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About 10,000 papers for “Structural pattern”

STRUCTURAL PATTERN RECOGNITION IN MOLECULES

SREEPAD HR · International Journal of Machine Intelligence · 2011

Structural pattern recognition in case of molecules is an important task in the field of bio-engineering. Several techniques are employed in order to get the exact structural conformation and structural parameters of molecules. Present paper discusses some of the available techniques such as Fourier Infra-Red Spectrocopy, Raman Spectroscopy and Theoretical Structure simulation which are employed in this technologically important field. An attempt has been made to look for the exact structural conformation in case of Polyformaldehyde using First-principles calculations based on Density Functional Theory.

Cited by 2

Grammaticalization and Diachronic Structural Pattern

Concepción Company Company · Catalan Journal of Linguistics · 2024

The paper posits the hypothesis that if a form or construction entering a process of grammaticalization has a diachronic structural pattern, the grammaticalization may become entrenched and strengthened; it can recategorize and move easily into other grammatical areas, can make more complex diachronic paths, and will progress better than a change without a pattern. The paper poses two types of structural pattern: an intraparadigmatic one and an interparadigmatic one. The empirical evidence is the grammaticalization of the predication dice que into the evidential discourse marker dizque and later into the adjective dizque. The paper examines two patterns supporting this grammaticalization: first, the various grammaticalizations undergone by the verb decir into different discourse particles, an intraparadigmatic pattern, and second, three predications that undergone the same diachronic path: que es que > quesque; puede que > pueque; vaya > ¡vaya!, an interparadigmatic pattern.

Cited by 0Open access

Designing Pattern Matching Algorithms by Exploiting Structural Pattern Properties

Ramesh Hariharan · 1994

This thesis presents algorithms and, in some cases, lower bounds for some fundamental pattern matching problems. In all cases, the algorithms are obtained by understanding and strongly exploiting structural pattern properties. The following results are obtained. Exact Complexity of String Matching: We consider the question of how many character comparisons are needed to nd all occurrences of a pattern string of length m in a text string of length n. We show an almost tight upper bound of the form n+O(n=m) character comparisons, following preprocessing. Speci cally, we show an upper bound of n+ (n m) character comparisons. This bound is achieved by an on-line algorithm which performs O(n) work in total, requires O(m) space and O(m ) time for preprocessing. The following lower bounds are also shown: for on-line algorithms, a bound of n+ (n m) character comparisons for m = 35 + 36k, for any integer k 1, and for general algorithms, a bound of n + m+3 character comparisons, for m = 2k + 1, for any integer k 1.

Cited by 0

Structural, Syntactic, and Statistical Pattern Recognition

Joint IAPR International Workshops on Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition and 2024 Venedig, Torsello, Andrea, Rossi, Luca et al. · Lecture notes in computer science · 2025

Cited by 0

Structural Pattern Analysis for Texture Synthesis

Yizhe Li, Guoliang Chen, Qilong Zheng · 2006

Sample based texture synthesis allows the generation of arbitrarily sized textures based on a small sample image. One main drawback of the technique is that a new pixel or patch is selected only considering local information in the generated texture. Thus structural error can't assure to be avoided for structural textures. To accelerate texture synthesis process and to avoid structural mismatch in the output picture, we present in this paper a structural pattern analysis (SPA) algorithm and a texture synthesis method using the algorithm. The pattern analysis tool also quantizes the texture randomness in macro scope view. It can be used in other applications such as automatic texture classification and quality evaluation of synthesized texture. We illustrate our result with some examples

Cited by 1

Recognition, Structural Pattern Recognition, Neural Networks

Seema Asht, Rajeshwar Dass · 2015

Abstract – Pattern Recognition has attracted the attention of researchers in last few decades as a machine learning approach due to its wide spread application areas. The application area includes medicine, communications, automations, military intelligence, data mining, bioinformatics, document classification, speech recognition, business and many others. In this review paper various approaches of Pattern Recognition have been presented and their pros-cons, application specific paradigm has been shown. On the basis of survey, pattern recognition techniques can be categorized into six parts. These

Cited by 0

Primitives extraction for structural pattern recognition

Koo, Chee Keong. · DR-NTU (Nanyang Technological University) · 2008

This dissertation presents the investigations conducted on the various corner detection operators in terms of their working principles and detection algorithms. These operators are broadly classified under two categories: ones that require pre-processing of the images of the objects which are in binary format and ones that are performed directly on the images of objects in grey level without requiring the additional pre-processing steps. In view of these, the pre-processing operations such as noise reductions, edge detection and edge thinning are investigated as well. This is to provide a reference point for the comparison on the effect of these pre-processing operations on the comer operators. The results are then compared with the corner operators that do not require these pre-processing. Images of objects with different sizes, texture, shapes and under various background conditions are used in the investigations. This is to provide a comparison platform in terms of the response of these comers operators towards different contrast, shape and noise.

