This paper describes a probabilistic region-based deformable model using a new adaptive scheme for B-spline representation. The idea is to adapt the number of spline control points which are necessary to describe an object with complex shape. For this purpose, the curve segment length (CSL) is used as criterion. The proposed split and merge strategy on the spline model consists in : adding a new control point when CSL is greater than a certain splitting threshold so that the contour tracks all the concavities and, removing a control point when CSL is less to a certain merging threshold so that the contour aspect maintains its smoothness. Noise on synthetic and real weld radiographic images is assumed following Gaussian or Rayleigh distribution. The experiments carried out confirm the adequacy of this approach, especially in tracking pronounced concavities contained in images.
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In this paper, a simple and fast querying method for content-based image retrieval is presented. In order to measure the similarity degree between two color images both quickly and effectively, we use a weighted pseudo-metric employing one-dimensional Daubechies decomposition and compression of the extracted feature vectors. In order to improve the discriminatory capacity of the pseudo-metric, we compute its weights using separately a classical logistic regression model and a Bayesian logistic regression model. The Bayesian logistic regression model was shown to be significantly better than the classical logistic regression model at improving the retrieval performance. Experimental results are reported on the WANG and ZuBuD color image databases proposed by [Deselaers T., Keysers D., Ney H.: Classification error rate for quantitative evaluation of content-based image retrieval systems. 17th International Conference on Pattern Recognition (ICPR'04), 2, pp. 505-508, Cambridge, UK].
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In non-destructive testing with radiography, a perfect knowledge of the weld defect shape is an essential step to appreciate the quality of the weld and make decision on its acceptance or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represent the real defect as accurately as possible, the choice of the thresholding methods must be made judiciously. In this paper, performance criteria are used to conduct a comparative study of the thresholding methods based on the gray level histogram, the 2D histogram and the locally adaptive approach to weld defect detection in radiographic images.
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This paper describes a novel method for segmentation of weld defect in radiographic images. Contour estimation is formulated as a statistical estimation problem, where both the contour and the observation model parameters are unknown. Our approach can be described as a region-based maximum likelihood formulation of parametric deformable contours. This formulation provides robustness against the poor image quality, and allows simultaneous estimation of the contour parameters together with other parameters of the model. Implementation is performed by a deterministic iterative algorithm with minimal user intervention. Results testify very good performance of such contour estimation approach.
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