying 3d surface extraction of features requires cropping of several local surface patches around detected keypoints although an important step the extraction and representation of such local patches adds to the computational complexity of the algorithms this paper proposes a novel keypoints based surface representation ksr technique the proposed technique has the following two characteristics 1 it does not rely on the computation of features on a small surface patch cropped around a detected keypoint rather it exploits the geometrical relationship between the detected 3d keypoints for local surface representation 2 ksr is computationally efficient requiring only seconds to process 3d models with over 50 000 points with a matlab implementation experimental results on the uwa and stanford 3d models dataset suggest that it can accurately perform pairwise and multiview range image registration 3d modeling ksr was also tested for 3d object recognition with occluded scenes recognition results on the uwa dataset show that the proposed technique outperforms existing methods including 3d tensor vd lsd keypoint depth based feature spherical harmonics and spin image with a recognition rate of 95 9 the proposed approach also achieves a recognition rate of 93 5 on the challenging ca fascori dataset compared to 92 5 achieved by game theoretic the proposed method is computationally efficient compared to state of the art local feature methods highlightswe propose a novel technique called keypoint based surface representation ksr the proposed technique does not require local features around detected keypoints ksr exploits geometrical relationships between keypoints for surface representation ksr is tested on 3 popular datasets for 3d modeling and 3d object recognition ksr achieves superior 3d modeling and recognition results

registration of partially overlapping surfaces by rejection of false point correspondences we present a new algorithm for the registration of three dimensional partially overlapping surfaces it is based on an efficient scheme for the rejection of false point correspondences correspondence outliers and does not require initial pose estimation or feature extraction an initial list of corresponding points is first derived using the regional properties of vertices on both surfaces from these point correspondences pairs of corresponding rigid triplets are formed the normal vectors at the vertices of each corresponding triplet are used to compute the candidate rotations by clustering the candidate rotation axes and candidate rotation angles separately a large number of false correspondences are eliminated and an approximate rotation is decided from which an approximate translation is also obtained finally the optimal transformation parameters are determined by further refining the estimated parameters in an iterative manner mathematical analysis and experimental results show that the registration process is fast and accurate even when the objects are regularly shaped and contain many regionally similar surface patches

conditional estimation in two stage adaptive designs we consider conditional estimation in two stage sample size adjustable designs and the consequent bias more specifically we consider a design which permits raising the sample size when interim results look rather promising and which retains the originally planned sample size when results look very promising the estimation procedures reported comprise the unconditional maximum likelihood the conditionally unbiased rao blackwell estimator the conditional median unbiased estimator and the conditional maximum likelihood with and without bias correction we compare these estimators based on analytical results and a simulation study we show how they can be applied in a real clinical trial setting

multi view ensemble manifold regularization for 3d object recognition view based methods are popular in 3d object recognition however current methods with traditional classifiers are usually based on one to one view matching and fail to capture the structure information of multiple views some multi view based methods take different views into consideration but they still treat views separately in this paper we propose a novel 3d object recognizing method based on multi view data fusion called multi view ensemble manifold regularization memr in this method we model image features with a regularization term for svm to train this modified svm multi view learning is achieved with alternating optimization hypergraph construction is used to better capture the connectivity among views experimental results show that the accuracy rate has been improved by 20 25 which demonstrates the effectiveness of the proposed method

least median of squares regression abstract classical least squares regression consists of minimizing the sum of the squared residuals many authors have produced more robust versions of this estimator by replacing the square by something else such as the absolute value in this article a different approach is introduced in which the sum is replaced by the median of the squared residuals the resulting estimator can resist the effect of nearly 50 of contamination in the data in the special case of simple regression it corresponds to finding the narrowest strip covering half of the observations generalizations are possible to multivariate location orthogonal regression and hypothesis testing in linear models

imposing semi local geometric constraints for accurate correspondences selection in structure from motion a game theoretic perspective most structure from motion pipelines are based on the iterative refinement of an initial batch of feature correspondences typically this is performed by selecting a set of match candidates based on their photometric similarity an initial estimate of camera intrinsic and extrinsic parameters is then computed by minimizing the reprojection error finally outliers in the initial correspondences are filtered by enforcing some global geometric property such as the epipolar constraint in the literature many different approaches have been proposed to deal with each of these three steps but almost invariably they separate the first inlier selection step which is based only on local image properties from the enforcement of global geometric consistency unfortunately these two steps are not independent since outliers can lead to inaccurate parameter estimation or even prevent convergence leading to the well known sensitivity of all filtering approaches to the number of outliers especially in the presence of structured noise which can arise for example when the images present several repeated patterns in this paper we introduce a novel stereo correspondence selection scheme that casts the problem into a game theoretic framework in order to guide the inlier selection towards a consistent subset of correspondences this is done by enforcing geometric constraints that do not depend on full knowledge of the motion parameters but rather on some semi local property that can be estimated from the local appearance of the image features the practical effectiveness of the proposed approach is confirmed by an extensive set of experiments and comparisons with state of the art techniques

