Search results for "Pattern recognition"
showing 10 items of 2301 documents
Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
2021
Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-…
Learning non-linear time-scales with kernel -filters
2009
A family of kernel methods, based on the @c-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) @c-filter [G. Camps-Valls, M. Martinez-Ramon, J.L. Rojo-Alvarez, E. Soria-Olivas, Robust @c-filter using support vector machines, Neurocomput. J. 62(12) (2004) 493-499.], but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel @c-filters. The improved performance in several application examples suggest…
Robust γ-filter using support vector machines
2009
This Letter presents a new approach to time-series modelling using the support vector machines (SVM). Although the g-filter can provide stability in several time-series models, the SVM is proposed here to provide robustness in the estimation of the g-filter coefficients. Examples in chaotic time-series prediction and channel equalization show the advantages of the joint SVM g-filter. Teoría de la Señal y Comunicaciones
A Simple Fusion Method for Image Time Series Based on the Estimation of Image Temporal Validity
2015
High-spatial-resolution satellites usually have the constraint of a low temporal frequency, which leads to long periods without information in cloudy areas. Furthermore, low-spatial-resolution satellites have higher revisit cycles. Combining information from high- and low- spatial-resolution satellites is thought a key factor for studies that require dense time series of high-resolution images, e.g., crop monitoring. There are several fusion methods in the bibliography, but they are time-consuming and complicated to implement. Moreover, the local evaluation of the fused images is rarely analyzed. In this paper, we present a simple and fast fusion method based on a weighted average of two in…
Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos
2015
[EN] Non-parametric regression methods are powerful statistical methods to retrieve biophysical parameters from remote sensing measurements. However, their performance can be affected by what has been presented during the training phase. To ensure robust retrievals, various cross-validation sub-sampling methods are often used, which allow to evaluate the model with subsets of the field dataset. Here, two types of cross-validation techniques were analyzed in the development of non-parametric regression models: hold-out and k-fold. Selected non-parametric linear regression methods were least squares Linear Regression (LR) and Partial Least Squares Regression (PLSR), and nonlinear methods were…
Metodo di Template Matching per l'Analisi di Immagini
2012
La presente invenzione si riferisce ad un metodo di Template Matching per l’analisi di immagini da ImmunoFluorescenza Indiretta (IFI) per la rivelazione e classificazione automatica di pattern autoanticorpali. L’invenzione qui presentata generalizza il metodo del Template Matching operando innovativamente il mapping del contenuto visuale dell’immagine con particolari funzioni discrete qui denominate “mappatori”; inoltre, utilizzando le informazioni provenienti dalla sovrapposizione dei vari mappatori con un metodo di confronto funzionale, realizza una funzione di correlazione originale. La metodologia descritta nel seguito presenta una flessibilità tale da renderla applicabile a qualsiasi p…
Segmentation d'images robuste appliqué à l'imagerie par résonance magnétique et l'échographie de la prostate
2012
Prostate segmentation in trans rectal ultrasound (TRUS) and magnetic resonanceimages (MRI) facilitates volume estimation, multi-modal image registration, surgicalplaning and image guided prostate biopsies. The objective of this thesis is to developshape and region prior deformable models for accurate, robust and computationallyefficient prostate segmentation in TRUS and MRI images. Primary contributionof this thesis is in adopting a probabilistic learning approach to achieve soft classificationof the prostate for automatic initialization and evolution of a shape andregion prior deformable models for prostate segmentation in TRUS images. Twodeformable models are developed for the purpose. An…
Development of an Automatic Pollen Classification System Using Shape, Texture and Aperture Features
2015
International audience; Automatic detection and classification of pollen species has value for use inside of palynologic allergen studies. Traditional labeling of different pollen species requires an expert biologist to classify particles by sight, and is therefore time-consuming and expensive. Here, an automatic process is developed which segments the particle contour and uses the extracted features for the classification process. We consider shape features, texture features and aperture features and analyze which are useful. The texture features analyzed include: Gabor Filters, Fast Fourier Transform, Local Binary Patterns, Histogram of Oriented Gradients, and Haralick features. We have s…
A solution to the stochastic point location problem in metalevel nonstationary environments.
2008
This paper reports the first known solution to the stochastic point location (SPL) problem when the environment is nonstationary. The SPL problem involves a general learning problem in which the learning mechanism (which could be a robot, a learning automaton, or, in general, an algorithm) attempts to learn a "parameter," for example, lambda*, within a closed interval. However, unlike the earlier reported results, we consider the scenario when the learning is to be done in a nonstationary setting. For each guess, the environment essentially informs the mechanism, possibly erroneously (i.e., with probability p), which way it should move to reach the unknown point. Unlike the results availabl…
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing
2020
The Tsetlin Machine (TM) is a machine learning algorithm founded on the classical Tsetlin Automaton (TA) and game theory. It further leverages frequent pattern mining and resource allocation principles to extract common patterns in the data, rather than relying on minimizing output error, which is prone to overfitting. Unlike the intertwined nature of pattern representation in neural networks, a TM decomposes problems into self-contained patterns, represented as conjunctive clauses. The clause outputs, in turn, are combined into a classification decision through summation and thresholding, akin to a logistic regression function, however, with binary weights and a unit step output function. …