waashk/dblp huggingface dataset:
art extension for description, indexing and retrieval of 3d objects. this paper presents a new three-dimensional shape descriptor: 3d angular radial transform. it is an extension of the 2d region based shape descriptor proposed by mpeg-7, the angular radial transform (art).we propose to generalize the art to index 3d models.
a new approach for the registration of images with inconsistent differences. in this paper, we focus on the image registration problem. mathematically, this problem consists of minimizing an energy which is composed of a regularization term and a similarity term. the similarity term, which depends on image intensities, has to be chosen according to the nature of image grey-level dependencies. its adequacy always depends on the validity of some assumptions about these dependencies. but, in medical applications, there are many situations where these assumptions are not confirmed. in particular, intensity variations caused by observed pathologies may not be consistent with assumptions. such variations may distort registration constraints and cause registration errors. in order to cope with this problem, we propose a new approach which takes into account possible inconsistencies in the computation of registration constraints. this approach is described from two different points of view. first, we formulate a new minimization problem with an extra unknown which measures the degree of inconsistency on each pixel. then, we show that this problem is equivalent to another one which can be related to usual ones. we also outline several ways to generalize our approach and propose an algorithm to solve numerically problems. finally, we illustrate on synthetic data some characteristics of the algorithmwhen dealing with inconsistent image differences.
local fisher embedding. in recent work, several supervised spectral embedding procedures have been proposed. although experimentally validated, the construction of these algorithms was rather ad hoc. this paper shows how supervised locally linear embedding can be seen as performing a local fisher mapping. a new formulation, combining linear and fisher embedding, is proposed and experimentally validated.
curvature estimation of surfaces in 3d grey-value images. in this paper we present a novel method to estimate curvature of iso grey-level surfaces in grey-value images. our method succeeds where isophote curvature fails. there is neither a segmentation of the surface needed nor a parametric model assumed. our estimator works on the orientation (normal vector) field of the surface. this orientation field and a description of local structure is obtained by the gradient structure tensor. the estimated orientation field has discontinuities mod ¡. it is mapped via the knutsson mapping to a continuous representation. the principal curvatures of the surface, a coordinate invariant property, arecomputed in this mapped representation. an evaluation shows that our curvature estimation is robust even in the presence of noise, independent of the scale of the object and furthermore the relative error stays small.
fundamental matrix estimation via tip - transfer of invariant parameters. the fundamental matrix (fm) represents the perspective transform between two or more uncalibrated images of a stationary scene, and is traditionally estimated based on 2- parameter point-to-point correspondences between image pairs. recent invariant correspondence techniques however, provide robust correspondences in terms of 4 to 6- parameter invariant regions. such correspondences contain important information regarding scene geometry, information which is lost in fm estimation techniques based solely on 2-parameter point translation. in this article, we present a method of incorporating this additional information into point-based fm estimation routines, entitled tip (transfer of invariant parameters). the tip method transforms invariant correspondence parameters into additional point correspondences, which can be used with fm estimation routines. experimentation shows that the tip methods result in more robust fm estimates in the case of sparse correspondence, and allows estimation based on as few as 3 correspondences in the case of affine-invariant features.
feature extraction from micrographs of forged nickel based alloy. various algorithms for the automatic extraction of features from micrographs of forged inconel 718 tm will be presented in this paper. this includes the extraction of an optimized boundary image, the elimination of scratches and parallel lines ("twins") and from subsequent evaluation and the detection of ä phase particles. requested features are grain size, the amount and distribution of ä phase with respect to grain boundaries and anisotropic effects.
multi-objective evolutionary clustering using variable-length real jumping genes genetic algorithm. in this paper, we present a novel multi-objective evolutionary clustering approach using variable-length real jumping genes genetic algorithms (vrjgga). the proposed algorithm that extends jumping genes genetic algorithm (jgga) [1] evolves near-optimal clustering solutions using multiple clustering criteria, without apriori knowledge of the actual number of clusters. experimental results based on several artificial and realworld data show that vrjgga can obtain non-dominated and near-optimal clustering solutions in terms of different cluster quality measures and classification performance.
the twin towers cluster in torah codes. in this paper we describe a torah code experiment which: - focuses on a famous contemporary event. - has a simple and explicit data collection. - has considerably simpler measurements than in previous torah code experiments.the pattern we study here is a cluster of related key words in the form of elss in a single table. the cluster that we discover contains a subset of all key words obtained in the data collection. we account for the choices needed to determine this subset using the bonferroni inequality. our protocol involves some other explicit choices, accounted for in a similar way. the final p-value is less than 5.14×10-5.
