Search results for " classification"

DAISY: a distributed architecture for intelligent SYstem

1997

Distributed perceptual systems are endowed with different kind of sensors, from which information flows to suitable modules to perform useful elaborations for decisions making. In this paper a new distributed architecture, named 'Distributed Architecture for Intelligent SYstem' (DAISY), is proposed. It is based on the concept of co-operating behavioral agents supervised by a 'Central Engagement Module'. This module integrates the processing of data coming from the behavioral agents with a symbolic level of representation, by the introduction of a 'conceptual space' intermediate analogue representation. The DAISY project is under development; experiments on navigation and exploration for an …

A Conceptual Probabilistic Model for the Induction of Image Semantics

2010

In this paper we propose a model based on a conceptual space automatically induced from data. The model is inspired to a well-founded robotics cognitive architecture which is organized in three computational areas: sub-conceptual, linguistic and conceptual. Images are objects in the sub-conceptual area, that become "knoxels" into the conceptual area. The application of the framework grants the automatic emerging of image semantics into the linguistic area. The core of the model is a conceptual space induced automatically from a set of annotated images that exploits and mixes different information concerning the set of images. Multiple low level features are extracted to represent images and…

Method for Classifying a Digital Image

2010

3D skeleton-based human action classification: A survey

2016

In recent years, there has been a proliferation of works on human action classification from depth sequences. These works generally present methods and/or feature representations for the classification of actions from sequences of 3D locations of human body joints and/or other sources of data, such as depth maps and RGB videos.This survey highlights motivations and challenges of this very recent research area by presenting technologies and approaches for 3D skeleton-based action classification. The work focuses on aspects such as data pre-processing, publicly available benchmarks and commonly used accuracy measurements. Furthermore, this survey introduces a categorization of the most recent…

Bayesian Network Based Classification of Mammography Structured Reports

2013

In modern medical domain, documents are created directly in electronic form and stored on huge databases containing documents, text in integral form and images. Retrieving right informations from these servers is challenging and, sometimes, this is very time consuming. Current medical technology do not provide a smart methodology classification of such documents based on their content. In this work the radiological structured reports are analysed classified and assigning an appropriate label. The text classifier is used to label a mammographic structured report. The experimental data are real clinical report coming from a hospital server. Analysing the structured report content, the classif…

SMART TECHNIQUES FOR FAST MEDICAL IMAGE ANALYSIS AND PROCESSING

Medical Imaging has become an important transversal applications and re- search field that embraces a great variety of sciences. Imaging is the central science of measurement in diagnosis and treating diseases. The effort of the technological progress has made possible human imaging starting from a single molecule to the whole body. The open challenge is to treat the huge amount of medical informations with the use of smart and fast techniques that allows clinical and images data analysis and processing. In this ph.D. Thesis, many issues have been addressed and a certain amount of improvement in various fields have been produced, such as biom- etry, organs and tissues segmentation, MRI ther…

Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition

2010

Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerpr…

Real-Time Object Detection in Embedded Video Surveillance Systems

2008

In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short-term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.

T100: A modern classic ensemble to profile irony and stereotype spreaders

2022

In this work we propose a novel ensemble model based on deep learning and non-deep learning classifiers. The proposed model was developed by our team for participating at the Profiling Irony and Stereotype Spreaders (ISSs) task hosted at PAN@CLEF2022. Our ensemble (named T100), include a Logistic Regressor (LR) that classifies an author as ISS or not (nISS) considering the predictions provided by a first stage of classifiers. All these classifiers are able to reach state-of-the-art results on several text classification tasks. These classifiers (namely, the voters) are a Convolutional Neural Network (CNN), a Support Vector Machine (SVM), a Decision Tree (DT) and a Naive Bayes (NB) classifie…

A Study on Classification Methods Applied to Sentiment Analysis

2013

Sentiment analysis is a new area of research in data mining that concerns the detection of opinions and/or sentiments in texts. This work focuses on the application and the comparison of three classification techniques over a text corpus composed of reviews of commercial products in order to detect opinions about them. The chosen domain is about "perfumes", and user opinions composing the corpus are written in Italian language. The proposed approach is completely data-driven: a Term Frequency / Inverse Document Frequency (TFIDF) terms selection procedure has been applied in order to make computation more efficient, to improve the classification results and to manage some issues related to t…