Search results for "COMPUTATION"
showing 10 items of 7362 documents
On the use of generalized harmonic means in image processing using multiresolution algorithms
2019
In this paper we design a family of cell-average nonlinear prediction operators that make use of the generalized harmonic means and we apply the resulting schemes to image processing. The new famil...
Pattern languages with and without erasing
1994
The paper deals with the problems related to finding a pattern common to all words in a given set. We restrict our attention to patterns expressible by the use of variables ranging over words. Two essentially different cases result, depending on whether or not the empty word belongs to the range. We investigate equivalence and inclusion problems, patterns descriptive for a set, as well as some complexity issues. The inclusion problem between two pattern languages turns out to be of fundamental theoretical importance because many problems in the classical combinatorics of words can be reduced to it.
Revisited mixed-value method via symmetric BEM in the substructuring approach
2012
Abstract Within the Symmetric Boundary Element Method, the mixed-value analysis is re-formulated. This analysis method contemplates the subdivision of the body into substructures having interface kinematical and mechanical quantities. For each substructure an elasticity equation, connecting weighted displacements and tractions to nodal displacements and forces of the same interface boundary and to external action vector, is introduced. The assembly of the substructures is performed through both the strong and weak regularity conditions of the displacements and tractions. We obtain the solving equations where the compatibility and the equilibrium are guaranteed in the domain Ω for the use of…
Alternative method for binary shape alignment of non-symmetrical shapes based on minimal enclosing box
2012
Proposed is a novel method based on the minimal enclosing box (MEB) to determine the canonical orientation associated with a three-dimensional binary shape. It is suggested that, when the shape has no clear distinctive features and two or more of the eigenvalues are similar, this method is more suitable than the commonly used method based on principal component analysis (PCA). An experiment is performed with shapes of human livers by measuring the degree on which a prototypical image (atlas) matches to a new shape after alignment by PCA, minimal area projection (MAP), and MEB showing that in this case MEB outperforms the usual PCA-based alignment method and also the MAP method.
WITHDRAWN: An efficient multiscale algorithm
2016
The publisher regrets that this article has been temporarily removed. The reason for the overturn of the decision on ACHA-16-25 from Acceptance to Rejection is: One of the colleagues of the authors, Elisa Francomano, claims that the authors submitted the manuscript to ACHA without her knowledge and omitting her as one of the authors. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy .
Using Attribute Grammars for Description of Inductive Inference Search Space
1998
The problem of practically feasible inductive inference of functions or other objects that can be described by means of an attribute grammar is studied in this paper. In our approach based on attribute grammars various kinds of knowledge about the object to be found can be encoded, ranging from usual input/output examples to assumptions about unknown object's syntactic structure to some dynamic object's properties. We present theoretical results as well as describe the architecture of a practical inductive synthesis system based on theoretical findings.
Prediction of Disease–lncRNA Associations via Machine Learning and Big Data Approaches
2021
This chapter introduces long non-coding RNAs and their role in the occurrence and progress of diseases. The discovery of novel lncRNA-disease associations may provide valuable input to the understanding of disease mechanisms at the lncRNA level, as well as to the detection of biomarkers for disease diagnosis, treatment, prognosis, and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of potential disease-lncRNA associations can effectively decrease the time and cost of biological experiments. We first review the main computatio…
<title>Fast motion estimation based on spatio-temporal Gabor filters: parallel implementation on multi-DSP</title>
2000
The aim of our work is to implement a system of motion estimation in image sequences processing on DSP's: fast motion estimation based on Gabor spatio-temporal filters. Our approach consists to calculate optical flow using an energy-based method, named combined filtering which associates the energetic responses of Gabor spatio- temporal filters organized in triads. For this purpose, we applicate a technique developed by the Laboratory LIS in France, inspired from the architecture of Heeger. To reduce the computation time, we present also a parallel implementation of the algorithm on a multi-DSP architecture using SynDEx tool which is a programming environment to generate optimized distribut…
Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling
2008
Automated video surveillance applications require accurate separation of foreground and background image content. Cost-sensitive embedded platforms place real-time performance and efficiency demands on techniques to accomplish this task. In this chapter, we evaluate pixel-level foreground extraction techniques for a low-cost integrated surveillance system. We introduce a new adaptive background modeling technique, multimodal mean (MM), which balances accuracy, performance, and efficiency to meet embedded system requirements. Our evaluation compares several pixel-level foreground extraction techniques in terms of their computation and storage requirements, and functional accuracy for three r…
An inductive inference approach to classification
1994
Abstract In this paper we introduce a formal framework for investigating the relationship of inductive inference and the task of classification. We give the first results on the relationship between functions that can be identified in the limit and functions that can be acquired from unclassified objects only. Moreover, we present results on the complexity of classification functions and the preconditions necessary in order to allow the computation of such functions.