waashk/dblp huggingface dataset:
1d-pca, 2d-pca to nd-pca. in this paper, we first briefly reintroduce the 1d and 2d forms of the classical principal component analysis (pca). then, the pca technique is further developed and extended to an arbitrary n-dimensional space. analogous to 1d- and 2d-pca, the new nd-pca is applied directly to n-order tensors (n . 3) rather than 1-order tensors (1d vectors) and 2-order tensors (2d matrices). in order to avoid the difficulties faced by tensors computations (such as the multiplication, general transpose and hermitian symmetry of tensors), our proposed nd-pca algorithm has to exploit a newly proposed higher-order singular value decomposition (ho-svd). to evaluate the validity and performance of nd-pca, a series of experiments are performed on the frgc 3d scan facial database.
3d human body measurement by multiple range images. a compact and high-speed 3d human body measurement system is proposed. whole human body data which we can take from systems that have been developed with a few viewpoints is not successfully acquired due to occlusion. it is proposed a method that can obtain successful data by allocating multiple rangefinders correctly. four compact rangefinders are installed in a pole. those four pole units with 16 rangefinders are assigned around a human. multiple viewpoint range images allow the 3d shape reconstruction for a human body. the measurement time is 2 seconds and the average error is found to be 1.88 mm. in this paper, system configuration, calibration and experimental results are described.
separating reflections from images using kernel independent component analysis. when we view a scene through transparent glass, the image is a linear superposition of two images, a real image observed through a glass and a virtual image reflected on it. we can separate the reflections by a polarization and independent component analysis (ica). since the image observed through digital camera is non-linearly transformed by gamma correction etc, it may cause error in image processing for image analysis and measurement. the kernel-based methods are effective for such non-linearity. in this paper, we remove the reflections by using kernel independent component analysis (kica) and show that kica is more effective than ica even if the observed image is non-linearly transformed by camera.
stochastic framework for symmetric affine matching between point sets. this paper presents a new approach to obtain symmetry in point matching problem. here, symmetric matching means the essential property that the choices of source and target should not determine the eventual matching results. most earlier approaches to achieve symmetric matching have been in deterministic fashions, where symmetry constraints are added into the matching cost functions to impose source-target symmetric property during the matching process. nevertheless, these modified cost functions cannot generally converge to real ground truth, and further, the perfect source-target symmetry cannot be achieved. given initial forward and backward matching matrices pair, computed from any reasonable matching strategies, our approach yields perfectly symmetric mapping matrices from a stochastic framework that simultaneously considers the errors underneath the initial matching matrices and the imperfectness of the symmetry constraint. an iterative generalized total least square (gtls) strategy has been developed such that perfect source-target symmetry is imposed.
multiple camera calibration with bundled optimization using silhouette geometry constraints. we propose a method of calibrating multiple camera systems that operates by adjusting the camera parameters and the 3d shape of objects onto silhouette observations. our method employs frontier points, which are geometrically meaningful points on object surfaces, to determine the geometrical relations among multiple cameras. in contrast to conventional methods, both camera parameters and the 3d positions of the frontier points are jointly estimated by minimizing the 2d projection errors between the 2d projected positions of the frontier points and observed silhouette contours for all cameras. this method makes it possible to obtain accurate calibration results without using any special instruments. experimental results using real image data demonstrate the effectiveness of our method.
an omnidirectional stereo vision system using a single camera. we describe a new omnidirectional stereo imaging system that uses a concave lens and a convex mirror to produce a stereo pair of images on the sensor of a conventional camera. the light incident from a scene point is split and directed to the camera in two parts. one part reaches camera directly after reflection from the convex mirror and forms a single-viewpoint omnidirectional image. the second part is formed by passing a subbeam of the reflected light from the mirror through a concave lens and forms a displaced single viewpoint image where the disparity depends on the depth of the scene point. a closed-form expression for depth is derived. since the optical components used are simple and commercially available, the resulting system is compact and inexpensive. this, and the simplicity of the required image processing algorithms, make the proposed system attractive for real-time applications, such as autonomous navigation and object manipulation. the experimental prototype we have built is described.
