Search results for " Pattern Recognition"
showing 10 items of 1050 documents
Determination of sea surface temperature using combined TOVS and AVHRR data. Application to the Canary Islands area, Spain
1996
Abstract The determination of sea surface temperature from satellite is performed by means of multi-channel algorithms with channels 4 and 5 of AVHRRNOAA or using radiative transfer models and radiosounding profiles of air temperature and humidity. In this work, an alternative to the current algorithms has been established. A new method combining the information supplied by sensors of TOVS and AVHRR systems onboard NOAA satellites is proposed. It is based on the split-window technique, the coefficients A and B being determined as a function of the water vapour content, which is calculated using the TOVS sensors. The T4 and T5 temperatures are supplied by the AVHRR system. Then, combining bo…
A survey on tubulin and arginine methyltransferase families sheds light on p. lividus embryo as model system for antiproliferative drug development
2019
Tubulins and microtubules (MTs) represent targets for taxane-based chemotherapy. To date, several lines of evidence suggest that effectiveness of compounds binding tubulin often relies on different post-translational modifications on tubulins. Among them, methylation was recently associated to drug resistance mechanisms impairing taxanes binding. The sea urchin is recognized as a research model in several fields including fertilization, embryo development and toxicology. To date, some &alpha
Chaotic multiagent system approach for MRF-based image segmentation
2005
In this paper, we propose a new chaotic approach for image segmentation based on multiagent system (MAS). We consider a set of segmentation agents organized around a coordinator agent. Each segmentation agent performs iterated conditional modes (ICM) starting from its own initial image created using a chaotic mapping. The coordinator agent diversifies the initial images using a crossover and a chaotic mutation operators. The efficiency of our chaotic MAS approach is shown through some experimental results.
Time-Frequency Filtering for Seismic Waves Clustering
2014
This paper introduces a new technique for clustering seismic events based on processing, in time-frequency domain, the waveforms recorded by seismographs. The detection of clusters of waveforms is performed by a k-means like algorithm which analyzes, at each iteration, the time-frequency content of the signals in order to optimally remove the non discriminant components which should compromise the grouping of waveforms. This step is followed by the allocation and by the computation of the cluster centroids on the basis of the filtered signals. The effectiveness of the method is shown on a real dataset of seismic waveforms.
Silhouette encoding and synthesis using elliptic Fourier descriptors, and applications to videoconferencing
2004
Abstract This paper investigates the use of elliptic Fourier descriptors as a shape descriptor for encoding the silhouette of a person. Shape descriptors are here used for predicting the shape of silhouettes in missing frames within a sequence. This prediction scheme is applied to the case of generating in-between images in a low frame rate videoconferencing system, where the reconstructed silhouette is used as a binary mask for reducing the computational time for the frame reconstruction.
Quantifying the complexity of short-term heart period variability through K nearest neighbor local linear prediction
2008
The complexity of short-term heart period (HP) variability was quantified exploiting the paradigm that associates the degree of unpredictability of a time series to its dynamical complexity. Complexity was assessed through k-nearest neighbor local linear prediction. A proper selection of the parameter k allowed us to perform either linear or nonlinear prediction, and the comparison of the two approaches to infer the presence of nonlinear dynamics. The method was validated on simulations reproducing linear and nonlinear time series with varying levels of predictability. It was then applied to HP variability series measured from healthy subjects during head-up tilt test, showing that short-te…
Experimental approach for testing the uncoupling between cardiovascular variability series
2002
In cardiovascular variability analysis, the significance of the coupling between two series is commonly assessed by defining a zero level on the magnitude-squared coherence (MSC). Although the use of the conventional value of 0.5 does not consider the dependence of MSC estimates on the analysis parameters, a theoretical threshold Tt is available only for the weighted covariance (WC) estimator. In this study, an experimental threshold for zero coherence Te was derived by a statistical test from the sampling distribution of MSC estimated on completely uncoupled time series. MSC was estimated by the WC method (Parzen window, spectral bandwidth B = 0.015, 0.02, 0.025, 0.03 Hz) and by the parame…
Integration of a structural features-based preclassifier and a man-machine interactive classifier for a fast multi-stroke character recognition
2003
A transputer-based parallel machine for handwritten character recognition is proposed. An algorithm based on structural features and on a tree classifier was used to accomplish the pre-classification of the unknown sample in order to speed up the recognition process. The algorithm for the final classification is based on the description of the strokes through Fourier descriptors. The learning phase is accomplished through a man-machine interactive process. The proposed system can expand its knowledge base. A special representation of this knowledge base is proposed in order to record a great amount of data in a suitable way. A fast multistroke handwritten isolated character recognition syst…
Image Segmentation based on Genetic Algorithms Combination
2005
The paper describes a new image segmentation algorithm called Combined Genetic segmentation which is based on a genetic algorithm. Here, the segmentation is considered as a clustering of pixels and a similarity function based on spatial and intensity pixel features is used. The proposed methodology starts from the assumption that an image segmentation problem can be treated as a Global Optimization Problem. The results of the image segmentations algorithm has been compared with recent existing techniques. Several experiments, performed on real images, show good performances of our approach compared to other existing methods.
A memetic approach to discrete tomography from noisy projections
2010
Discrete tomography deals with the reconstruction of images from very few projections, which is, in the general case, an NP-hard problem. This paper describes a new memetic reconstruction algorithm. It generates a set of initial images by network flows, related to two of the input projections, and lets them evolve towards a possible solution, by using crossover and mutation. Switch and compactness operators improve the quality of the reconstructed images during each generation, while the selection of the best images addresses the evolution to an optimal result. One of the most important issues in discrete tomography is known as the stability problem and it is tackled here, in the case of no…