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In this correspondence, we study the extra-factor estimation problem with the assumption that the training image ensemble is expressed as an nth-order tensor with the nth-dimension characterizing all features for an image and other dimensions for different extra factors, such as illuminations, poses, and identities. To overcome the local minimum issue of conventional algorithms designed for this problem, we present a novel statistical learning framework called mode-kn Factor Analysis for obtaining a closed-form solution to estimating the extra factors of any test image. In the learning stage, for the kth (k ≠ n) dimension of the data tensor, the mode-kn patterns are constructed by concatenating the feature dimension and the kth extra-factor dimension, and then a mode-kn factor analysis model is learnt based on the mode-kn patterns unfolded from the original data tensor. In the inference stage, for a test image, the mode classification of the kth dimension is performed within a probabilistic framework. The advantages of mode-kn factor analysis over conventional tensor analysis algorithms are twofold: 1) a closed-form solution, instead of iterative sub-optimal solution as conventionally, is derived for estimating the extra-factor mode of any test image; and 2) the classification capability is enhanced by interacting with the process of synthesizing data of all other modes in the kth dimension. Experiments on the Pointing'04 and CMU PIE databases for pose and illumination estimation both validate the superiority of the proposed algorithm over conventional algorithms for extra-factor estimation. [ABSTRACT FROM AUTHOR]

This paper introduces a head pose estimation system that automatically localizes the nose-tips of the faces and estimates head poses in images simultaneously. In the training stage, the nose-tips of the faces are first manually labeled. The appearance variations caused by head pose changes are then characterized by a Tensorposes model. Given an image with unknown head pose and nose-tip location, the nose-tip of the face is automatically localized in a coarse-to-fine fashion after the skin color segmentation. The head pose is also estimated simultaneously. The performance of our system is evaluated on the Pointing'04 head pose image data set. We first evaluate the classification performance of the Tensorposes models with image patches of the faces cropped according to the manually labeled nose-tip locations of the faces in the Pointing '04 data set. By leaving-one-person-out evaluation strategy, we obtain the optimal parameters of the Tensorposes model, and evaluate the discriminative power of the Tensorposes model built based on high order singular value decomposition (HOSVD) and multilinear independent component analysis (MICA), and naive principal component analysis (PCA) subspace models. It is shown Tensorposes model by HOSVD and MICA decomposition performs similarly good but much better than naive PCA subspace models. The Tensorposes model is then utilized to automatically localize nose-tip location in the testing image and to simultaneously estimate the head pose. The nose-tip localization and pose estimation accuracy of the proposed system are evaluated against the ground truth. Finally, cross-database evaluation of the performance of our system is carried out on Pointing'04 database, a selected subset of CMU PIE database, and some pictures from CLEAR'07 head pose evaluation database. The experiments show that our system generalizes reasonably well to the real-world scenarios. [ABSTRACT FROM AUTHOR]

Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, having style, living location, and weather conditions. Males and females may' also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database. [ABSTRACT FROM AUTHOR]

Principal Components Analysis (PCA) has traditionally been utilized with data expressed in the form of 1-D vectors, but there exists much data such as gray-level images, video sequences, Gabor-filtered images and so on, that are intrinsically in the form of second or higher order tensors. For representations of image objects in their intrinsic form and order rather than concatenating all the object data into a single vector, we propose in this paper a new optimal object reconstruction criterion with which the information of a high-dimensional tensor is represented as a much lower dimensional tensor computed from projections to multiple concurrent subspaces. In each of these subspaces, correlations with respect to one of the tensor dimensions are reduced, enabling better object reconstruction performance. Concurrent subspaces analysis (CSA) is presented to efficiently learn these subspaces in an iterative manner. In contrast to techniques such as PCA which vectorize tensor data, CSA's direct use of data in tensor form brings an enhanced ability to learn a representative subspace and an increased number of available projection directions. These properties enable CSA to outperform traditional algorithms in the common case of small sample sizes, where CSA can be effective even with only a single sample per class. Extensive experiments on images of faces and digital numbers encoded as second or third order tensors demonstrate that the proposed CSA outperforms PCA-based algorithms in object reconstruction and object recognition. [ABSTRACT FROM AUTHOR]

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters. [ABSTRACT FROM AUTHOR]

