11363507
An efficient method of volume rendering for medical slices
The volume rendering method uses a special light source, slit-light source, to illuminate the volume and simulates the interactions of light with objects, such as light absorption, scattering and reflection. In comparison with previous methods, our method has several advantages. This includes flexibility for implementation, efficiency for computation, high reality for display and easiness of parallelization. The algorithm can be parallelized easily either using the data space subdivision method or the image space subdivision method. We describe some experimental results of applying our method to several sets of MRI and CT slices of human heads, which show the effectiveness of our method.<<ETX>>
17819380
Feature Selection for Complex Patterns
Feature selection is an important data preprocessing step in data mining and pattern recognition. Many algorithms have been proposed in the past for simple patterns that can be characterised by a single feature vector. Unfortunately, these algorithms are hardly applicable to what are referred as complex patterns that have to be described by a finite set of feature vectors. This paper addresses the problem of feature selection for the complex patterns. First, we formulated the calculation of mutual information for complex patterns based on Gaussian mixture model. A hybrid feature selection algorithm is then proposed based on the formulated mutual information calculation (filter) and Baysian classification (wrapper). Experimental results on XM2VTS speaker recognition database have not only verified the performance of the proposed algorithm, but also demonstrated that traditional feature selection algorithms designed for simple patterns would perform poorly for complex patterns.
7583260
Simultaneous MAP estimation of inhomogeneity and segmentation of brain tissues from MR images
Intrascan and interscan intensity inhomogeneities have been identified as a common source of making many advanced segmentation techniques fail to produce satisfactory results in separating brains tissues from multi-spectral magnetic resonance (MR) images. A common solution is to correct the inhomogeneity before applying the segmentation techniques. This paper presents a method that is able to achieve simultaneous semi-supervised MAP (maximum a-posterior probability) estimation of the inhomogeneity field and segmentation of brain tissues, where the inhomogeneity is parameterized. Our method can incorporate any available incomplete training data and their contribution can be controlled in a flexible manner and therefore the segmentation of the brain tissues can be optimised. Experiments on both simulated and real MR images have demonstrated that the proposed method estimated the inhomogeneity field accurately and improved the segmentation.
12496830
A Fast Algorithm for Color Image Segmentation
Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering in the lower layer (higher resolution). Experiments have shown that the proposed algorithm is significantly faster than the original K-means algorithm while producing comparable segmentation results
6293846
Cryptographic Key Generation from Biometric Data Using Lattice Mapping
Crypto-biometric systems are recently emerging as an effective process of key management to address the security weakness of conventional key release systems using pass-codes, tokens or pattern recognition based biometrics. This paper presents a lattice mapping based fuzzy commitment method for cryptographic key generation from biometric data. The proposed method not only outputs high entropy keys, but also conceals the original biometric data such that it is impossible to recover the biometric data even when the stored information in the system is open to an attacker. Simulated results have demonstrated that its authentication accuracy is comparable to the well-known k-nearest neighbour classification
26165513
Optimal Image Watermark Decoding
Not much has been done in utilizing the available information at the decoder to optimize the decoding performance of watermarking systems. This paper focuses on analyzing different decoding methods, namely, Minimum Distance, Maximum Likelihood and Maximum a-posteriori decoding given varying information at the decoder in the blind detection context. Specifically, we propose to employ Markov random fields to model the prior information given the embedded message is a structured logo. The application of these decoding methods in Quantization Index Modulation systems shows that the decoding performance can be improved by Maximum Likelihood decoding that exploits the property of the attack and Maximum a-posteriori decoding that utilizes the modeled prior information in addition to the property of the attack.
7023282
Empty space skipping and occlusion clipping for texture-based volume rendering
We propose methods to accelerate texture-based volume rendering by skipping invisible voxels. We partition the volume into sub-volumes, each containing voxels with similar properties. Sub-volumes composed of only voxels mapped to empty by the transfer function are skipped. To render the adaptively partitioned sub-volumes in visibility order, we reorganize them into an orthogonal BSP tree. We also present an algorithm that computes incrementally the intersection of the volume with the slicing planes, which avoids the overhead of the intersection and texture coordinates computation introduced by the partitioning. Rendering with empty space skipping is 2 to 5 times faster than without it. To skip occluded voxels, we introduce the concept of orthogonal opacity map, that simplifies the transformation between the volume coordinates and the opacity map coordinates, which is intensively used for occlusion detection. The map is updated efficiently by the GPU. The sub-volumes are then culled and clipped against the opacity map. We also present a method that adaptively adjusts the optimal number of the opacity map updates. With occlusion clipping, about 60% of non-empty voxels can be skipped and an additional 80% speedup on average is gained for iso-surface-like rendering.
