Search results for "Computer Vision and Pattern Recognition"
showing 10 items of 997 documents
Kromos: Ontology based information management for ICT societies
2009
Over the last few years, several projects for the development of innovative systems capable of collecting and sharing information have been carried out, following the increasing companies' interest on a correct knowledge management. ICT companies' managers have realized that knowledge and its management, more than the mere data, constitute fundamental part of their activities. This paper proposes a Knowledge Management System whose main feature is an underlying ontological knowledge representation. This data representation allows the specialization of the reasoning capabilities and the provision of ad hoc behaviors. The system has been designed for the management of projects and processes a…
Collaboration experience in the supply chain of knowledge and patent development
2017
In this paper, we aim at understanding the role of collaboration experience in supply chains of knowledge (SCoK). The SCoK of a company is its supply chain not related to the flow of physical goods but to the flow of R&D commodities. R&D commodities are for example patents, technologies, research services, studies, and projects, and, in high-tech industries, their development and commercialisation are considered as important as real products. To accomplish our aim in this paper, we fulfil the following research objectives: (1) investigate the relationship between the collaboration experience in SCoK and the propensity of the firm to develop new patents; (2) examine how the structural embedd…
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…
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.
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 …
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.
Shift- and scale-invariant recognition of contour objects with logarithmic radial harmonic filters.
2008
The phase-only logarithmic radial harmonic (LRH) filter has been shown to be suitable for scale-invariant block object recognition. However, an important set of objects is the collection of contour functions that results from a digital edge extraction of the original block objects. These contour functions have a constant width that is independent of the scale of the original object. Therefore, since the energy of the contour objects decreases more slowly with the scale factor than does the energy of the block objects, the phase-only LRH filter has difficulties in the recognition tasks when these contour objects are used. We propose a modified LRH filter that permits the realization of a shi…