This paper addresses the noise estimation in the digital domain and proposes a noise estimator based on the step signal model. It is efficient for any distribution of noise because it does not rely only on the smallest amplitudes in the signal or image. The proposed approach uses polarized/directional derivatives and a nonlinear combination of these derivatives to estimate the noise distribution (e.g., Gaussian, Poisson, speckle, etc.). The moments of this measured distribution can be computed and are also calculated theoretically on the basis of noise distribution models. The 1D performances are detailed, and as this paper is mostly dedicated to image processing, a 2D extension is proposed. The 2D performances for several noise distributions and noise models are presented and are compared with selected other methods.

Electrocardiogram (ECG) signals are used to analyze the cardiovascular activity in the human body and have a primary role in the diagnosis of several heart diseases. The QRS complex is the most important and distinguishable component in the ECG because of its spiked nature and high amplitude. Automatic detection and delineation of the QRS complex in ECG is of extreme importance for computer aided diagnosis of cardiac disorder. Therefore, the accurate detection of this component is crucial to the performance of subsequent machine learning algorithms for cardiac disease classification. The aim of the present work is to detect the QRS wave from electrocardiogram (ECG) signals. Initially the baseline drift has been removed from the signal followed by the decomposition using continuous wavelet transform. Modulus maxima approach proposed by Mallat has been used to compute the Lipschitz exponent of the components. By using the property of R peak, having highest and prominent amplitude and Lipschitz exponents, we have applied the K means clustering technique to classify QRS complex. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database.

Electrocardiogram (ECG) signal is used to analyze the cardiovascular activity in the human body and has a primary role in the diagnosis of several heart diseases. The QRS complex is the most distinguishable component in the ECG. Therefore, the accuracy of the detection of QRS complex is crucial to the performance of subsequent machine learning algorithms for cardiac disease classification. The aim of the present work is to detect QRS wave from ECG signals. Wavelet transform filtering is applied to the signal in order to remove baseline drift, followed by QRS localization. By using the property of R peak, having highest and prominent amplitude, we have applied thresholding technique based on the median absolute deviation(MAD) of modulus maximas to detect the complex. In order to evaluate the algorithm, the analysis has been done on MIT-BIH Arrhythmia database. The results have been examined and approved by medical doctors.

This paper presents a nonlinear derivative approach to addressing the problem of discrete edge detection. This edge detection scheme is based on the nonlinear combination of two polarized derivatives. Its main property is a favorable signal-to-noise ratio (SNR) at a very low computation cost and without any regularization. A 2D extension of the method is presented and the benefits of the 2D localization are discussed. The performance of the localization and SNR are compared to that obtained using classical edge detection schemes. Tests of the regularized versions and a theoretical estimation of the SNR improvement complete this work.

In order to complete the work on the 3D SUSAN operator, an extraction of principal saliency degrees and direction is proposed. This will lead the proposed operator to a complete classification of vertices in five different classes: cavity points, valley points, flat points, prong points and salient points. To assess the operator, we propose two different studies. The robustness to the resolution and the regularity of meshes is presented for three different operators. To complete the evaluation, the robustness to noise is analysed, for the Stokely and the SUSAN operator. Our conclusion presents the benefits, drawbacks of the technique and future works.

This paper deals with edge detection and vertex characterization of 3D meshes. First, a new measure is introduced, the saliency degree, which provide directly the shape information of a vertex of the mesh without any curvature computation. The relationship between the saliency degree and the curvature is demonstrated for continuous and discontinuous 2D curves. Then, a description of the 3D operator is given. Finally, results on geometric and real objects are shown. The contributions of this work are the definition of a new 3D operator which is able to extract point characteristic on 3D low resolution mesh.

In this letter, we address the problem of the influence of neighbor edges and their effect on the edge delocalization while extracting a neighbor contour by a derivative approach. The properties to be fulfilled by the regularization operators to minimize or suppress this side effect are deduced, and the best detectors are pointed out. The study is carried out in 1-D for discrete signal. We show that among the derivative filters, one of them can correctly detect our model edges without being influenced by a neighboring transition, whatever their separation distance is and their respective amplitude is. A model of contour and close transitions is presented and used throughout this letter. The noise effect on the edge delocalization is recalled through one of the Canny criteria. Different derivative filters are applied onto synthetic images, and their performances are compared

Wavelet transforms are superior tools for signal analysis. They are used in this paper in order to perform a texture study of the evolution state of ovocyte cells. The analysis is based on a multiscale parameter. High order orthogonal B-spline wavelet basis are used to obtain significant parameters. Separable and quincunx schemes are studied and compared.