Search results for "Signal Processing"
showing 10 items of 2451 documents
A Support Vector Machine Signal Estimation Framework
2018
Support vector machine (SVM) were originally conceived as efficient methods for pattern recognition and classification, and the SVR was subsequently proposed as the SVM implementation for regression and function approximation. Nowadays, the SVR and other kernel‐based regression methods have become a mature and recognized tool in digital signal processing (DSP). This chapter starts to pave the way to treat all the problems within the field of kernel machines, and presents the fundamentals for a simple, framework for tackling estimation problems in DSP using support vector machine SVM. It outlines the particular models and approximations defined within the framework. The chapter concludes wit…
Special issue on pattern recognition techniques in data mining
2017
Peer Reviewed
Influence of the type of illumination on the measurement of the modulation transfer function in the living human eye: A theoretical study
1999
Abstract Applications such as refractive surgery demand an objective appraisal of the retinal image quality. The modulation transfer function (MTF) provides that information when measured directly. Moreover, the MTF obtained using a simple and objective method such as that described in this paper allows the neural contrast sensitivity function (CSF) to be obtained from the global CSF and the MTF. When calculating the MTF it must be borne in mind whether the applicable theory is coherent or incoherent. In the literature, the developed theory presents some approximations and incongruities. Also, it is interesting to note that the method of recording the MTF (short or long time of integration,…
Diversity in search strategies for ensemble feature selection
2005
Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subse…
Influencia del tipo de registro (unipolar o bipolar) en las características espectrales de los registros epicárdicos de la fibrilación ventricular. E…
2007
Introduction and objectives. The aim of this study was to examine the hypothesis that the recording mode (ie, unipolar or bipolar) affects the information obtained using spectral analysis techniques during ventricular fibrillation by carrying out an experiment using epicardial electrodes. Methods. Recordings of ventricular fibrillation were obtained in 29 isolated rabbit hearts using a multipleelectrode probe located on the left ventricular free wall. The parameter values obtained in the frequency domain (by Fourier analysis) using unipolar or bipolar electrodes, different interelectrode distances, and different orientations (ie, horizontal, vertical, or diagonal) were compared. Results. Ch…
A new criterion for determining the expansion center for circular-harmonic filters
1995
A new criterion for locating the expansion center of circular harmonic filters is presented. The innovation consists in the use of the information provided by both the circular harmonic energy map and the peak to correlation energy map of the object to be detected. The choice of an expansion center with a high value of peak to correlation energy ensures a good discrimination capability of the filter. In addition, we choose a point which is a local maximum for the energy map. An improvement of the discrimination ability is obtained with respect to previous methods.
Applying logistic regression to relevance feedback in image retrieval systems
2007
This paper deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here we present a novel algorithm for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this prob…
Modified LACIF filtering in background disjoint noise
2011
Abstract This work deals with pattern recognition methods based on correlations for images in the presence of noise. We propose a modification of the nonlinear Locally Adaptive Contrast Invariant Filter (LACIF) that yields correlation peaks that are invariant to linear intensity changes of the target but that has some limitations in the presence low variance nonoverlapping background noise. The modification of the filter implies a normalization by a global variance of several distributions. The estimation of the variance distributions is done locally by means of correlations. Experimental results as well as comparisons with the classical matched filter and the common LACIF are given.
Intensity invariant nonlinear correlation filtering in spatially disjoint noise.
2010
We analyze the performance of a nonlinear correlation called the Locally Adaptive Contrast Invariant Filter in the presence of spatially disjoint noise under the peak-to-sidelobe ratio (PSR) metric. We show that the PSR using the nonlinear correlation improves as the disjoint noise intensity increases, whereas, for common linear filtering, it goes to zero. Experimental results as well as comparisons with a classical matched filter are given.
Combining similarity measures in content-based image retrieval
2008
The purpose of content based image retrieval (CBIR) systems is to allow users to retrieve pictures from large image repositories. In a CBIR system, an image is usually represented as a set of low level descriptors from which a series of underlying similarity or distance functions are used to conveniently drive the different types of queries. Recent work deals with combination of distances or scores from different and usually independent representations in an attempt to induce high level semantics from the low level descriptors of the images. Choosing the best method to combine these results requires a careful analysis and, in most cases, the use of ad-hoc strategies. Combination based on or…