Search results for " Pattern Recognition"
showing 10 items of 1050 documents
THE USE OF WEAK ESTIMATORS TO ACHIEVE LANGUAGE DETECTION AND TRACKING IN MULTILINGUAL DOCUMENTS
2013
This paper deals with the problems of language detection and tracking in multilingual online short word-of-mouth (WoM) discussions. This problem is particularly unusual and difficult from a pattern recognition perspective because, in these discussions, the participants and content involve the opinions of users from all over the world. The nature of these discussions, consisting of multiple topics in different languages, presents us with a problem of finding training and classification strategies when the class-conditional distributions are nonstationary. The difficulties in solving the problem are many-fold. First of all, the analyst has no knowledge of when one language stops and when the…
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments
2016
The task of designing estimators that are able to track time-varying distributions has found promising applications in many real-life problems.Existing approaches resort to sliding windows that track changes by discarding old observations. In this paper, we report a novel estimator referred to as the Stochastic Discretized Weak Estimator (SDWE), that is based on the principles of discretized Learning Automata (LA). In brief, the estimator is able to estimate the parameters of a time varying binomial distribution using finite memory. The estimator tracks changes in the distribution by operating a controlled random walk in a discretized probability space. The steps of the estimator are discre…
Semi-Supervised Classification Method for Hyperspectral Remote Sensing Images
2004
A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning met…
A LiDAR Prototype with Silicon Photomultiplier and MEMS Mirrors
2018
In this paper, we present a low cost prototype of a Time-Of-Flight (TOF) LiDAR system, employing a SiPM as photo detector and MEMS mirrors in order to steer the nanosecond pulsed optical beam with a scanning angle of +/-6°. Preliminary TOF measurements have been performed both indoor and outdoor to test the limits of the system.
Voluntary distance running prevents TNF-mediated liver injury in mice through alterations of the intrahepatic immune milieu
2017
AbstractPhysical activity confers a broad spectrum of health benefits. Beyond the obvious role in metabolically driven diseases, the role of physical activity in acute liver injury is poorly explored. To study the role of physical activity in acute liver injury, a novel model of voluntary distance running in mice was developed and mice were subjected to acute liver injury induced by N-galactosamine (GalN) and lipopolysaccharide (LPS). Analyses included histological stains, immunoblotting, qRT-PCR and FACS analysis. Voluntary distance running increased to an average of 10.3 km/day after a learning curve. Running lead to a decrease in the absolute numbers of intrahepatic CD4+ T and B lymphocy…
Cognitive factors in the evaluation of synthetic speech
1998
Abstract This paper illustrates the importance of various cognitive factors involved in perceiving and comprehending synthetic speech. It includes findings drawn from the relative psychological and psycholinguistic literature together with experimental results obtained at the Fondazione Ugo Bordoni laboratory. Overall, it is shown that listening to and comprehending synthetic voices is more difficult than with a natural voice. However, and more importantly, this difficulty can and does decrease with the subjects' exposure to said synthetic voices. Furthermore, greater workload demands are associated with synthetic speech and subjects listening to synthetic passages are required to pay more …
Inflammasomes in Liver Fibrosis
2017
AbstractCell death and inflammation are two central elements in the development of liver fibrosis. Inflammasomes are intracellular multiprotein complexes expressed in both hepatocytes and nonparenchymal cells in the liver that are key regulators of inflammation and cell fate. They respond to cellular danger signals by activating caspase 1, releasing the proinflammatory cytokines IL-1β and IL-18, as well as initiating a novel pathway of programmed cell death termed “pyroptosis.” These processes can initiate and perpetuate an abnormal wound-healing response with the principle cellular target being the activation of hepatic stellate cells. From the various inflammasomes, the NLRP3 inflammasome…
Dynamic best spectral bands selection for face recognition
2014
In this paper, face recognition in uncontrolled illumination conditions is investigated. A twofold contribution is proposed. First, three state-of-art algorithms, namely Multiblock Local Binary Pattern (MBLBP), Histogram of Gabor Phase Patterns (HGPP) and Local Gabor Binary Pattern Histogram Sequence (LGBPHS) are evaluated upon the IRIS-M3 face database to study their robustness against a high illumination variation conditions. Second, we propose to use visible multispectral images, provided by the same face database, to enhance the performance of the three mentioned algorithms. To reduce the high data dimensionality introduced by the use of multispectral images, we have designed a system t…
A New Wavelet-Based Texture Descriptor for Image Retrieval
2007
This paper presents a novel texture descriptor based on the wavelet transform. First, we will consider vertical and horizontal coefficients at the same position as the components of a bivariate random vector. The magnitud and angle of these vectors are computed and its histograms are analyzed. This empirical magnitud histogram is modelled by using a gamma distribution (pdf). As a result, the feature extraction step consists of estimating the gamma parameters using the maxima likelihood estimator and computing the circular histograms of angles. The similarity measurement step is done by means of the well-known Kullback-Leibler divergence. Finally, retrieval experiments are done using the Bro…
Apprentissage de modalités auxiliaires pour la localisation basée vision
2018
In this paper we present a new training with side modality framework to enhance image-based localization. In order to learn side modality information, we train a fully convo-lutional decoder network that transfers meaningful information from one modality to another. We validate our approach on a challenging urban dataset. Experiments show that our system is able to enhance a purely image-based system by properly learning appearance of a side modality. Compared to state-of-the-art methods, the proposed network is lighter and faster to train, while producing comparable results.