Manuele Bicego

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Several pattern recognition and classification techniques have been applied to the biometrics domain. Among them, an interesting technique is the Scale Invariant Feature Transform (SIFT), originally devised for object recognition. Even if SIFT features have emerged as a very powerful image descriptors, their employment in face analysis context has never(More)
Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures. This paper proposes a Hidden Markov Model (HMM)-based technique suitable for clustering of data sequences. The main aspect of the work is the use of a probabilistic model-based approach using HMM(More)
Hidden Markov models (HMM) are a widely used tool for sequence modelling. In the sequence classification case, the standard approach consists of training one HMM for each class and then using a standard Bayesian classification rule. In this paper, we introduce a novel classification scheme for sequences based on HMMs, which is obtained by extending the(More)
In this paper, Hidden Markov Models (HMMs) are investigated for the purpose of classifying planar shapes represented by their curvature coefficients. In the training phase, special attention is devoted to the initialization and model selection issues, which make the learning phase particularly effective. The results of tests on different data sets show that(More)
Hidden Markov models constitute a widely employed tool for sequential data modelling; nevertheless, their use in the clustering context has been poorly investigated. In this paper a novel scheme for HMMbased sequential data clustering is proposed, inspired on the similaritybased paradigm recently introduced in the supervised learning context. With this(More)
We developed a genome-wide transcriptomic atlas of grapevine (Vitis vinifera) based on 54 samples representing green and woody tissues and organs at different developmental stages as well as specialized tissues such as pollen and senescent leaves. Together, these samples expressed ∼91% of the predicted grapevine genes. Pollen and senescent leaves had unique(More)
The automatic execution of a complex task requires the identification of an underlying mental model to derive a possible task control sequence. The model aims at analysing and segmenting the task in simpler sub-tasks. As an example of a complex task, in this paper we consider teleoperation where a person commands a remote robot. This paper presents a new(More)
In the context of the automated surveillance field, automatic scene analysis and understanding systems typically consider only visual information, whereas other modalities, such as audio, are typically disregarded. This paper presents a new method able to integrate audio and visual information for scene analysis in a typical surveillance scenario, using(More)
It has been shown in many different contexts that the Generalized Gaussian (GG) distribution represents a flexible and suitable tool for data modeling. Almost all the reported applications are focused on modeling points (fixed length vectors); a different but crucial scenario, where the employment of the GG has received little attention, is the modeling of(More)