action spaces for efficient bayesian tracking of human motion. bayesian tracking implemented as a particle filter is one of the most used techniques for full-body human tracking. however, given the high-dimensionality of the models to be tracked, the number of required particles to properly populate the space of solutions make the problem computationally very expensive. to overcome this, we present an efficient scheme which makes use of an action model that guides the prediction step of the particle filter. in this manner, particles are propagated to locations in the search space with most a posteriori information. hence, we sample from a smooth motion model only those postures which are feasible given a particular action. we show, that this scheme improves the efficiency and accuracy of the overall tracking approach.
non-rigid registration and geometric approach for tracking in neurosurgery. registration of points obtained from preoperative 3d model and points of intraopertive stereo 3d representation, as well as tracking of surgical devices are two important stages in computer aided surgery (cas). this work shows the application and comparison of two different methods (rpm and icp) in the registration process of patient's head markers, as well as the use of geometric methods to track the surgical device in real time.
non-rigid alignment and real-time tracking using the geometric algebra framework. this paper presents two applications of geometric algebras: the main is a variant of the thin-plate - robust point matching algorithm, named by us "robust sphere matching", to register 3d objects modeled as sets of spheres using the representation of such spheres in the conformal geometric algebra. by this way, we take advantage and combine the geometric interpretation of entities of such algebra with the statistical methods used in the algorithm. in the experimental part, examples using synthetic and real data are presented to show how the algorithm works reliably, aligning volumetric models based on spheres. the second applications is the real time tracking of devices.
texture feature extraction and indexing by hermite filters. we present a texture feature extraction for image indexing and retrieval based on gabor-like hermite filters. these ones satisfy a frequency constraint of steered discrete hermite filters, which form a local orthogonal basis and agree with the gaussian derivative model of the human visual system. fast implementation of such filters is performed by a normalized recurrence relation of their discrete representation, the krawtchouk filters. in order to achieve dimensionality reduction for texture image indexing purposes, we apply a compact parametric texture model, which corresponds to the spatial autocorrelation of each subband output. experimental results obtained from a texture image database are also presented.
a real-time system for classification of moving objects. the paper describes a system for moving object classification. being restricted by real-time system constraints we found a small set of features, characterizing object shape and motion dynamics. the system was tested on a large movies database including more than 100 images sequences showing people, animals, vehicles and plants in motion. the svm classifier [1] was used in our system, yielding very good classification results.
part-based multi-frame registration for estimation of the growth of cellular networks in plant roots. motion estimation from confocal scanning laser microscope images of growing plant cell structures presents interesting challenges; motion exhibits multiple local discontinuities and noise is non-isotropic and non-gaussian. a method is presented for estimating motion of cell networks based on a physically motivated, part-based model of cell boundary structure. each part models the shape and appearance of a localised image region and can undergo constrained non-rigid deformation. this enables motion discontinuities between parts to be modelled. parts are coupled in order to improve localisation and increase computational efficiency. results from applying mcmc show accurate localisation of the structure across multiple frames. the form of the model assists biologists in interpreting growth.
online appearance learning or 3d articulated human tracking. a human appearance modelling framework where colour distributions are associated with surface regions on an articulated body model is presented. in general, these distributions are unknown, multi-modal and changing in time. we therefore propose using recursively updated histograms to represent them. for a certain pose, a set of histograms may be collected and a likelihood constructed based on the histograms' similarity with the previously learned histograms. to ease histogram estimation and improve computational efficiency, a merging and splitting algorithm is derived which groups surface regions based upon histogram similarity and prior knowledge of clothing layout. an investigation of the behaviour of this likelihood shows it to be broad, smooth and peaked around the correct location, a good candidate for coarse sampling and gradient-based search methods. we show how conditioning the likelihood to maximise foreground usage reduces secondary maxima. finally, we present results from tracking a challenging sequence.
