Search results for "Pattern Recognition"
showing 10 items of 2301 documents
Automatic differentiation of melanoma from dysplastic nevi.
2015
International audience; Malignant melanoma causes the majority of deaths related to skin cancer. Nevertheless, it is the most treatable one, depending on its early diagnosis. The early prognosis is a challenging task for both clinicians and dermatologist, due to the characteristic similarities of melanoma with other skin lesions such as dysplastic nevi. In the past decades, several computerized lesion analysis algorithms have been proposed by the research community for detection of melanoma. These algorithms mostly focus on differentiating melanoma from benign lesions and few have considered the case of melanoma against dysplastic nevi. In this paper, we consider the most challenging task a…
Pd/Au/SiC Nanostructured Diodes for Nanoelectronics: Room Temperature Electrical Properties
2010
Pd/Au/SiC nanostructured Schottky diodes were fabricated embedding Au nanoparticles (NPs) at the metalsemiconductor interface of macroscopic Pd/SiC contacts. The Au NPs mean size was varied controlling the temperature and time of opportune annealing processes. The electrical characteristics of the nanostructured diodes were studied as a function of the NPs mean size. In particular, using the standard theory of thermoionic emission, we obtained the effective Schottky barrier height (SBH) and the effective ideality factor observing their dependence on the annealing time and temperature being the signature of their dependence on the mean NP size. Furthermore, plotting the effective SBH as a fu…
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
2016
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimat…
Toward a Collective Agenda on AI for Earth Science Data Analysis
2021
In the last years we have witnessed the fields of geosciences and remote sensing and artificial intelligence to become closer. Thanks to both the massive availability of observational data, improved simulations, and algorithmic advances, these disciplines have found common objectives and challenges to advance the modeling and understanding of the Earth system. Despite such great opportunities, we also observed a worrying tendency to remain in disciplinary comfort zones applying recent advances from artificial intelligence on well resolved remote sensing problems. Here we take a position on research directions where we think the interface between these fields will have the most impact and be…
Multi-temporal and Multi-source Remote Sensing Image Classification by Nonlinear Relative Normalization
2016
Remote sensing image classification exploiting multiple sensors is a very challenging problem: data from different modalities are affected by spectral distortions and mis-alignments of all kinds, and this hampers re-using models built for one image to be used successfully in other scenes. In order to adapt and transfer models across image acquisitions, one must be able to cope with datasets that are not co-registered, acquired under different illumination and atmospheric conditions, by different sensors, and with scarce ground references. Traditionally, methods based on histogram matching have been used. However, they fail when densities have very different shapes or when there is no corres…
Spatial noise-aware temperature retrieval from infrared sounder data
2020
In this paper we present a combined strategy for the retrieval of atmospheric profiles from infrared sounders. The approach considers the spatial information and a noise-dependent dimensionality reduction approach. The extracted features are fed into a canonical linear regression. We compare Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) for dimensionality reduction, and study the compactness and information content of the extracted features. Assessment of the results is done on a big dataset covering many spatial and temporal situations. PCA is widely used for these purposes but our analysis shows that one can gain significant improvements of the error rates when using…
A method for quantifying atrial fibrillation organization based on wave-morphology similarity
2002
A new method for quantifying the organization of single bipolar electrograms recorded in the human atria during atrial fibrillation (AF) is presented. The algorithm relies on the comparison between pairs of local activation waves (LAWs) to estimate their morphological similarity, and returns a regularity index (/spl rho/) which measures the extent of repetitiveness over time of the detected activations. The database consisted of endocardial data from a multipolar basket catheter during AF and intraatrial recordings during atrial flutter. The index showed maximum regularity (/spl rho/=1) for all atrial flutter episodes and decreased significantly when increasing AF complexity as defined by W…
A fast recursive algorithm to compute local axial moments
2001
The paper describes a fast algorithm to compute local axial moments used in the algorithm of discrete symmetry transform (DST). The basic idea is grounded on fast recursive implementation of respective linear filters by using the so-called primitive kernel functions since the moment computation can be performed in the framework of linear filtering. The main result is that the computation of the local axial moments is independent of the kernel size, i.e. of the order O(1) per data point (pixel). This result is of relevance whenever the DST is used to face with real time computer vision problems. The experimental results confirm the time complexity predicted by the theory.
An Automatic System for the Analysis and Classification of Human Atrial Fibrillation Patterns from Intracardiac Electrograms
2008
This paper presents an automatic system for the analysis and classification of atrial fibrillation (AF) patterns from bipolar intracardiac signals. The system is made up of: 1) a feature- extraction module that defines and extracts a set of measures potentially useful for characterizing AF types on the basis of their degree of organization; 2) a feature-selection module (based on the Jeffries-Matusita distance and a branch and bound search algorithm) identifying the best subset of features for discriminating different AF types; and 3) a support vector machine technique-based classification module that automatically discriminates the AF types according to the Wells' criteria. The automatic s…
PPG/ECG Multisite Combo System Based on SiPM Technology
2019
Two versions of a PPG/ECG combined system have been realized and tested. In a first version a multisite system has been equipped by integrating 3 PPG optodes and 3 ECG leads, whereas in another setup a portable version has been carried out. Both versions have been realized by equipping the optical probes with SiPM detectors. SiPM technology is expected to bring relevant advantages in PPG systems and overcome the limitations of physiological information extracted by state of the art PPG, such as poor sensitivity of detectors used for backscattered light detection and motion artifacts seriously affecting the measurements repeatability and pulse waveform stability. This contribution presents t…