14826038
A Spatio-Temporal Modeling Method for Shape Representation
The spherical harmonic (SPHARM) description is a powerful surface modeling technique that can model arbitrarily shaped but simply connected three dimensional (3D) objects. Because SPHARM based 3D models can derive functional information analysis and classify different pathological symptoms, it has been used in many applications in biomedical image computing. There is an urgent requirement for efficient spatio-temporal shape modeling to represent the dynamic anatomical structures in many applications (e.g., medical image analysis, geospatial information systems). In this paper we propose a novel real spherical harmonics based spatio-temporal shape modeling method to efficiently and flexibly represent the shapes sequence of anatomical structures in medical images. Our method works well on the simply connected 3D objects and the effectiveness of our approach is demonstrated through theoretic and experimental exploration of a set of medical image applications. Furthermore, an evaluation criterion for spatio-temporal shape modeling efficiency is proposed and the comparison results showed the good performance of our method.
2480556
Data Brokers: Building Collections through Automated Negotiation
Collecting digital materials is time-consuming and can gain from automation. Since each source – and even each acquisition – may involve a separate negotiation of terms, a collector may prefer to use a broker to represent his interests with owners. This paper describes the Data Broker Framework (DBF), which is designed to automate the process of digital object acquisition. For each acquisition, a negotiation agent is assigned to negotiate on the collector’s behalf, choosing from strategies in a strategy pool to automatically handle most bargaining cases and decide what to accept and what counteroffers to propose. We introduce NOODLE (Negotiation OntOlogy Description LanguagE) to formally specify terms in the negotiation domain.
6859814
Three-dimensional Models for Cardiac Bioelectricity Simulation: Cell to Organ
Computer models of the heart have become useful tools in improving understanding of cardiac electrical activation at the cellular level, and in analyzing mechanisms of arrhythmias and antiarrhythmic therapies. In this paper, we propose to use spherical harmonic (SPHARM) surface reconstruction and alignment methods to generate curvilinear meshes for finite element models of ventricular anatomy which incorporate detailed structural information. The endocardial and epicardial surfaces are defined by SPHARM, and the volume between these surfaces is divided into nested shells. Consequently, the resulting mesh comprises hexahedral elements. Using this novel SPHARM-based meshing method, the transmembrane potential propagation is simulated, based on FitzHugh—Nagumo reaction—diffusion equations. The dynamic electrical activation propagations are simulated on realistic cardiac data during a heart cycle. The obtained results clearly demonstrate an accurate resolution of the cardiac potential during the excitation. Our novel curvilinear mesh models have a great potential to be used in an improved simulation of cardiovascular pathologies for testing therapy strategies and planning interventions.
15158980
Large-Scale Modeling of Parametric Surfaces Using Spherical Harmonics
We present an approach for large-scale modeling of parametric surfaces using spherical harmonics (SHs). A standard least square fitting (LSF) method for SH expansion is not scalable and cannot accurately model large 3D surfaces. We propose an iterative residual fitting (IRF) algorithm, and demonstrate its effectiveness and scalability in creating accurate SH models for large 3D surfaces. These large-scale and accurate parametric models can be used in many applications in computer vision, graphics, and biomedical imaging. As a simple extension of LSF, IRF is very easy to implement and requires few machine resources.
33719729
New Algorithms for Efficient Mining of Association Rules
Abstract Discovery of association rules is an important data mining task. Several algorithms have been proposed to solve this problem. Most of them require repeated passes over the database, which incurs huge I/O overhead and high synchronization expense in parallel cases. There are a few algorithms trying to reduce these costs. But they contain weaknesses such as often requiring high pre-processing cost to get a vertical database layout, containing much redundant computation in parallel cases, and so on. We propose new association mining algorithms to overcome the above drawbacks, through minimizing the I/O cost and effectively controlling the computation cost. Experiments on well-known synthetic data show that our algorithms consistently outperform a priori, one of the best algorithms for association mining, by factors ranging from 2 to 4 in most cases. Also, our algorithms are very easy to be parallelized, and we present a parallelization for them based on a shared-nothing architecture. We observe that our parallelization develops the parallelism more sufficiently than two of the best existing parallel algorithms.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.
31075540
Reducing multiclass cancer classification to binary by output coding and SVM
Multiclass cancer classification based on microarray data is presented. The binary classifiers used combine support vector machines with a generalized output-coding scheme. Different coding strategies, decoding functions and feature selection methods are incorporated and validated on two cancer datasets: GCM and ALL. Using random coding strategy and recursive feature elimination, the testing accuracy achieved is as high as 83% on GCM data with 14 classes. Comparing with other classification methods, our method is superior in classificatory performance.
11412243
Customizing computation accelerators for extensible multi-issue processors with effective optimization techniques
Compared with single-issue general purpose processors (GPPs), extensible multi-issue/VLIW processors can exploit instruction - level parallelism, which are more suitable for computation intensive tasks. Moreover, they offer the ability of customizing computation accelerators for an application domain. In this paper, we present an automated methodology that customizes computation accelerators for the multi-issue/VLIW extensible processors, where several techniques are also proposed to optimize the design of an accelerator.
14935665
Optimal Matrix Computing Using Vector Division with Sub-word Parallel
Available computing is expected to be effective and efficient in ubiquitous multimedia services. And, matrix multiplication is particular to ubiquitous computing such as image transformation so that it is urgent to exploit potential parallelism as well as data reuse in matrix multiplication. Therefore, based on synchronized data triggered architecture (SDTA), we propose a method of parallel vector division (PVD) as well as sub-word parallel (SWP) supported on SDTA to optimize the matrix computation on specified architecture. Experimental results show that PVD with SWP is suitable for matrix multiplication on SDTA instruction set architecture. Besides, we could extend the proposed model into other data-intensive computing on SDTA.
21499977
Predicate Analysis Based on Path Information
Predicated execution leads new challenges to traditional optimizers. Limited by predicate representations, current predicate analysis methods could only perform locally or have low efficiency. A new representation based on path information is proposed in this paper, based on which a global predicate relation query system is constructed. Experiment results indicate that precise and efficient global predicate analysis could be achieved with this system and the performance of optimized codes can also be improved.
29851215
Unified probabilistic models for face recognition from a single example image per person
This paper presents a new technique of unified probabilistic models for face recognition from only one single example image per person. The unified models, trained on an obtained training set with multiple samples per person, are used to recognize facial images from another disjoint database with a single sample per person. Variations between facial images are modeled as two unified probabilistic models: within-class variations and between-class variations. Gaussian Mixture Models are used to approximate the distributions of the two variations and exploit a classifier combination method to improve the performance. Extensive experimental results on the ORL face database and the authors' database (the ICT-JDL database) including totally 1,750 facial images of 350 individuals demonstrate that the proposed technique, compared with traditional eigenface method and some well-known traditional algorithms, is a significantly more effective and robust approach, for face recognition.