We propose a motion estimation system that uses stereo image pairs as the input data. To perform experimental work, we also obtain a sequence of outdoor stereo images taken by two metric cameras. The system consists of four main stages, which are (1) determination of point correspondences on the stereo images, (2) correction of distortions in image coordinates, (3) derivation of 3D point coordinates from 2D correspondences, and (4) estimation of motion parameters based on 3D point correspondences. For the first stage of the system, we use a four-way matching algorithm to obtain matched points on two stereo image pairs at two consecutive brae instants (t, and t,.,). Since the input data are stereo images taken by cameras, it has two types of distortions, which are (i) film distortion and (ii) lens distortion. These two distortions must be corrected before any process can be applied on the matched points. To accomplish this goal, we use (i) bilinear transform for film distortion correction and (ii) lens formulas for lens distortion correction. After correcting the distortions, the results are 2D coordinates of each matched point that can be used to derive 3D coordinates. However, due to data noise, the calculated 3D coordinates do not usually represent a consistent rigid structure that is suitable for motion estimation; therefore, we suggest a procedure to select good 3D point sets as the input for motion estimation. The procedure exploits two constraints, rigidity between different time instants and uniform point distribution across the object on the image. For the last stage, we use an algorithm to estimate the motion parameters. We also wish to know what is the effect of quantization error on the estimated results; therefore an error analysis based on quantization error is performed on the estimated motion parameters. In order to test our system, eight sets of stereo image pairs are extracted from an outdoor stereo image sequence and used as the input data. The experimental results indicate that the proposed system does provide reasonable estimated motion parameters. [ABSTRACT FROM AUTHOR]

This article presents a model-based approach for nonrigid object motion and deformation analysis from 3D data. The modeling primitives used in this research are the superquadrics, which have already been proven useful in describing a variety of natural and man-made objects. This model-based approach is not only in accordance with the human visual perception process but also able to decouple the large and unstructured nonrigid motion estimation system into simple and well structured subsystems. We develop a recursive algorithm for estimating global motion and object shape, which effectively incorporates a priori knowledge of the object into the estimation procedure and obtains a good estimate of the global motion and object shape even if the given 3D points are distributed bias. After compensating for the global motion of the object, a tensor model of local deformation is introduced and a spherical harmonic surface-fitting algorithm is described such that the localized deformations of the object surface can be characterized. The local deformations of the object are then estimated using tensor-description-based analysis and parametrized by the directions and magnitudes of the extreme deformations in a localized surface element. To illustrate the potential of this model-based approach for nonrigid motion analysis, a real data example is presented using the proposed approach. This example involves estimating the left ventricle motion and deformations from a time sequence of 3D coordinates of coronary artery bifurcation points. The estimation results show the success of the model-based approach even when the given bifurcation points are distributed only on half of the left ventricle surface. [ABSTRACT FROM AUTHOR]

This article presents a robust algorithm for automated registration of images related by rigid-body transformations. This algorithm uses a new region-based similarity metric, which enables accurate registration of images of large contrast differences. Region segmentation required by the metric is accomplished using a multiscale segmentation algorithm, and minimization of this metric is done using the Powell direction set method. Experimental results are presented to demonstrate that the algorithm is effective for aligning images from single or multiple imaging modalities without the use of any fiducial markers. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 513–518, 1997 [ABSTRACT FROM AUTHOR]

We explain that the task of multiple rigid motion segmentation and estimation from image feature point correspondences demands an estimator of high robustness. We show that a heuristics-based partial modeling approach can be used to develop a highly robust estimator called the MF estimator for general regression, where ‘MF’ represents an abbreviation of Model Fitting. Finally, we provide experimental results in estimating single rigid motion from a mixture of 2D-2D (or image-image), 3D-2D (or range-image), and 3D-3D (or range-range) corresponding point data by using the proposed MF estimator. As will be seen. only four well matched corresponding point pairs are needed to get a good estimate of motion parameters no matter how m(my mismatched corresponding point pairs (or outliers) occur. The article represents an initial effort towards robust image-motion analysis. [ABSTRACT FROM AUTHOR]

Motion can provide useful information about the structure of an articulated object. In this paper, we develop a method to estimate the revolute joints from two monocular images of the moving articulated object. Firstly, according to the characteristic of the articulated structure and motion, constraint equations about motion and joint parameters from image point correspondences are deduced, which provide crucial information for joint estimation. Then, the position ambiguity caused by the one DOF attribute, and the scale ambiguity resulted from monocular images are discussed. Also, a skillful function is employed to avoid the degeneration cases of estimation. Finally, the joint axis are computed from the estimated rotation matrices, and all estimated joint points are scaled to have the same scale factor to their ground truth. Simulations and experiments on real images show the correctness and efficiency of the algorithm. [Copyright &y& Elsevier]