submanifolds that are level sets of solutions to a second order elliptic pde abstract we consider the problem of characterizing which noncompact hypersurfaces in r n can be regular level sets of a harmonic function modulo a c diffeomorphism as well as certain generalizations to other pdes we prove a versatile sufficient condition that shows in particular that any nonsingular algebraic hypersurface whose connected components are all noncompact can be transformed onto a union of components of the zero set of a harmonic function via a diffeomorphism of r n the technique we use combines robust but not explicit local constructions with appropriate global approximation theorems in view of applications to a problem posed by berry and dennis intersections of level sets are also studied

fast and accurate surface alignment through an isometry enforcing game surface registration is often performed as a two step process a feature matching scheme is first adopted to find a coarse initial alignment between two meshes subsequently a refinement step which usually operates in the space of rigid motions is applied to reach an optimal registration with respect to pointwise distances between overlapping areas in this paper we propose a novel technique that allows to obtain an accurate surface registration in a single step without the need for an initial motion estimation the main idea of our approach is to cast the selection of correspondences between points on the surfaces in a game theoretic framework where a natural selection process allows matching points that satisfy a mutual rigidity constraint to thrive eliminating all the other correspondences this process yields a very robust inlier selection scheme that does not depend on any particular technique for selecting the initial strategies as it relies only on the global geometric compatibility between correspondences the practical effectiveness of the approach is confirmed by an extensive set of experiments and comparisons with state of the art techniques highlightswe introduce a novel pipeline for rigid surface alignment based on game theory the method allows to obtain accurate solutions without requiring an initial estimate we propose simple descriptors designed to be fast and effective within our pipeline we establish a relation between evolutionary stable states and optimal alignments the technique is efficient and outperforms the state of the art

structural indexing efficient 3 d object recognition the authors present an approach for the recognition of multiple 3 d object models from three 3 d scene data the approach uses two different types of primitives for matching small surface patches where differential properties can be reliably computed and lines corresponding to depth or orientation discontinuities these are represented by splashes and 3 d curves respectively it is shown how both of these primitives can be encoded by a set of super segments consisting of connected linear segments these super segments are entered into a table and provide the essential mechanism for fast retrieval and matching the issues of robustness and stability of the features are addressed in detail the acquisition of the 3 d models is performed automatically by computing splashes in highly structured areas of the objects and by using boundary and surface edges for the generation of 3 d curves the authors present results with the current system 3 d object recognition based on super segments and discuss further extensions

3d geometric scale variability in range images features and descriptors despite their ubiquitous presence little has been investigated about the scale variability the relative variations in the spatial extents of local structures of 3d geometric data in this paper we present a comprehensive framework for exploiting this 3d geometric scale variability in range images that provides rich information for characterizing the overall geometry we derive a sound scale space representation which we refer to as the geometric scale space that faithfully encodes the scale variability of the surface geometry and derive novel detectors to extract prominent features and identify their natural scales the result is a hierarchical set of features of different scales which we refer to as scale dependent geometric features we then derive novel local shape descriptors that represent the surface structures that give rise to those features by carving out and encoding the local surface that fall within the support regions of the features this leads to scale dependent or scale invariant local shape descriptors that convey significant discriminative information of the object geometry we demonstrate the effectiveness of geometric scale analysis on range images and show that it enables novel applications in particular fully automatic registration of multiple objects from a mixed set of range images and 3d object recognition in highly cluttered range image scenes

