40089095
Ray-guided global optimization method for training neural networks
Abstract A novel method for globally searching to find the good minima is proposed in this paper. Starting from a local minimum, the weight space around it is scanned with the process being guided by terrain-independent emanating rays. During the search, starting points for further exploration are identified and used to find corresponding local minima. Based on the correct classification rate (CCR) on the validation data, the best minimum is found.
8311095
Classifying image texture with statistical landscape features
This paper proposes to use three-dimensional information derived from the graph of an image function for texture description. The graph of an image function is a rumpled surface appearing like a landscape. To characterize the texture through this landscape, six novel texture feature curves based on the statistics of the geometrical and topological properties of the solids shaped by the graph and a variable horizontal plane are used. The proposed statistical landscape features have been shown by systematic experiments to offer very low error rates on a large subset of the Brodatz texture album having excluded some nonhomogeneous images, the entire Brodatz texture set, as well as the VisTex texture collection.
205904713
Fuzzy aggregated connectedness for image segmentation
Fuzzy methodology has been successfully applied to image analysis and pattern recognition since the theory of fuzzy set was 0rst introduced by Zadeh [1]. It is believed that the fuzzy set theory models the inherent imprecision in images. Fuzzy geometrical approaches focus on the spatial measures and have attracted considerable research interest in handling the spatial uncertainty in images [2]. Fuzzy connectedness is an important topological property of images. The classical de0nition was given by Rosenfeld [3]. A modi0cation to the traditional concept of fuzzy connectedness was further proposed [4]. Based on the notion of fuzzy connectedness of image elements, fuzzy objects for n-dimensional digital spaces was exploited for image segmentation [5]. Fuzzy connectedness is a natural extension to the crisp connectedness and re:ects the imprecise topological structures of images. The algorithms in the literature usually compute the fuzzy connectedness with regard to the best path between a pair of points. This approach su;ers from high sensitivity to noise. Noise in corrupted images may misguide the detection of the best path and further in:uence the determination of fuzzy connectedness. We believe that this has mainly been due to the binarization in determining the best path. The information of a path is either fully preserved or discarded and this contradicts the concept of fuzzy sets. In this paper,
26820236
3-D IFS fractals as real-time graphics model
Abstract Fractals have recently been attracting increasing research interest. Unlike conventional geometrical figures, fractals have unlimited details—a potentially useful feature for artistic images. One way to generate fractals is to use Iterated Function Systems (IFS). There have been significant research efforts on 2-D IFS. Programs for rendering 2-D IFS fractals are readily available. 3-D IFS appear to be a natural extension but remain an area to be investigated. This paper explores the use of 3-D IFS for creating fascinating fractal scenes. An efficient coloring, lighting and mist effects scheme is proposed. A program using the proposed scheme has been developed that can render delicate scenes consisting of multiple IFS fractals in real-time on the PC.
16801553
Mini-Max Initialization for Function Approximation
No abstract available.
10274461
Pattern Classification Using Rectified Nearest Feature Line Segment
This paper proposes a new classification method termed Rectified Nearest Feature Line Segment (RNFLS). It overcomes the drawbacks of the original Nearest Feature Line (NFL) classifier and possesses a novel property that centralizes the probability density of the initial sample distribution, which significantly enhances the classification ability. Another remarkable merit is that RNFLS is applicable to complex problems such as two-spirals, which the original NFL cannot deal with properly. Experimental comparisons with NFL, NN(Nearest Neighbor), k-NN and NNL (Nearest Neighbor Line) using artificial and real-world datasets demonstrate that RNFLS offers the best performance.
11936833
The weakest specifunction
The weakest specifunction has been introduced to generalise the notions of weakest prespecification and weakest parallel environment. It calculates the weakest specification function whose value refines the target specification when applied to a given component; thus it forms the basis for compositional refinement, an essential ingredient in program derivation. But unlike those previous calculi it is able to deal with several unknown components simultaneously and hence has wider applicability. In this paper we extend the general theory of the weakest specifunction, identifying those spaces in which it behaves miraculously. The par-seq specifunction places an established component in parallel with a required component and the result in sequence with another required component to meet a given specification. We extend the study of the par-seq specifunction in the context of log s, an intermediate-level language for reactive computing in an abstraction of the PRAM and BSP models of computation, and provide a single complete law for its use in program derivation. The resulting calculus is applied to the derivation of a distributed algorithm for dynamic load balancing.
16524073
Partitioning of temporal planning problems in mixed space using the theory of extended saddle points
We study the partitioning of temporal planning problems formulated as mixed-integer nonlinear programming problems, develop methods to reduce the search space of partitioned subproblems, and propose algorithms for resolving unsatisfied global constraints. The algorithms are based on the necessary and sufficient extended saddle-point condition for constrained local minimization developed in this paper. When compared with the MIPS planner in solving some PDDL2.1 planning problems, our distributed implementation of MIPS shows significant improvements in time and quality.
6262105
A New Indexing Method for High Dimensional Dataset
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse of dimensionality” problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of “curse of dimensionality”; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.
6014524
Physically-based animation of volumetric objects
The paper presents a voxel-based animation technique which employs either a mass-spring model or a finite element model. Two volumetric objects, a voxelized chair and a scanned muscle, are used as case studies with the two different models. The mass-spring model is used to show an animation sequence of a falling and bouncing chair: wireframe display and volume rendering are used to display a real-time animation of the process. In addition, a real-time simulation is carried out by the finite element method (FEM) of a voxel-based multi-resolution muscle mesh. Two techniques, a direct integration and a simplified modal analysis method are discussed in the context of applying FEM for muscle deformation. Local deformation optimization with modal analysis for higher resolution muscle volumetric animation, which allows accurate prediction of muscle deformation changes, has been used. Physiological muscle force has been considered and a biomechanically-based 3D FEM muscle model has been implemented. Realistic animations have been produced based on the FEM simulation with various graphics techniques.
40831365
Mining Frequent Spatial Patterns in Image Databases
Mining useful patterns in image databases can not only reveal useful information to users but also help the task of data management. In this paper, we propose an image mining framework, Frequent Spatial Pattern mining in images (FSP), to mine frequent patterns located in a pair of spatial locations of images. A pattern in the FSP is associated with a pair of spatial locations and refers to the occurrence of the same image content in a set of images. This framework is designed to be general so as to accept different levels of representations of image content and different layout forms of spatial representations.