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.
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.
16416021
Analyzing the Terrorist Social Networks with Visualization Tools
Analysis of terrorist social networks is essential for discovering knowledge about the structure of terrorist organizations. Such knowledge is important for developing effective combating strategies against terrorism. Visualization of a network with the support of social network analysis techniques greatly facilitates the inspection of the network global structure. However, its usefulness becomes limited when the size and complexity of the network increase. In this work, we develop two interactive visualization techniques for complex terrorist social networks: fisheye views and fractal views. Both techniques facilitate the exploration of complex networks by allowing a user to select one or more focus points and dynamically adjusting the graphical layout and abstraction level to enhance the view of regions of interest. Combining the two techniques can effectively help an investigator to recognize patterns previously unreadable in the normal display due to the network complexity. Case studies are presented to illustrate how such visualization tools are capable to extract the hidden relationships among terrorists in the network through user interactions. Experiment was conducted to evaluate the performance of the visualization techniques.
33160619
Characterization Of Electroosmotic Flow In Microchannels
This paper presents experimental and theoretical studies of the electroosmotic flow in microchannels. The current monitoring method is used to determine the average flow velocity and the ζ potential by measuring the current-time relationship during electrolyte displacement process. A rigorous mathematical model is developed to describe the electrokinetic transport phenomena in such displacement process. Experiments were carried out for the NaCl electrolyte to evaluate the electroosmotic velocity and the ζ potential under various experimental conditions. The theoretical predictions for the current-time relationship are compared with measured results.
818365
Effects of luminance quantization error on color image processing
A common approach to color image processing is to apply monochrome techniques to the quantized intensity component. However, the quantization error in the intermediate intensity image propagates to the final processed image. This can lead to significant distortion, depending on the input RGB values and on the particular image processing function being applied. A theoretical analysis of the worst-case quantization error is presented. Experimental results with histogram equalization demonstrate how the histogram resolution affects the performance.
39460490
Localization Techniques for Cluster-Based Data Grid
In this paper, we present an efficient method for optimizing localities of data distribution when executing data parallel applications. The data to logical grid nodes mapping technique is employed to enhance the performance of parallel programs on cluster grid. Cluster grid is a typical computational grid environment consists of several clusters located in multiple campuses that are distributed globally over the Internet. Objective of the proposed technique 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.
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.