This paper proposes a model for eliminating noise from ECG using α-trimmed thresholding. In this paper, the α-trimmed thresholding tested in the wavelet based Adaptive Filter Model. The Wavelet co-efficients at every sub-band level is made adapted by LMS algorithm to minimum mean square value and thresholded the adapted coefficients. It is shown that the proposed thresholding method offers vari...

We present a variation on classic beam thresholding techniques that is up to an order of magnitude faster than the traditional method, at the same performance level. We also present a new thresholding technique, global thresholding, which, combined with the new beam thresholding, gives an additional factor of two improvement, and a novel technique, multiple pass parsing, tha t can be combined w...

We propose a probabilistic framework for interpreting and developing hard thresholding sparse signal reconstruction methods and present several new algorithms based on this framework. The measurements follow an underdetermined linear model, where the regression-coefficient vector is the sum of an unknown deterministic sparse signal component and a zero-mean white Gaussian component with an unkn...

This section contains a concise description of the algorithm, that is divided into 6 steps. After the original image is loaded, a simple thresholding operation is performed to discard data that we are confident that do not belong to the lungs (Figure 1a). After the thresholding, we select the outermost boundary voxels in the axial slices of this pre-segmented volume. The seeds found in Step 1 a...

Thresholding is a simple but effective technique for image segmentation. In this paper, a general locally adaptive thresholding method using neighborhood processing is presented. The method makes use of local image statistics of mean and variance within a variable neighborhood and two thresholds obtained from the global intensity distribution. It can thus take advantage of local thresholding an...

Non-local means (NLM) is a popular image denoising scheme for reducing additive Gaussian noise. It uses a patch-based approach to find similar regions within a search neighborhood and estimates the denoised pixel based on the weighted average of all pixels in the neighborhood. All weights are considered for averaging, irrespective of the value of the weights. This paper proposes an improved var...

The proliferation of video content on the web makes similarity detection an indispensable tool in web data management, searching, and navigation. We have previously proposed a compact representation of video clips, called video signature, for retrieving similar video clips in large databases. In this paper, we propose a new signature clustering algorithm to further improve retrieval performance...

In recent studies on sparse modeling, lq (0 < q < 1) regularized least squares regression (lqLS) has received considerable attention due to its superiorities on sparsity-inducing and bias-reduction over the convex counterparts. In this paper, we propose a Gauss-Seidel iterative thresholding algorithm (called GAITA) for solution to this problem. Different from the classical iterative thresholdin...

The major drawback of watershed transformation is over-segmentation. It also has a significant advantage: very good edge extraction. Thresholding methods usually utilize only global information such as an image histogram; however, they have the ability to group pixels into clusters by their value. The method presented in this paper combines the advantages of watershed segmentation and multileve...