• Aucun résultat trouvé

Maximum Likelihood Curves for MultipleObjects Extraction: Application to RadiographicInspection for Weld Defects Detection

N/A
N/A
Info
Télécharger
Protected

Academic year: 2021

Partager "Maximum Likelihood Curves for MultipleObjects Extraction: Application to RadiographicInspection for Weld Defects Detection"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

International Journal on New Computer Architectures and Their Applications

Volume 1, Issue 1, 2011, Pages 210-222

Maximum Likelihood Curves for Multiple

Objects Extraction: Application to Radiographic Inspection for Weld Defects Detection

A. B. Goumeidane, M. Khamadja, N. Nacereddine

Abstract: This paper presents an adaptive probabilistic region-based deformable model using an explicit representation that aims to extract automatically defects from a radiographic film. To deal with the height computation cost of such model, an adaptive polygonal representation is used and the search space for the greedy-based model evolution is reduced. Furthermore, we adapt this explicit model to handle topological changes in presence of multiple defects.

Keywords : Explicit deformable model, adaptive contour representation, Maximum likelihood criterion

Centre de Recherche en Technologies Industrielles - CRTI - www.crti.dz

Powered by TCPDF (www.tcpdf.org)

Références

Télécharger maintenant ( PDF - 1 Page - 7.49 KB )

Documents relatifs

Adaptive and Statistical Polygonal Curve for Multiple Weld Defects Detection in Radiographic Images

This paper presents an adaptive probabilistic region-based deformable model using an explicit representation that aims to extract automatically defects from

Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images

Based on probability theory involving a graphical structure and random variables, BN iswidely used for classification tasks and in this paper, BN is used as a class discrimination

Bayesian Networks-Based Defects ClassesDiscrimination in Weld Radiographic Images

Based on probabilitytheory involving a graphical structure and random variables, BN iswidely used for classification tasks and in this paper, BN is used as aclass discrimination

Bayesian Networks-Based Defects Classes Discrimination in Weld Radiographic Images

Based on probability theory involving a graphical structure and random variables, BN is widely used for classification tasks and in this paper, BN is used as a class

Automatic Detection and Features Computationof Weld Defects for Radiographic Inspection

Weld defect features are computed from the segmentation result (final binary level set).. We have computed several features; they are ranked in two categories: Geometric features

Automatic Detection and Features Computation of Weld Defects for Radiographic Inspection

Weld defect segmentation and restoration from rough radiographic image: (a) Defined ROI in rough weld radiographic image, (b) ROI image with initial contour (dashed green line)

Hybrid Wavelet-Fractal Image Coder Applied to Radiographic Images of Weld Defects

A comparative study between the hybrid wavelet-fractal coder and pure fractal compression technique have been made in order to investigate the compression ratio

An Implicit Region-Based Deformable Model with Local Segmentation Applied To Weld Defects Extraction

The proposed model uses the piecewise constant approximation of Chan-Vese model to compute Signed Pressure Force (SPF) function, this one attracts the curve to the