The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We aim to match two hypergraphs via pairwise characterization of multiple relationships. To this end, we introduce a technique referred to as Marginalized Constrained Compatibility Estimation (MCCE), which transforms the compatibility tensor representing hyper-edge similarities into a compatibility matrix representing edge similarities. We then cluster graph vertices associated with the compatibility...
This paper proposes a novel approach named Compressed Submanifold Multifactor Analysis (CSMA) to concisely and precisely deal with multifactor analysis. Compared to the state-of-the-art MPCA method that loses the original local geometry structures of input factors due to the averaging process, our proposed approach can preserve their original geometry. In addition, the fast low-rank approximation...
Multi-camera systems such as linear camera arrays are commonly used to capture content for multi-baseline stereo estimation, view generation for auto-stereoscopic displays, or similar tasks. However, even after a careful mechanical alignment, residual vertical disparities and horizontal disparity offsets impair further processing steps. In consequence, the multicamera content needs to be rectified...
It has been shown that multilinear subspace analysis is a powerful tool to overcome difficulties posed by viewpoint, illumination and expression variations in Active Appearance Model(AAM). However, the Higher Order Singular Value Decomposition (HOSVD) in multilinear analysis requires training samples to build the training tensor, which include face images under all different variations. It is hard...
In this paper, we propose a compact image signature based on VLAT. Our method integrates spatial information while significantly reducing the size of original VLAT by using two pojection steps. we carry out experiments showing our approach is competitive with state of the art signatures.
Graph-based methods are an important category of semi-supervised learning techniques. However, in many situations the graph representation of relational patterns can lead to substantial loss of information. This is because in real-world problems objects and their features tend to exhibit multiple relationships rather than simple pairwise ones. In this paper, we develop a semi-supervised learning method...
With the recent explosion in the development of multimedia hardware capable of 3D display, 3D Picture Coding Sytems have assumed a pivotal role. While encoding techniques for stereo-scopic images is a well researched topic and compression standards such as MPEG provide variants to support it, compression of RGB-D data such as from the Microsoft Kinect sensor offers a number of unsolved challenges...
This paper proposes a framework of tensor-based ICA method for N-dimensional data analysis, which is called generalized N-dimensional ICA (GND-ICA). The proposed GND-ICA is based on multilinear algebra that treats N-dimensional data as a tensor without any unfolding preprocess. As an application, the GND-ICA can be used for multiple feature fusion and representation for color image classification...
Comparing with conventional character normalization methods not taking the discriminative information into account, this paper proposes a novel normalization method — Discriminative Normalization. Saliency regions contain most of discriminative information among similar characters. According to different types, they are enlarged in character normalization to increase their influence in recognition...
We present a method for human action recognition based on the combination of Histograms of Gradients into orientation tensors. It uses only information from HOG3D: no features or points of interest are extracted. The resulting raw histograms obtained per frame are combined into an orientation tensor, making it a simple, fast to compute and effective global descriptor. The addition of new videos and/or...
Due to the fact that many objects in the real world can be naturally represented as tensors, tensor subspace analysis has become a hot research area in pattern recognition and computer vision. However, existing tensor subspace analysis methods cannot provide an intuitionistic nor semantic interpretation for the projection matrices. In this paper, we propose Sparse Tensor Principal Component Analysis...
Recently sparse coding has received expressions of interest in the field of pattern recognition. Most existing methods take the data-as-vector formulation, and deal with images (the second order tensor) or volumes (the third order tensor) by vectorization. However, such kind of vectorization will lose the original structure of the data and reduce the reliability of post processing, leading a poor...
Computing similarities between data samples is a fundamental step in most Pattern Recognition (PR) tasks. Better similarity measures lead to more accurate prediction of labels. Computing similarities between video sequences has been a challenging problem for the PR community for long because videos have both spatial and temporal context which are hard to capture. We describe a novel approach that...
We present an efficient algorithm that computes the relative pose between two calibrated views given that the rotation is around a single axis. The algorithm is suited for indoor and urban environments that have an abundance of orthogonal lines. We also present a framework in which this algorithm is used within a hypothesize-and-test framework to simultaneously detect orthogonal lines and compute...
In pattern recognition, two of the main paradigms for describing objects are the feature-based and the (dis)similarity-based one. The former aims at encoding tangible features that characterize the object per-se. The latter gives a relational description of the object, considering the similarities with other reference entities. In this paper, we propose the marriage between these two philosophies:...
In this paper we address the robust face recognition problem for color faces with large variations in pose, illumination and facial expression. A novel algorithm is proposed, namely the Multilinear Color Tensor Discriminant (MCTD) model. This approach utilizes tensor representation to preserve image structure, as well as enhance discriminate capability via color space transformation. On the other...
A recognition method of road markings for map generation is presented. For accurate position estimation and classification, two voting schemes are proposed and combined. The first is multi-frame sparse tensor voting for geometric feature extraction, and the second is contour localization using the resulting tensor field. Classification is based on the similarity between the aligned contour and the...
This paper tackles the matching problem of partial deformable shapes with changing boundary and varying topology. We compute high-order graph matching directly on manifolds, without global/local surface parameterization. In particular, we articulate the heat kernel tensor (HKT), which is a high-order potential of geometric compatibility between feature tuples measured by heat kernels within bounded...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.