6108459
Fast multiple object tracking via a hierarchical particle filter
A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.
10638554
Near-optimal regularization parameters for applications in computer vision
Computer vision requires the solution of many ill-posed problems such as optical flow, structure from motion, shape from shading, surface reconstruction, image restoration and edge detection. Regularization is a popular method to solve ill-posed problems, in which the solution is sought by minimization of a sum of two weighted terms, one measuring the error arising from the ill-posed model, the other indicating the distance between the solution and some class of solutions chosen on the basis of prior knowledge (smoothness, or other prior information). One of important issues in regularization is choosing optimal weight (or regularization parameter). Existing methods for choosing regularization parameters either require the prior information on noise in the data, or are heuristic graphical methods. We apply a method for choosing near-optimal regularization parameters by approximately minimizing the distance between the true solution and the family of regularized solutions. We demonstrate the effectiveness of this approach for the regularization on two examples: edge detection and image restoration.
12121845
Automatic image orientation detection
We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.
5809817
Efficient Kernel Machines Using the Improved Fast Gauss Transform
The computation and memory required for kernel machines with N training samples is at least O(N2). Such a complexity is significant even for moderate size problems and is prohibitive for large datasets. We present an approximation technique based on the improved fast Gauss transform to reduce the computation to O(N). We also give an error bound for the approximation, and provide experimental results on the UCI datasets.
7211602
Improved fast gauss transform and efficient kernel density estimation
Evaluating sums of multivariate Gaussians is a common computational task in computer vision and pattern recognition, including in the general and powerful kernel density estimation technique. The quadratic computational complexity of the summation is a significant barrier to the scalability of this algorithm to practical applications. The fast Gauss transform (FGT) has successfully accelerated the kernel density estimation to linear running time for low-dimensional problems. Unfortunately, the cost of a direct extension of the FGT to higher-dimensional problems grows exponentially with dimension, making it impractical for dimensions above 3. We develop an improved fast Gauss transform to efficiently estimate sums of Gaussians in higher dimensions, where a new multivariate expansion scheme and an adaptive space subdivision technique dramatically improve the performance. The improved FGT has been applied to the mean shift algorithm achieving linear computational complexity. Experimental results demonstrate the efficiency and effectiveness of our algorithm.
43280075
Optimizations of Data Distribution Localities in Cluster Grid Environments
The advent of widely interconnected computing resources introduces the technologies of ubiquitous computing, peer to peer computing and grid computing. In this paper, we present an efficient data distribution scheme for optimizing data localities of SPMD data parallel programs on cluster grid, a typical computational grid environment consists of several clusters located in multiple campuses that distributed globally over the Internet. Because of the Internet infrastructure of cluster grid, the communication overhead becomes as key factor to the performance of parallel applications. Effectiveness of the proposed distribution mechanism is to reduce inter-cluster communication overheads and to speed the execution of data parallel programs in the underlying distributed cluster grid. The theoretical analysis and experimental results show improvement of communication costs and scalable of the proposed techniques on different hierarchical cluster grids.
15657957
Automatic Summarization of Chinese and English
As a result of the rapid growth in Internet access, significantly more information has become available online in real time. However, there is not sufficient time for users to read large volumes of information and make decisions accordingly. The problem of information-overloading can be resolved through the application of automatic summarization. Many summarization systems for documents in different languages have been implemented. However, the performance of summarization system on documents in different languages has not yet been investigated. In this paper, we compare the result of fractal summarization technique on parallel documents in Chinese and English. The grammatical and lexical differences between Chinese and English have significant effect on the summarization processes. Their impact on the performances of the summarization for the Chinese and English parallel documents is compared.
44571521
Designing computing platform for BioGrid
Grid environment allows sharing of a large amount of idle resources of compute and other available information. Accordingly, we have set up the BioGrid for computing resources by combining grid technology with parallel bioinformatics applicable software in erecting the BioGrid computing platform. In one instance for example, biology data which is heterogeneous and grows rapidly, we plan to integrate and share it using grid technology. In this paper, the FASTA applicable software is used to test and analyse the grid environment to reduce time and improve efficacy. The experimental results and performances of the bioinformatics tool use on the grid system are also presented in this paper.
7122598
An Efficient Parallel Loop Self-scheduling on Grid Environments
The approaches to deal with scheduling and load balancing on PC-based cluster systems are famous and well-known. Self-scheduling schemes, which are suitable for parallel loops with independent iterations on cluster computer system, they have been designed in the past. In this paper, we propose a new scheme that can adjust the scheduling parameter dynamically on an extremely heterogeneous PC-based cluster and grid computing environments in order to improve system performance. A grid computing environment consists of multiple PC-based clusters is constructed using Globus Toolkit and SUN Grid Engine middleware. The experimental results show that our scheduling can result in higher performance than other similar schemes.
45865876
Graph theoretic characterization and reliability of the generalized Boolean n-cube network
In this paper, a general class of Boolean n-cube structures is investigated. The interconnection is based on a mixed radix number system which results in a variety of generalized Boolean n-cube structures for a given number of processors N = x·2y, where x and y are positive integers. A number of interesting properties of the network are revealed. By a constructive method, the node connectivity of this network is found. Finally, we show that the graph is super-λ and is an optimal reliable structure for interconnection networks.