18767466
Spectra of Regular Graphs and Hypergraphs and Orthogonal Polynomials
Abstract In this paper we study the distribution of eigenvalues of regular graphs, regular hypergraphs, and biregular bipartite graphs of given girth by considering the polynomials orthogonal with respect to the measures attached to the spectra of such graphs and to the continuous spectra of their ‘universal covers’. Our estimates are tight for Biggs graphs and generalized polygons. We also give an application to the distribution of eigenvalues of Hecke operators acting on weight 2 cusp forms for certain congruence subgroups.
5294188
Method for identifying transcription factor binding sites in yeast
MOTIVATION
Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit.


RESULTS
We develop a TFBS identification method, TFBSfinder, which utilizes several data sources, including DNA sequences, phylogenetic information, microarray data and ChIP-chip data. For a TF, TFBSfinder rigorously selects a set of reliable target genes and a set of non-target genes (as a background set) to find overrepresented and conserved motifs in target genes. A new metric for measuring the degree of conservation at a binding site across species and methods for clustering motifs and for inferring position weight matrices are proposed. For synthetic data and yeast cell cycle TFs, TFBSfinder identifies motifs that are highly similar to known consensuses. Moreover, TFBSfinder outperforms well-known methods.


AVAILABILITY
http://cg1.iis.sinica.edu.tw/~TFBSfinder/.
10927916
Integration of Image Matching and Classification for Multimedia Navigation
With the recent explosive growth in the volume of images on the World-Wide Web, it has become increasingly difficult to search for images of interests. The classification of images helps users to access a large image collection efficiently. Classification reduces search space by filtering out unrelated images. Classification also allows for more user-friendly interfaces: users can better visualize easily result space by browsing the representative images of the candidates. In this paper, we present a technique for image classification based on color, shape and composition using the primary objects. We apply this classification technique in image matching for image retrieval on the Web. Our experimental results show that this approach can maintain 73% of recall by searching only 24% of the whole data set. We also show how we apply such technique to assist users in navigation.
35066294
A 2-adic approach to the analysis of cyclic codes
This paper describes how 2-adic numbers can be used to analyze the structure of binary cyclic codes and of cyclic codes defined over Z/sub 2(a)/, a/spl ges/2, the ring of integers modulo 2/sup a/. It provides a 2-adic proof of a theorem of McEliece that characterizes the possible Hamming weights that can appear in a binary cyclic code. A generalization of this theorem is derived that applies to cyclic codes over Z/sub 2(a)/ that are obtained from binary cyclic codes by a sequence of Hensel lifts. This generalization characterizes the number of times a residue modulo 2/sup a/ appears as a component of an arbitrary codeword in the cyclic code. The limit of the sequence of Hensel lifts is a universal code defined over the 2-adic integers. This code was first introduced by Calderbank and Sloane (1995), and is the main subject of this paper. Binary cyclic codes and cyclic codes over Z/sub 2(a)/ are obtained from these universal codes by reduction modulo some power of 2. A special case of particular interest is cyclic codes over Z/sub 4/ that are obtained from binary cyclic codes by means of a single Hensel lift. The binary images of such codes under the Gray isometry include the Kerdock, Preparata, and Delsart-Goethals codes. These are nonlinear binary codes that contain more codewords than any linear code presently known. Fundamental understanding of the composition of codewords in cyclic codes over Z/sub 4/ is central to the search for more families of optimal codes. This paper also constructs even unimodular lattices from the Hensel lift of extended binary cyclic codes that are self-dual with all Hamming weights divisible by 4. The Leech lattice arises in this way as do extremal lattices in dimensions 32 through 48.