optimal detection of blurred edges. i develop a variational model for the detection of blurred edges. though yielding a complicated general solution, the model gives insight into edge detection models previously proposed for step edges. in particular it shows that a previous iir solution for step edges is correct even in the fir case, and suggests why operators approximating the first derivative of a gaussian are successful, despite their suboptimality for step edge detection. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
a graph-spectral approach to surface segmentatio. in this paper we describe a graph-spectral method for 3d surface segmentation from 2d imagery. the method locates patches by finding groups of pixels that can be connected using a curvature minimising path. the path is the steady state markov chain on transition probability matrix. we provide two methods for computing this matrix. the fir st uses information provided by the field of surface normals extracted from the 2d intensity image using shape-from-shading. here we compute the elements of the transition matrix using the change in surface normal directions to estimate the normal curvature. the second approachuses the raw image brightness together with a lambertian reflectance model to make estimates of curvature. we compare the surface segmentations delivered by these two methods with those obtained using shape-index maximal patches.
vector field smoothing via heat flow. in this paper, we develop a new method for recovering and smoothing fields of surface normals in shape-from-shading. we show how transform the problem of recovering surface normals that satisfy lmbeert's law into that of solving the steady state heat equation for a scalar potential. according to this picture, the smoothed field of surface normals is found by taking the gradient of the scalar field. the heat equation for the scalar field can be solved using simple finite difference methods, and leads to an iterative procedure for vector estimation. we illustrate the utility of the new method on real world imagery and compare our results with those delivered by an alternative.
pollen classification using brightness-based and shape-based descriptors. pollen grain classification have recently received more attention from computer vision researchers. to distinguish among taxa, palynologist make direct use of keys such as the size, exine structure and sculpture of the pollen grains. we propose a framework in which the pollen grains of each taxa are characterized using brightness and shape descriptors derived from their intensity images. these descriptors are associated to the ornamentation and morphology of the pollen grain. the method is statistically evaluated on preparations containing species of the urticaceae family.
range image segmentation based on split-merge clustering. in this paper, we present a split-merge clustering segmentation algorithm based on gaussian mixture models, which resolves the models by expectation-maximization (em) algorithm and seeks model via bayesian information criterion (bic). it starts iteratively splitting from a single gaussian model, then iteratively merging clusters. after convergence of the last stage, the clustering model is selected via a modified bic and used to gain an initial segmentation, followed by a region merge step to achieve final segmentation. new algorithm was applied to 60 range images acquired by two kinds of range cameras, and got approving results with acceptable computation time.
augmenting fast stereo with silhouette constraints for dynamic 3d capture. we propose and evaluate a method that combines a visual hull with multi-view stereo for the fast acquisition of dynamic 3d data. both use the same set of cameras. the focus lies on high speed, as 3d has to be generated repeatedly, based on the output of multiple video cameras. the underlying principle is to bootstrap the system with quickly computed sparse stereo, constrained by silhouette information. we further show how this integrates with a fast plane sweeping algorithm, as our dense stereo algorithm of choice.
gesture detection in low-quality video. the spotting and recognition of the human gestures is a key task in automating the analysis of the video material and human-robot interaction. specially, applying this technology to low-resolution video has many potential applications. the human area is small with respect to input video frames in broadcast sports video, surveillance video, etc. however, this condition makes the spotting certain gesture in a video sequence a challenging task, especially if there is large camera motion. to overcome the problems, we propose a posture matching method based on curvature scale space templates of the human silhouette. we also propose a new recognition method which is robust to noisy sequences of data.
volume motion template for view-invariant gesture recognition. the representation of gestures changes dynamically, depending on camera viewpoints. this camera viewpoints problem is difficult to solve in environments with a single directional camera, since the shape and motion information for representing gestures is different at different viewpoints. in view-based methods, data for each viewpoint is required, which is ineffective and ambiguous in recognizing gestures. in this paper, we propose a volume motion template (vmt) to overcome the viewpoint problem in a single-directional stereo camera environment. the vmt represents motion information in 3d space using disparity maps. motion orientation is determined with 3d motion information. the projection of vmt at the optimal virtual viewpoint can be obtained by motion orientation. the proposed method is not only independent of variations of viewpoints, but also can represent depth motion. the proposed method has been evaluated in view-invariant representation and recognition using the gesture sequences which include parallel motion in an optical axis. the experimental results demonstrated the effectiveness of the proposed vmt for view-invariant gesture recognition.
automatic adjacency grammar generation from user drawn sketches. in this paper we present an innovative approach to automatically generate adjacency grammars describing graphical symbols. a grammar production is formulated in terms of rulesets of geometrical constraints among symbol primitives. given a set of symbol instances sketched by a user using a digital pen, our approach infers the grammar productions consisting of the ruleset most likely to occur. the performance of our work is evaluated using a comprehensive benchmarking database of on-line symbols.