joint image segmentation and interpretation using iterative semantic region growing on sar sea ice imagery. segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. typically, the segmentation and interpretation are performed separately with the interpretation as a post processing of segmentation. in this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. the segmentation algorithm is based on a region growing technique and the interpretation is a markov random field (mrf) based classification. the two processes are integrated under the bayesian framework, with both aiming at reducing a defined energy. the interactions between the two are bidirectional by letting the interpretation result have some degree of control on the region growing process. various features can hence be efficiently combined, and accurate classifications are obtained for operational synthetic aperture radar (sar) sea ice applications.
a maximum a posteriori probability viterbi data association algorithm for ball tracking in sports video. in this paper, we derive a data association algorithm for object tracking in a maximum a posteriori framework: the output of the algorithm is the sequence of measurement-totarget associations with maximum a posteriori probability. we model the object motion as a markov process, and solve this otherwise combinatorially complex problem efficiently by applying the viterbi algorithm. a method for combining forward and backward tracking results is also developed, to recover from tracking errors caused by abrupt motion changes of the object. the proposed algorithm is applied to broadcast tennis video to track a tennis ball. experiments show that its performance is comparable to that of a computationally more expensive particle-filter-based algorithm.
classifier combination based on active learning. in this paper, we propose classifier combination based on active learning, which deals with the design of classifier combination systems as training a combiner at the aggregation level and introduces svm active learning into the design of this multi-category decision combiner. this algorithm presented greatly reduces the number of labeled data the classifier system needs in order to achieve satisfactory performance. this algorithm consists of two main steps: firstly designing and training first level classifiers which can output posterior probability vectors as the input of the second level combiner, secondly designing second level combiner based on svm active learning and classifying testing samples with this combiner. experiments on standard database show that our algorithm performs better than current classifier combination rules when considering both labeling cost and classification accuracy.
string kernels for matching seriated graphs. graph seriation allows the nodes of a graph to be placed in a string order, and then matched using string alignment algorithms. prior work has used bayesian methods to derive the string edit costs required in matching. the aim in this paper is to demonstrate how the matching of seriated graphs can be kernelised. to do this we make use of string kernels and show how the parameters of the kernels can be linked to edge density. we illustrate that the graph edit distances computed using the string kernel can be used for graph clustering.
dimensionality reduction with adaptive kernels. a kernel determines the inductive bias of a learning algorithm on a specific data set, and it is beneficial to design specific kernel for a given data set. in this work, we propose a kind of new kernel, called locality- adaptive-kernel (lake), which adaptively measures the data similarity by considering the geometrical structure of the data set. in theory, we prove that the lake is a special marginalized kernel; and intuitively, when the local kernel in lake is constrained to be linear, it has the explicit semantic of merging multiple local linear analyzers into a single global nonlinear one. we show in a toy problem that the kernel principal component analysis with lake well captures the intrinsic nonlinear principal curve of the data set. moreover, a large set of experiments are presented to verify that the classification performance is sensitive to the kernel variation; and the extensive face recognition experiments on different databases demonstrate that kpca and kda based on lake are both superior to those based on traditional fixed kernels.
estimation of rigid and non-rigid facial motion using anatomical face model. we present a model-based approach to recover the rigid and non-rigid facial motion parameters in video sequences. our face model is based on anatomically motivated muscle actuator controls to model the articulated non-rigid motion of a human face. the model is capable of generating a variety of facial expressions by using a small number of muscle actuator controls. we estimate rigid and non-rigid parameters in two steps. first, we use a multi-resolution scheme to recover the global 3d rotation and translation by linear least squareminimization. then, we estimate the muscle actuator controls using the levenberg-marquardt minimization technique applied to a function, which is constrained by both optical flow and the dynamics of the deformable model. we present the results of our system on both real and synthetic images.
a nonlinear variational model for pet reconstruction. pet image was often influenced by noise. in this paper, we proposed a nonlinear variational model for improving reconstruction of pet images. the use of variational model was due to its effectiveness for reducing noise in 2d images while preserving edges. our results indicated that the proposed method application to computer-simulated and real pet phantom outperformed the conventional method in terms of both visual quality and quantitative accuracy.
combining cepstral and prosodic features in language identification. a novel approach of combining cepstral features and prosodic features in language identification is presented in this paper. this combination approach shows a significant improvement on a gmm-ubm based language identification (lid) system which utilizes modern shifted delta cepstrum (sdc) and feature warping techniques. the proposed system achieves a high accuracy of 87.1% on a 10-language task, and outperforms the baseline system by 12%. the prosodic features are proven to be very effective in both tonal and non-tonal lid, as they deliver new language-discrimination information in addition to those from widely used cepstral features. additionally, the performance of mfcc and plp features with different coefficient numbers in language identification tasks are researched and compared. less number of coefficients is more likely to be sufficient or even better for language identification.
