Search results for "Thresholding"
showing 10 items of 47 documents
A Scheme for Continuous Input to the Tsetlin Machine with Applications to Forecasting Disease Outbreaks
2019
In this paper, we apply a new promising tool for pattern classification, namely, the Tsetlin Machine (TM), to the field of disease forecasting. The TM is interpretable because it is based on manipulating expressions in propositional logic, leveraging a large team of Tsetlin Automata (TA). Apart from being interpretable, this approach is attractive due to its low computational cost and its capacity to handle noise. To attack the problem of forecasting, we introduce a preprocessing method that extends the TM so that it can handle continuous input. Briefly stated, we convert continuous input into a binary representation based on thresholding. The resulting extended TM is evaluated and analyzed…
Image boundaries detection: from thresholding to implicit curve evolution
2014
The development of high dimensional large-scale imaging devices increases the need of fast, robust and accurate image segmentation methods. Due to its intrinsic advantages such as the ability to extract complex boundaries, while handling topological changes automatically, the level set method (LSM) has been widely used in boundaries detection. Nevertheless, their computational complexity limits their use for real time systems. Furthermore, most of the LSMs share the limit of leading very often to a local minimum, while the effectiveness of many computer vision applications depends on the whole image boundaries. In this paper, using the image thresholding and the implicit curve evolution fra…
Computer vision profilometer: equipment and evaluation of measurements
1991
Abstract This paper describes a new equipment that measures roughness values by a computer vision (CV) technique. Measurements carried out by a CV profilometer are also evaluated. A laser source (power 2 mW), a cylindrical lens and a charge coupled device (CCD) TV-camera with a suitable optical system form an image of the profile of the sample under inspection. This image is then transformed into a binary image by thresholding and the line that divides the bright zone from the dark zone is the sample profile. From this line the characteristic roughness values can be calculated. The roughness measurements are carried out both by the CV profilometer and a stylus profilometer on eight specimen…
Combined K-Best sphere decoder based on the channel matrix condition number
2008
It is known that sphere decoding (SD) methods can provide maximum-likelihood (ML) detection over Gaussian MIMO channels with lower complexity than the exhaustive search. Channel matrix condition number represents an important influence on the performance of usual detectors. Throughout this paper, two particular cases of a SD method called K-Best carry out a combined detection in order to reduce the computational complexity with predictable performance degradation. Algorithm selection is based on channel matrix condition number thresholding. K-Best is a suboptimal SD algorithm for finding the ML solution of a detection problem. It is based on a fixed complexity tree search, set by a paramete…
Signal Restoration via a Splitting Approach
2012
International audience; In the present study, a novel signal restoration method from noisy data samples is presented and is termed as "signal split (SSplit)" approach. The new method utilizes Stein unbiased risk estimate estimator to split the signal, the Lipschitz exponents to identify noise elements and a heuristic approach for the signal reconstruction. However, unlike many noise removal techniques, the present method works only in the non-orthogonal domain. Signal restoration was performed on each individual part by finding the best compromise between the data samples and the smoothing criteria. Statistical results are quite promising and suggest better performance than the conventional…
Edge detection insensitive to changes of illumination in the image
2010
In this paper we present new edge detection algorithms which are motivated by recent developments on edge-adapted reconstruction techniques [F. Arandiga, A. Cohen, R. Donat, N. Dyn, B. Matei, Approximation of piecewise smooth functions and images by edge-adapted (ENO-EA) nonlinear multiresolution techniques, Appl. Comput. Harmon. Anal. 24 (2) (2008) 225-250]. They are based on comparing local quantities rather than on filtering and thresholding. This comparison process is invariant under certain transformations that model light changes in the image, hence we obtain edge detection algorithms which are insensitive to changes in illumination.
Optical CDMA enhanced by nonlinear optics
2010
Intended for the next generation of optical access networks, OCDMA is of great interest to meet the demand of increasing the number of users per access fiber, especially as spectral phase coding increases its performance in the optical domain. This, however, requires handling broad spectra and short pulses, which are best dealt with using opto-electronic or all-optical devices instead of slower electronics. Among others, we demonstrate spectral-phase-coded OCDMA using a fiber-based saturable absorber as thresholding in the receiver.
A support vector domain method for change detection in multitemporal images
2010
This paper formulates the problem of distinguishing changed from unchanged pixels in multitemporal remote sensing images as a minimum enclosing ball (MEB) problem with changed pixels as target class. The definition of the sphere-shaped decision boundary with minimal volume that embraces changed pixels is approached in the context of the support vector formalism adopting a support vector domain description (SVDD) one-class classifier. SVDD maps the data into a high dimensional feature space where the spherical support of the high dimensional distribution of changed pixels is computed. Unlike the standard SVDD, the proposed formulation of the SVDD uses both target and outlier samples for defi…
Objective improvement of the visual quality of ion microscope images
2013
The need to operate with low ion beam fluences implies the images obtained using ion microscope (IM) are often grainy and have poor visual quality compared to what can be obtained using e.g. confocal microscopy. This results from the Poissonian distribution of counts in pixels. Here we report work on some different approaches for objectively improving the visual quality of IM images. In this work we present (i) dramatic improvement in the visual image quality of off-axis and direct-scanning transmission ion microscopy (STIM) images by suppression of zero-pixels; (ii) denoising of PIXE images using wavelet filtering and (iii) use of the feature preserving characteristics of wavelet filtering…
Multivariate denoising methods combining wavelets and principal component analysis for mass spectrometry data
2010
The identification of new diagnostic or prognostic biomarkers is one of the main aims of clinical cancer research. In recent years, there has been a growing interest in using mass spectrometry for the detection of such biomarkers. The MS signal resulting from MALDI-TOF measurements is contaminated by different sources of technical variations that can be removed by a prior pre-processing step. In particular, denoising makes it possible to remove the random noise contained in the signal. Wavelet methodology associated with thresholding is usually used for this purpose. In this study, we adapted two multivariate denoising methods that combine wavelets and PCA to MS data. The objective was to o…