smoothing with active surfaces: a multiphase level set approach. in this paper, we propose to use an active contour method to attract active surfaces towards non-smoothed segmentation masks. to achieve this task, we introduce a new region-based term for active contour segmentation in the multiphase level set framework. this term attracts evolving curves to reference contours while being constrained by rigidity terms. this technique considerably reduces flickering on the boundaries of the segmentation masks.
description of local singularities for image registration. recently, it has been shown that gradient-based meth- ods are the most powerful approaches for describing the lo- cal content of digital images in the neighborhood of salient points. in practice, salient points are always located on image singularities whatever the detector used. in this pa- per, we show that a more efficient mathematical notion can be used to describe singularities: the h¨older exponent. we propose here to conjointly use the h¨older exponents and the direction of minimal regularity of the bidimensionnal signal singularities to compute a signature describing precisely a region of interest centered on an interest point. h¨older ex- ponents are estimated thanks to the foveal wavelets theory and the resulting descriptor is shown to be more efficient than classical sift and pca-sift descriptors in the case of an image registration application.
adaptative evaluation of image segmentation results. we present in this article a new unsupervised evaluation criterion that enables the quantification of the quality of an image segmentation result according to the type of the original image. we first briefly present a comparative study of existing unsupervised evaluation criteria. then, we present a method for the determination of the type of the original image: uniform, mixed or textured by using a learning method (support vector machine). in the third part, we present the proposed algorithm for segmentation evaluation and the experimental results on synthetic images from a large database. last, we conclude and present some perspectives of this work.
measuring shape: ellipticity, rectangularity, and triangularity. object classification often operates by making decisions based on the values of several shape properties measured from an image of the object. this paper describes several algorithms (both old and new) for calculating ellipticity, rectangularity, and triangularity shape descriptors. the methods are evaluated by testing on both synthetic and real data.
a symmetric convexity measure. a new area-based convexity measure for polygons is described. it has the desirable properties that it is not sensitive to small boundary defects, and it is symmetric with respect to intrusions and protrusions. the measure requires a maximally overlapping convex polygon, and this is efficiently estimated using a genetic algorithm. examples of the measures application to medical image analysis are shown.
a hybrid fingerprint matcher. we describe a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. a set of 8 gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. a square tessellation of the filtered images is then used to construct an eight-dimensional feature map, called the ridge feature map. the ridge feature map along with the minutiae set of a fingerprint image is used for matching purposes. the genuine accept rate of the hybrid matcher is observed to be ~ 10% higher thanthat of a minutiae-based matcher at low false accept rates. fingerprint verification using the hybrid matcher on a pentium iii (800 mhz) processor, takes - 1.4 seconds.
comparison of methods for hyperspherical data averaging and parameter estimation. averaging is an important concept which has found numerous applications in general and in pattern recognition and computer vision in particular. in this paper we consider averaging directional vectors of arbitrary dimensions. given a set of vectors, we intend to compute an average vector which optimally represents the input vectors according to some formal criterion. several optimisation criteria are formulated. in particular, we present a class of robust estimators of up to 50% outlier tolerance. furthermore, we propose a technique to estimate another distribution parameter. experimental results on spherical data are presented to demonstrate the usefulness of the proposed methods.
building detection by dempster-shafer fusion of lidar data and multispectral aerial imagery. a method for the classification of land cover in urban areas by the fusion of first and last pulse lidar data and multi-spectral images is presented. apart from buildings, the classes "tree", "grass land", and "bare soil" are also distinguished by a classification method based on the theory of dempster - shafer for data fusion. examples are given for a test site in germany.
coarse-to-fine multiscale affine invariant shape matching and classification. in this paper, a multiscale algorithm for matching and classifying 2-d shapes is developed. the algorithm uses the 1-d dyadic wavelet transform (dwt) to decompose a shape's boundary into multiscale levels. then the coarse to fine matching and classification are achieved in two stages. in the first stage, the global features are extracted by calculating the curve moment invariants of the approximation coefficients. by calculating the normalized cross correlation of the 1-d triangle area representation of the detail coefficients, the local similarity is achieved by the second stage. the proposed algorithm is invariant to the affine transformation and to the boundary starting point variation. in addition, the results demonstrate that the new algorithm is not sensitive to small boundary deformations.