improving retrieval performance by long-term relevance information. relevance feedback (rf) is an iterative process which improves the retrieval performance by utilizing the user's feedback on retrieved results. traditional rf techniques use solely the short-term experience and are short of knowledge of cross-session agreement. in this paper, we propose a novel rf framework which facilitates the combination of short-term and long-term experiences by integrating the traditional methods and a new technique called the virtual feature. the feedback history of all the users is digested by the system and is represented as a virtual feature of the images. as such, the dissimilarity measure can be adapted dynamically depending on the estimate of the relevance probability derived from the virtual features. the results manifest that the proposed framework outperforms the one that adopts a single traditional rf technique.
extended isomap for classification. the isomap method has demonstrated promising results in finding a low dimensionalembedding from samples in the high dimensional input space. the crux of this method is to estimate geodesic distance with multidimensional scaling fo dimensionality eduction. since the isomap method is developed based on the reconstruction principle, it may not be optimal from the classification viewpoint. we present an extended isomap method that utilizes fisher linear discriminant for pattern classification. numerous experiments on image data sets show that our extension is more effective than the original isomap method for pattern classification. furthermore, the extended isomap shows promising results compared with best classification methods in the literature.
a unification framework for tree and block wavelet encoders. wavelet transform coefficient encoders are broadly categorized as depending on correlations: across sub-bands through a zerotree, or within sub-bands through a block quad-tree. this paper proposes a unification framework that allows a suitable algorithm to be crafted according to need. an example block-tree algorithm is described in detail, and results show that the performance closely approaches a state-of-the-art encoder, without needing the complexity of arithmetic coding.
statistical model for the classification of the wavelet transforms of t-ray pulses. this study applies auto regressive (ar) and auto regressive moving average (arma) modeling to wavelet decomposed terahertz pulsed signals to assist biomedical diagnosis and mail/packaging inspection. t-ray classification systems supply a wealth of information about test samples to make possible the discrimination of heterogeneous layers within an object. in this paper, the classification of normal human bone (nhb) osteoblasts against human osteosarcoma (hos) cells and the identification of seven different powder samples are demonstrated. a correlation method and an improved prony's method are investigated in the calculation of the ar and arma model parameters. these parameters are obtained for models from second to eighth orders and are subsequently used as feature vectors for classification. for pre-processing, wavelet de-noising methods including the sure (stein's unbiased estimate of risk) and heuristic sure soft threshold shrinkage algorithms are employed to de-noise the normalised t-ray pulsed signals. a mahalanobis distance classifier is used to perform the final classification. the error prediction covariance of ar/arma modeling and the classification accuracy are calculated and used as metrics for comparison.
sammon's nonlinear mapping using geodesic distances. sammon's nonlinear mapping (nlm) is an iterative procedure to project high dimensional data into low dimensional configurations. this paper discusses nlm using geodesic distances and proposes a mapping method geonlm. we compare its performance through experiments to the performances of nlm and isomap. it is found that both geonlm and isomap can unfold data manifolds better than nlm. geonlm outperforms isomap when the short-circuit problem occurs in computing the neighborhood graph of data points. in turn, isomap outperforms geonlm if the neighborhood graph is correctly constructed. these observations are discussed to reveal the features of geodesic distance estimation by graph distances.
analyzing facial expressions using intensity-variant 3d data for human computer interaction. research on automatic techniques for analyzing human facial behavior has been intensified, driven by the emerging research field of affective computing [10, 9] and its important application in human computer interaction and intelligent systems. the face surface is a three dimensional timevarying 'wave', which is associated with the movement of facial expressions. tracing the behavior of the 3d primitive features could reveal precious information about the nature of the underlying physical process. in this paper, we propose to study the intensity-variant facial expressions in a 3d space. we present a new approach for 3d expression model analysis, tracking and classification. we demonstrate the feasibility and advantage of the proposed method using the 3d range data for prototypic 3d facial expression recognition.
