40658443
A Parallel Algorithm for Object Contour Extraction Within a Spiral Architecture
949912
Replicated Shared Object Model for Edge Detection with Spiral Architecture
Edge detection in computer vision and image processing is a process which detects one kind of significant features appearing as discontinuities in intensities. A parallel edge detection algorithm based on Spiral Architeture is designed in this paper by a Replicated Shared Object Model (RSOM). We use this approach to meet the need of replicated object management, update propagation, underlying communication and consistency maintenance among replicated objects.
25659954
An Improved Algorithm for the Planar 3-Cut Problem
Abstract A 3-cut of a connected graph G is a subset of edges which, when deleted, separates G into three connected components. In this paper we present an O ( n log n ) algorithm for finding a minimum 3-cut in planar graphs. Our algorithm improves the best previously known algorithm for the problem by an O( n log n) factor.
2934981
On Finding the Rectangular Duals of Planar Triangular Graphs
This paper presents a new linear-time algorithm for finding rectangular duals of planar triangular graphs. The algorithm is conceptually simpler than the previously known algorithm. The coordinates of the rectangular dual constructed by the new algorithm are integers and carry clear combinatorial meaning.
1623769
Orthogonal locality preserving indexing
We consider the problem of document indexing and representation. Recently, Locality Preserving Indexing (LPI) was proposed for learning a compact document subspace. Different from Latent Semantic Indexing which is optimal in the sense of global Euclidean structure, LPI is optimal in the sense of local manifold structure. However, LPI is extremely sensitive to the number of dimensions. This makes it difficult to estimate the intrinsic dimensionality, while inaccurately estimated dimensionality would drastically degrade its performance. One reason leading to this problem is that LPI is non-orthogonal. Non-orthogonality distorts the metric structure of the document space. In this paper, we propose a new algorithm called Orthogonal LPI. Orthogonal LPI iteratively computes the mutually orthogonal basis functions which respect the local geometrical structure. Moreover, our empirical study shows that OLPI can have more locality preserving power than LPI. We compare the new algorithm to LSI and LPI. Extensive experimental results show that Orthogonal LPI obtains better performance than both LSI and LPI. More crucially, it is insensitive to the number of dimensions, which makes it an efficient data preprocessing method for text clustering, classification, retrieval, etc.
2162472
Block-level link analysis
Link Analysis has shown great potential in improving the performance of web search. PageRank and HITS are two of the most popular algorithms. Most of the existing link analysis algorithms treat a web page as a single node in the web graph. However, in most cases, a web page contains multiple semantics and hence the web page might not be considered as the atomic node. In this paper, the web page is partitioned into blocks using the vision-based page segmentation algorithm. By extracting the page-to-block, block-to-page relationships from link structure and page layout analysis, we can construct a semantic graph over the WWW such that each node exactly represents a single semantic topic. This graph can better describe the semantic structure of the web. Based on block-level link analysis, we proposed two new algorithms, Block Level PageRank and Block Level HITS, whose performances we study extensively using web data.
16128617
CyberSeer: 3D audio-visual immersion for network security and management
Large complex networks have become an inseparable part of modern society. However, very little has been done to develop tools to manage and ensure the security of such networks. Network operators continue to slave over endless daily logs and alerts in a struggle to keep networks operational. Perhaps the most formidable enemy of network operations today is the volume of management data that must be perused. Expensive commercial products attempt to visualize data but with limited utility, as witnessed by the prevailing use of command-line interfaces and homegrown scripts. In addition to data collection tools, operators need to immediately observe and debug the effects of their actions; yet that information is buried deep in the data that pours daily from monitoring equipment. Thus, they need better ways to abstract network events and better, more informative ways to render them.
 In this work we first propose a new approach for abstracting network information, namely spectral representation. Second, we introduce immersive spatial audio representations of network events. Third, we introduce 3D interactive auto-stereoscopic (AS) displays for visual representations. We integrate the three techniques as follows. We use spectral techniques to extract complex events buried inside voluminous network traces and logs. Then, we create a desktop interactive immersive auto-stereoscopic 3D environment that is seamlessly integrated with multi-channel spatially rendered audio to render such events in a far more human-friendly fashion.