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

The experimental results show that the SVM classifier is an efficient automatic weld defect classification algorithm and can achieve high accuracy percent and is faster than

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

The particularity of this model is the capacity to detect boundaries’ objects without need to use gradient of the image, this propriety gives its several advantages: it

Fractal image compression, based on concepts of iterated function systems (IFS), consists of partitioning blocks and approximates each block by a transformed codebook block derived

Abstract : This paper presents a variational level set approach applied to segment images; especially to detect welds defects in radiographic images.. Level set methods are a

Also the level set function is easily initialized with a binary function, which is more efficient to construct than the widely used signed distance function (SDF). The

In this approach we exploit the performance of the GVF as external force and enhance it by joining to it an external adaptive pressure forces which speeds up to the snake