Search results for " recognition."

Unsupervised low-key image segmentation using curve evolution approach

2013

Low-key images widely exist in imaging-based systems such as space telescopes, medical imaging equipment, machine vision systems. Unsupervised low-key image segmentation is an important process for image analysis or digital measurement in these applications. In this paper, a novel active contour model with the probability density function (PDF) of gamma distribution for image segmentation is proposed. The flexible gamma distribution is used to describe both of the heterogeneous foreground and dark background in a low-key image. Besides, an unsupervised curve initialization method is also designed in this paper, which helps to accelerate the convergence speed of curve evolution. The effectiv…

Remote sensing image segmentation by active queries

2012

Active learning deals with developing methods that select examples that may express data characteristics in a compact way. For remote sensing image segmentation, the selected samples are the most informative pixels in the image so that classifiers trained with reduced active datasets become faster and more robust. Strategies for intelligent sampling have been proposed with model-based heuristics aiming at the search of the most informative pixels to optimize model's performance. Unlike standard methods that concentrate on model optimization, here we propose a method inspired in the cluster assumption that holds in most of the remote sensing data. Starting from a complete hierarchical descri…

Discovering single classes in remote sensing images with active learning

2012

When dealing with supervised target detection, the acquisition of labeled samples is one of the most critical phases: the samples must be yet representative of the class of interest, but must also be found among a vast majority of non-target examples. Moreover, the efficiency of the search is also an issue, since the samples labeled as background are not used by target detectors such as the support vector data description (SVDD). In this work we propose a competitive and effective approach to identify the most relevant training samples for one-class classification based on the use of an active learning strategy. The SVDD classifier is first trained with insufficient target examples. It is t…

Specific transfer effects following variable priority dual-task training in older adults

2016

International audience; Purpose: Past divided attention training studies in older adults have suggested that variable priority training (VPT) tends to show larger improvement than fixed priority training (FPT). However, it remains unclear whether VPT leads to larger transfer effects. Methods: In this study, eighty-three older adults aged between 55 and 65 received five 1-hour sessions of VPT, FPT or of an active placebo. VPT and FPT subjects trained on a complex dual-task condition with variable stimulus timings in order to promote more flexible and self-guided strategies with regard to attentional priority devoted to the concurrent tasks. Real-time individualized feedback was provided to e…

An architecture for autonomous agents exploiting conceptual representations

1998

An architecture for autonomous agents is proposed that integrates the functional and the behavioral approaches to robotics. The integration is based on the introduction of a conceptual level, linking together a subconceptual, behavioral, level, and a linguistic level, encompassing symbolic representation and data processing. The proposed architecture is described with reference to an experimental setup, in which the robot task is that of building a significant description of its working environment. © 1998 Elsevier Science B.V. All rights reserved.

A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

2015

This research aims to describe pattern recognition models for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. The early anticipation of anomalies can improve the rate of disease prevention. Here we present different learning techniques for predicting abnormalities and behavioural trends in various user contexts. In this paper we described a Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs. The outcomes of t…

Recognition of Falls and Daily Living Activities Using Machine Learning

2018

A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm. Re…

Mobile Phone Data Statistics as Proxy Indicator for Regional Economic Activity Assessment

2019

The mobile data analysis is an authoritative source of information for problems solving in the fields of human activity recognition, population dynamics, tourism, transport planning, traffics measuring, public administration and other activities and could be the source for valuable information as a proxy indicator. One of the obstacles to user data from mobile operators is compliance to the General Data Protection Regulation, so the development of data analytics approach that protects personal data without a necessity to identify mobility of particular persons was developed, that still provides economically relevant data. In the present research, the method for the economic activity assessm…

A Trajectory-Driven SISO mm-Wave Channel Model for a Human Activity Recognition

2021

ActRec: A Wi-Fi-Based Human Activity Recognition System

2020

In this paper, we develop a Wi-Fi-based activity recognition system called ActRec, which can be used for the remote monitoring of elderly. ActRec comprises two parts: radio-frequency (RF) sensing and machine learning. In the RF sensing part, two laptops act as transmitter and receiver to record the channel transfer function of an indoor environment. This RF data is collected in the presence of seven human participants performing three activities: walking, falling, and sitting. The RF data containing the fingerprints of user activity is then pre-processed with various signal processing algorithms to reduce noise effects and to estimate the mean Doppler shift (MDS) of each data sample. We pro…