In non destructive testing by 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 acceptability or rejection. Because of the complex nature of the considered images, and in order that the detected defect region represents the most accurately possible the real defect, the choice of thresholding methods must...

In this paper, we propose a general framework of adaptive local thresholding based on a verification-based multithreshold probing scheme. Object hypotheses are generated by binarization using hypothetic thresholds and accepted/rejected by a verification procedure. The application-dependent verification procedure can be designed to fully utilize all relevant informations about the objects of int...

We propose a general methodology (PURE-LET) to design and optimize a wide class of transform-domain thresholding algorithms for denoising images corrupted by mixed Poisson-Gaussian noise. We express the denoising process as a linear expansion of thresholds (LET) that we optimize by relying on a purely data-adaptive unbiased estimate of the mean-squared error (MSE), derived in a non-Bayesian fra...

We perform a short survey of image thresholding methods for very specific task, and assess their performance comparison. We analyse performance of adaptive thresholding methods concerning segmentation of immunonegative cells of follicular lymphoma tissue samples stained with 3,3’Diaminobenzidine&Haematoxylin. We use artificial images based on experimental images that greatly simulates real samp...

In this paper, we present a novel adaptive thresholding technique based upon an anisotropic diffusion model, which may be referred to as the anti-geometric heat flow. In contrast to its more popular counterparts (such as the geometric heat flow) which diffuse parallel to image edges, this model diffuses perpendicular to image edges, yielding surfaces which are naturally suited for adaptive thre...

Abstract: We revisit the adaptive Lasso as well as the thresholded Lasso with refitting, in a high-dimensional linear model, and study prediction error, lq-error (q ∈ {1, 2}), and number of false positive selections. Our theoretical results for the two methods are, at a rather fine scale, comparable. The differences only show up in terms of the (minimal) restricted and sparse eigenvalues, favor...

This paper proposes an Adaptive Image Denoising Method based on Thresholding that follows the similar approach as in the NeighShrink method. This method shrinks the noisy wavelet coefficients using an adaptive threshold. The NeighShrink and its versions namely, IAWDMBNC and IIDMWT always produce unfavourable smoothing of edges and details of the noisy image because these methods kill more noisy...

In this paper the binarization of matrix codes is investigated as an application of supervised learning of image processing tasks using a recurrent version of the Neural Abstraction Pyramid. The desired network output is computed using an adaptive thresholding method for undegraded images. The network is trained to iteratively produce the same output even when the contrast is lowered and typica...

Article history: Received 25 June 2008 Received in revised form 11 November 2008 Accepted 13 November 2008

In this paper, we propose a new adaptive wavelet thresholding method for using in speech Enhancement. This modified version of the wavelet thresholding method updates an adaptive threshold in each frame. In the proposed method the selection of the wavelet threshold value depends on the estimates of the clean speech signal features. The evaluation results show that by using the specific features...