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We present a promising analysis on using the pattern of symmetry in the face to increase the accuracy of three-dimensional face recognition. We introduce the concept of the dasiaaverage-half-facepsila, motivated by the symmetry preserving singular value decomposition. We compare face recognition results using the eigenfaces face recognition algorithm with average-half-face data and full face data...
To alleviate the conventional problems of LDA and its variants, we propose a procedure of predicting eigenvalues using few reliable eigenvalues from the range space. Partitioning of entire eigenspace is performed using two control points, however, the effective low dimensional discriminative vectors are extracted from the whole eigenspace. This prediction strategy enables to perform discriminant evaluation...
In this paper, a novel background model on spatio-temporal patches is introduced for video surveillance, especially for night outdoor scene, where extreme lighting conditions often cause troubles. The spatio-temporal patch, called brick, is presented to simultaneously capture spatio-temporal information in surveillance video. The set of bricks of a given background patch, under all possible lighting...
This study focuses on a recent paper ldquo100% Accuracy in Automatic Face Recognitionrdquo published on Science, in which an ldquoAverage Facerdquo is proposed and claimed to be capable of dramatically improving performance of a face recognition system. To reveal its working mechanism, we perform the averaging process using pose-varied synthetic images generated from 3D face database and conduct a...
In this paper, we propose a new nonlinear dimensionality reduction algorithm by adopting regularized least-square criterion on local areas of the data distribution. We first propose a local linear model to describe the characteristic of the low-dimensional coordinates of the neighborhood centered in each data point, and use regularized least-square criterion to evaluate the fitness of the low-dimensional...
In pattern recognition, computer vision, and image processing, many approaches are based on second order operators. Well-known examples are second order networks, the 3D structure tensor for motion estimation, and the Harris corner detector. A subset of second order operators are quadratic operators. It is lesser known that every second order operator can be written as a weighted quadratic operator...
With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or...
Fibre orientation is an important structural property of fibre-based materials. For example, in paper the orientation of the fibres influences the dimensional strength of the sheet and the tendency of the sheet to curl and twist at moisture changes. Here, we present a three-dimensional image analysis method for estimating the fibre orientation and the orientation anisotropy. The proposed method can...
Computer modeling of a large-scale scene such as a city becomes an important topic for computer vision and computer graphics research areas etc. Image-based rendering (IBR) is an effective method for expressing realistic scene, and can construct any arbitrary viewpoint by using the captured real images. However, the large size of the image database in IBR causes serious problems in actual applications,...
Generative models are well known in the domain of statistical pattern recognition. Typically, they describe the probability distribution of patterns in a vector space. In contrast, very little work has been done with generative models of graphs because graphs do not have a straight-forward vectorial representation. In this paper we examine the problem of creating generative distributions over sets...
In this paper, we present a new approach for view-invariant action recognition using constraints derived from the eigenvalues of planar homographies associated with triplets of body points. Unlike existing methods that study an action as a whole, or break it down into individual poses, we represent an action as a sequence of pose transitions. Using the fact that the homography induced by the motion...
Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR-2DLPP), which is an extension of 2D-LPP...
This paper presents the Monte Carlo subspace method - a cost-effective classification technique for high-dimensional data by the Monte Carlo scheme. The most intensive computation in the linear subspace methods is the reduction of dimensionality of the feature space by the eigen decomposition or singular value decomposition. In the present method, the subspaces are learned by updating their orthonormal...
In this paper, a multilinear approach based on image texture for face recognition is present. First, we extract the texture features of the facial images using the local binary pattern (LBP) algorithm. Then, we apply the high-order orthogonal iteration (HOOI) algorithm, the algebra of higher-order tensors, to obtain a compact and effective representation of the facial images based on the texture features...
In computer vision, background subtraction method is widely used to extract a changing region in a scene. However, it is difficult to simply apply this method to a scene with moving background object, because such object may be extracted as a changing region. Therefore, a method has been proposed to estimate both current background image and occluding object region simultaneously by using eigenspace-based...
To learn a metric for query-based operations, we combine the concept underlying manifold learning algorithms and the minimum volume ellipsoid metric in a unified algorithm to find the nearest neighbouring points on the manifold on which the query point is lying. Extensive experiments on standard benchmark data sets in the context of classification showed promising and interesting results with regard...
We propose a novel multivariate uniformity criterion for testing uniformity of point density in an arbitrary dimensional point pattern. An unsupervised, nonparametric data clustering algorithm, using this criterion, is also presented. The algorithm relies on a relatively general notion of cluster so that it is applicable to clusters of relatively unrestricted shapes, densities and sizes. We define...
We present a classifier unifying local features based representation and subspace based learning. We also propose a novel method to merge kernel eigen spaces (KES) in feature space. Subspace methods have traditionally been used with the full appearance of the image. Recently local features based bag-of-features (BoF) representation has performed impressively on classification tasks. We use KES with...
Graph structures have been proved important in high level-vision since they can be used to represent structural and relational arrangements of objects in a scene. One of the problems that arises in the analysis of structural abstractions of object is graph clustering. In this paper, we explore how permutation invariants computed from the trace of the heat kernel can be used to characterize graphs...
Current sign language recognition systems are still designed for signer-dependent operation only and thus suffer from the problem of interpersonal variability in production. Applied to signer-independent tasks, they show poor performance even when increasing the number of training signers. Better results can be achieved with dedicated adaptation methods. In this paper, we describe a vision-based recognition...
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