123058798
Compact representation of multidimensional data using tensor rank-one decomposition
This paper presents a new approach for representing multidimensional data by a compact number of bases. We consider the multidimensional data as tensors instead of matrices or vectors, and propose a tensor rank-one decomposition (TROD) algorithm by decomposing Nth-order data into a collection of rank-1 tensors based on multilinear algebra. By applying this algorithm to image sequence compression, we obtain much higher quality images with the same compression ratio as principal component analysis (PCA). Experiments with gray-level and color video sequences are used to illustrate the validity of this approach.
5819516
Facial expression decomposition
In this paper, we propose a novel approach for facial expression decomposition - higher-order singular value decomposition (HOSVD), a natural generalization of matrix SVD. We learn the expression subspace and person subspace from a corpus of images showing seven basic facial expressions, rather than resort to expert-coded facial expression parameters. We propose a simultaneous face and facial expression recognition algorithm, which can classify the given image into one of the seven basic facial expression categories, and then other facial expressions of the new person can be synthesized using the learned expression subspace model. The contributions of this work lie mainly in two aspects. First, we propose a new HOSVD based approach to model the mapping between persons and expressions, used for facial expression synthesis for a new person. Second, we realize simultaneous face and facial expression recognition as a result of facial expression decomposition. Experimental results are presented that illustrate the capability of the person subspace and expression subspace in both synthesis and recognition tasks. As a quantitative measure of the quality of synthesis, we propose using gradient minimum square error (GMSE) which measures the gradient difference between the original and synthesized images.
16403613
Gradient Adaptive Image Restoration and Enhancement
Various methods have been proposed for image enhancement and restoration. The main difficulty is how to enhance the structures uniformly while suppressing the noise without artifacts. In this paper, we tackle this problem in the gradient domain instead of the traditional intensity domain. By enhancing the gradient field, we can enhance the structure uniformly without overshooting at the boundary. Because the gradient field is very sensitive to noise, we apply an orientation-isotropy adaptive filter to the gradient field, suppressing the gradients in the noise regions while enhancing along the object boundaries. Thus we obtain a modulated gradient field, which is usually not integrable. We reconstruct the enhanced image from the modulated gradient field with least square errors by solving a Poisson equation. This method can enhance the object contrast uniformly, suppress the noise with no artifacts, and avoid setting stopping time as in PDE methods. Experiments on noisy images show the efficacy of our method.
1492261
Object Tracking with Particle Filter Using Color Information
Color-based particle filter for object tracking has been an active research topic in recent years. Despite great efforts of many researchers, there still remains to be solved the problem of contradiction between efficiency and robustness. The paper makes an attempt to partially solve this problem. Firstly, the Integral Histogram Image is introduced by which histogram of any rectangle region can be computed at negligible cost. However, straightforward application of the Integral Histogram Images causes the problem of “curse of dimensionality”. In addition, traditional histogram is inefficient and inaccurate. Thus we propose to adaptively determine histogram bins based on K-Means clustering, which can represent color distribution of object more compactly and accurately with as a small number of bins. Thanks to the Integral Histogram Images and the clustering based color histogram, we finally achieve a fast and robust particle filter algorithm for object tracking. Experiments show that the performance of the algorithm is encouraging.
10539492
On the extraction of vocal-related information to facilitate the management of popular music collections
With the explosive growth of networked collections of musical material, there is a need to establish a mechanism like a digital library to manage music data. This paper presents a content-based processing paradigm of popular song collections to facilitate the realization of a music digital library. The paradigm is built on the automatic extraction of information of interest from music audio signals. Because the vocal part is often the heart of a popular song, we focus on developing techniques to exploit the solo vocal signals underlying an accompanied performance. This supports the necessary functions of a music digital library, namely, music data organization, music information retrieval/recommendation, and copyright protection
9426825
Adaptive presentation for effective Web-based learning of 3D content
In this paper we focus on incorporating adaptive presentation and 3D visualization into Web-based learning environments to enhance learners' learning outcomes, especially to facilitate learners' spatial reasoning on geometric topics in computer graphics. This system is called CooTutor (coordinate tutor). According to educational media studies, "Media and Method" are the main concerns of developing such a system. In CooTutor, interactive 3D media is used to present spatial relations effectively to the learners. Instructional methods and strategies are embedded in its adaptive mechanism by incorporating ITS (intelligent tutoring systems) techniques. Since geometric transformation is the example domain of our study, learners' spatial ability is considered as an index for adapting the presentation. To achieve better abstraction and flexibility, we have chosen to separate the concept sequencing from the underlying learning materials. We believe that Web-based learning could take more advantages of available computing power to enhance learning by realizing innovative instructional design, as in the case of 3D interactive presentation.
45076379
Kernel Spectral Correspondence Matching Using Label Consistency Constraints
This paper investigates a kernel spectral approach to the problem of point pattern matching. Our first contribution is to show how kernel principal components analysis can be effectively used for solving the point correspondence matching problem when the point-sets are subject to structural errors, i.e. they are of different size. Our second contribution is to show how label consistency constraints can be incorporated into the construction of the Gram matrices for solving the articulated point pattern matching problem. We compare our algorithm with earlier point matching approaches and provide experiments on both synthetic data and real world data.
38802324
Experiments with improved approximate mean value analysis algorithms
Abstract Approximate mean value analysis (MVA) is a popular technique for analyzing queueing networks because of the efficiency and accuracy that it affords. In this paper, we present a new software package, called the improved approximate mean value analysis library (IAMVAL) , which can be easily integrated into existing commercial and research queueing network analysis packages. The IAMVAL packages include two new approximate MVA algorithms, the queue line (QL) algorithm and the fraction line (FL) algorithm, for analyzing multiple class separable queueing networks. The QL algorithm is always more accurate than, and yet has approximately the same computational efficiency as, the Bard–Schweitzer proportional estimation (PE) algorithm, which is currently the most widely used approximate MVA algorithm. The FL algorithm has the same computational cost and, in noncongested separable queueing networks where queue lengths are quite small, yields more accurate solutions than both the QL and PE algorithms.