Cited by 0

Spectral Geometry for Structural Pattern Recognition

El Ghawalby, Heyayda · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · 2010

Graphs are used pervasively in computer science as representations of data with a network or relational structure, where the graph structure provides a flexible representation such that there is no fixed dimensionality for objects.
\nHowever, the analysis of data in this form has proved an elusive problem; for instance, it suffers from the robustness to structural noise.
\nOne way to circumvent this problem is to embed the nodes of a graph in a vector space and to study the properties of the point distribution that results from the embedding. This is a problem that arises in a number of areas including manifold learning theory and graph-drawing.
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\nIn this thesis, our first contribution is to investigate the heat kernel embedding as a route to computing geometric characterisations of graphs. The reason for turning to the heat kernel is that it encapsulates information concerning the distribution of path lengths and hence node affinities on the graph.
\nThe heat kernel of the graph is found by exponentiating the Laplacian eigensystem over time. The matrix of embedding co-ordinates for the nodes of the graph is obtained by performing a Young-Householder decomposition on the heat kernel. Once the embedding of its nodes is to hand we proceed to characterise a graph in a geometric manner.
\nWith the embeddings to hand, we establish a graph characterization based on differential geometry by computing sets of curvatures associated with the graph nodes, edges and triangular faces.
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\nThe second contribution comes from the need to solve the problem that arise in the processing of a noisy data over a graph. The Principal difficulty of this task, is how to preserve the geometrical structures existing in the initial data.
\nBringing together several, distinct concepts that have received some independent recent attention in machine learning; we propose a framework to regularize real-valued or vector-valued functions on weighted graphs of arbitrary topology.
\nThe first of these is deduced from the concepts of the spectral graph theory that have been applied to a wide range of clustering and classification tasks over the last decades taking in consideration the properties of the graph \\(p\\)-Laplacian as a nonlinear extension of the usual graph Laplacian.
\nThe second one is the geometric point of view comes from the heat kernel embedding of the graph into a manifold. In these techniques we use the geometry of the manifold by assuming that it has the geometric structure of a Riemannian manifold.
\nThe third important conceptual framework comes from the manifold regularization which extends the classical framework of regularization in the sense of reproducing Hilbert Spaces to exploit the geometry of the embedded set of points.
\nThe proposed framework, based on the \\(p\\)-Laplacian operators considering minimizing a weighted sum of two energy terms: a regularization one and an additional approximation term which helps to avoid the shrinkage effects obtained during the regularization process.
\nThe data are structured by functions depending on data features, the curvature attributes associated with the geometric embedding of the graph.
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\nThe third contribution is inspired by the concepts and techniques of the graph calculus of partial differential functions.
\nWe propose a new approach for embedding graphs on pseudo-Riemannian manifolds based on the wave kernel which is the solution of the wave equation on the edges of a graph.
\nThe eigensystem of the wave-kernel is determined by the eigenvalues and the eigenfunctions of the normalized adjacency matrix and can be used to solve the edge-based wave equation. By factorising the Gram-matrix for the wave-kernel, we determine the embedding co-ordinates for nodes under the wave-kernel.
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\nThe techniques proposed through this thesis are investigated as a means of gauging the similarity of graphs.
\nWe experiment on sets of graphs representing the proximity of image features in different views of different objects in three different datasets namely, the York model house, the COIL-20 and the TOY databases.

Cited by 0Open access

JuliusCrisis Event JuliusOperational Pattern JuliusStrategy Structural

Jorge Sepulveda-haugen, Julius S. A · 2015

The intention of this paper is to incorporate system thinking actively to the Strategic and Organizational consultant process and to create a basic framework for the use of smart software that will allow us to accelerate the learning and decision-making process. Our approach trough JuliusProcess is to make a basic analogy between the evolution of strategic planning process and system thinking, as it shows below:

Cited by 0

Kernel-Based Structural Binary Pattern Tracking

Daehwan Kim, Hyo-Kak Kim, Seung‐Jun Lee et al. · IEEE Transactions on Circuits and Systems for Video Technology · 2014

In this paper, we propose a new pattern model, called the structural binary pattern (SBP) model, for object tracking. For the proposed SBP model, we introduce an alternate thresholding scheme to generate a set of multiple SBPs. The SBP encodes not only the binary pattern consisting of binarized differences between the average intensities of subregions within the target region, but also the spatial configuration of the subregions. With the proposed SBP model, we define a metric for similarity between the SBP models from the target and candidate for target localization, which is based on an isotropic kernel weighted Hamming distance. To further improve the tracking performance, we employ a color-based tracking method along with the SBP-based tracking method. The experimental results show that the proposed algorithm exhibits the better performance even when the object being tracked confronts drastic illumination changes, partial occlusion, a similar colored background, or low illumination as compared with conventional tracking methods.

Cited by 12