accurately estimating rigid transformations in registration using a boosting inspired mechanism feature extraction and matching provide the basis of many methods for object registration modeling retrieval and recognition however this approach typically introduces false matches due to lack of features noise occlusion and cluttered backgrounds in registration these false matches lead to inaccurate estimation of the underlying transformation that brings the overlapping shapes into best possible alignment in this paper we propose a novel boosting inspired method to tackle this challenging task it includes three key steps i underlying transformation estimation in the weighted least squares sense ii boosting parameter estimation and regularization via tsallis entropy and iii weight re estimation and regularization via shannon entropy and update with a maximum fusion rule the process is iterated the final optimal underlying transformation is estimated as a weighted average of the transformations estimated from the latest iterations with weights given by the boosting parameters a comparative study based on real shape data shows that the proposed method outperforms four other state of the art methods for evaluating the established point matches enabling more accurate and stable estimation of the underlying transformation highlightswe provide a novel method for evaluating point matches established by typical methods real data based experimental results show that the method outperforms the state of the art under certain conditions the finally estimated underlying transformation is optimal the transformation can be recovered with an error as small as 5 from typical point matches

three dimensional model based object recognition and segmentation in cluttered scenes viewpoint independent recognition of free form objects and their segmentation in the presence of clutter and occlusions is a challenging task we present a novel 3d model based algorithm which performs this task automatically and efficiently a 3d model of an object is automatically constructed offline from its multiple unordered range images views these views are converted into multidimensional table representations which we refer to as tensors correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table based voting scheme this results in a graph of relative transformations used to register the views before they are integrated into a seamless 3d model these models and their tensor representations constitute the model library during online recognition a tensor from the scene is simultaneously matched with those in the library by casting votes similarity measures are calculated for the model tensors which receive the most votes the model with the highest similarity is transformed to the scene and if it aligns accurately with an object in the scene that object is declared as recognized and is segmented this process is repeated until the scene is completely segmented experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency

from 3d point clouds to pose normalised depth maps we consider the problem of generating either pairwise aligned or pose normalised depth maps from noisy 3d point clouds in a relatively unrestricted poses our system is deployed in a 3d face alignment application and consists of the following four stages i data filtering ii nose tip identification and sub vertex localisation iii computation of the relative face orientation iv generation of either a pose aligned or a pose normalised depth map we generate an implicit radial basis function rbf model of the facial surface and this is employed within all four stages of the process for example in stage ii construction of novel invariant features is based on sampling this rbf over a set of concentric spheres to give a spherically sampled rbf ssr shape histogram in stage iii a second novel descriptor called an isoradius contour curvature signal is defined which allows rotational alignment to be determined using a simple process of 1d correlation we test our system on both the university of york uoy 3d face dataset and the face recognition grand challenge frgc 3d data for the more challenging uoy data our ssr descriptors significantly outperform three variants of spin images successfully identifying nose vertices at a rate of 99 6 nose localisation performance on the higher quality frgc data which has only small pose variations is 99 9 our best system successfully normalises the pose of 3d faces at rates of 99 1 uoy data and 99 6 frgc data

a novel representation and feature matching algorithm for automatic pairwise registration of range images automatic registration of range images is a fundamental problem in 3d modeling of free from objects various feature matching algorithms have been proposed for this purpose however these algorithms suffer from various limitations mainly related to their applicability efficiency robustness to resolution and the discriminating capability of the used feature representation we present a novel feature matching algorithm for automatic pairwise registration of range images which overcomes these limitations our algorithm uses a novel tensor representation which represents semi local 3d surface patches of a range image by third order tensors multiple tensors are used to represent each range image tensors of two range images are matched to identify correspondences between them correspondences are verified and then used for pairwise registration of the range images experimental results show that our algorithm is accurate and efficient moreover it is robust to the resolution of the range images the number of tensors per view the required amount of overlap and noise comparisons with the spin image representation revealed that our representation has more discriminating capabilities and performs better at a low resolution of the range images

automatic registration for 3d shapes using hybrid dimensionality reduction shape descriptions automatic registration for 3d shapes is an attractive problem in computer vision various registration algorithms based on different surface representations have been developed for this topic however most of the existing algorithms suffer from some limitations mainly related to discriminating similarity metric partially overlapping data and the robustness to resolution noise and occlusion in this research hybrid dimensionality reduction shape descriptions drsd are proposed for pair wise registration which aims to overcome these limitations based on recently emerging angle preserving parameterization techniques such as harmonic maps and abf 3d shapes are described in low dimension space with both local and global considerations therefore searching for correspondences verifying overlapping regions and calculating registration error all can be implemented in low dimension space moreover a large number of experiments using both real and synthetic images have been carried out to show the accuracy efficiency and robustness of the hybrid drsd algorithm