k-edge connected neighborhood graph for geodesic distance estimation and nonlinear data projection. nonlinear data projection based on geodesic distances requires the construction of a neighborhood graph that spans all data points so that the geodesic distance between any pair of data points could be estimated by the graph distance between the pair. this paper proposes an approach for constructing a k-edge connected neighborhood graph. the approach works by repeatedly extracting minimum spanning trees from the complete euclidean graph of all data points. the constructed neighborhood graph has the following properties: (1) it is k-connected; (2) each point connects to its k nearest neighbors; (3) if the graph is cut into two partitions, the cut edges contain k shortest edges between the two partitions. experiments show that the presented approach works well for clustered data and outperforms the nearest neighbor approaches used in isomap for evenly distributed data.
spherical objects based motion estimation for catadioptric cameras. in this paper, spherical objects are employed to estimate motion parameters between two central catadioptric cameras. an occluding contour of a sphere is projected to a conic in the central catadioptric image, and such a conic is called a sphere image. the main contribution of this work is that such a sphere image is shown to be able to be parameterized by only three parameters rather than five ones as a generic conic requires. based on such a finding, an efficient motion estimation algorithm is proposed which consists of the following three main steps: firstly, sphere images are efficiently detected by the hough transform and refined by minimizing the orthogonal distances. secondly, the centers of these spherical objects are estimated from the above obtained sphere images in the two catadioptric camera coordinate systems respectively. thirdly, motion parameters are estimated from the corresponding centers. the proposed algorithm is shown workable by experiments and can be applied to camera network calibration and robot localization.
building connected neighborhood graphs for locally linear embedding. locally linear embedding is a nonlinear method for dimensionality reduction and manifold learning. it requires well-sampled input data in high dimensional space so that neighborhoods of all data points overlap with each other. in this paper, we build connected neighborhood graphs for the purpose of assigning neighbor points. a few methods are examined to build connected neighborhood graphs. they have made lle applicable to a wide range of data including under-sampled data and non-uniformly distributed data. these methods are compared through experiments on both synthetic and real world data sets.
catadioptric line features detection using hough transform. a line in space is projected to a conic in the central catadioptric image, and such a conic is called a line image. this paper proposes a novel approach for efficiently detecting line images using hough transform. detecting line images brings two novel challenges for conic detection: one is that effects of occlusion are very significant where traditional conic detecting methods may fail, the other is that line images can belong to any type of conic, such as, line, circle, ellipse, hyperbola, parabola etc., and it is very difficult to detect a conic when its type is unknown. the main contribution of this work is that we prove a line image can be parameterized by only two parameters on the gaussian sphere rather than five ones as a generic conic requires, and the above two challenges can be substantially solved accordingly. the validity of our proposed approach is illustrated by experiments.
locally multidimensional scaling for nonlinear dimensionality reduction. a data embedding method is introduced to configure global coordinates of data using local distances as input. the method applies classical multidimensional scaling within a neighborhood of each data point. the local models are then aligned to derive global coordinates in order to minimize a residual measure. the residual measure has a quadratic form of resulting global coordinates, which makes the alignment problem solved analytically by using an eigensolver. experiments show that the method produces less deformed embedding results than locally linear embedding. variations of the method and possible extensions are also discussed.
fast leave-one-out evaluation and improvement on inference for ls-svms. in this paper, a fast leave-one-out (loo) evaluation formula is introduced for least squares support vector machine (ls-svm) classifiers. the computation cost can be reduced to approximately 1/n when compared to normal loo procedure (is the number of training samples). inspired by its fast speed, we are able to use it to replace the original level 3 posterior probability approximation formula of the bayesian framework [bayesian framework for least squares support vector machine classifiers, gaussian processes and kernel fisher discriminant analysis] for ls-svm classifiers. the improved inference framework shows higher generalization performance and faster computation speed.
face pose estimation and its application in video shot selection. in this paper, a face pose estimation method and its application in video shot selection for face image preprocessing is introduced. the pose estimator is learned by a boosting regression algorithm called squarelev.r [boosting methods for regression] that learns poses from simple haar-type features. it consists of two tree structured subsystems for the left-right angle and up-down angle respectively. as a specific application in video based face recognition, the best shot selection problem is discussed, which results in a real-time system that can automatically select the most frontal face from a video sequence.
a novel linear approach to camera calibration from sphere images. recently, some linear approaches to camera calibration from sphere images are proposed. in this paper, a novel linear approach is proposed by exploiting the identity constraint on the entries of the matrices for the sphere images, which is much simpler and more intuitive than previous linear ones. this novel algorithm has been tested in extensive experiments with respect to noise sensitivity.