A new approach for EEG feature extraction in P300-based lie detection

This study aimed to evaluate a lie-detection system by nonlinear analysis of electrooculography (EOG) signals in the polygraph test. The physiological signals such as photoplethysmography signal, electrodermal response, respiratory changes as well as EOG signal were recorded based on a Control Question Test (CQT). Three psychophysiological signals were evaluated based on the extracted features in the seven-position numerical scoring. The dynamics of EOG signals in subjects that had a total negative score were analyzed by recurrence quantification analysis (RQA). The six values of RQA were calculated to analyze the EOG signals in relevant questions compared to other questions. A one-way ANOVA with multiple comparisons was performed to evaluate the extracted variables in different questions. Eleven subjects had a total score of [Formula: see text]2 and less, so the EOG signals of these subjects were evaluated. Recurrence plots (RPs) of EOG signals showed clear differences in the two types of questions. The recurrence quantification analysis of vertical EOG signal indicated that [Formula: see text] and determinism (DET) values decreased significantly for relevant questions compared to other questions. Moreover, a significant decrease was observed in all RQA parameters except RR for the horizontal EOG signal. The differences of EOG signals in relevant questions observed using RPs and RQA were possibly related to the underlying changes in rapid eye movement due to the stress. The results of this study illustrate that the RQA technique is well suited to analyze the EOG signals in the detection of deception.

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A new approach for feature extraction with applications to Automatic Writer Recognition

2014 4th International Conference on Computer and Knowledge Engineering (ICCKE) ◽

10.1109/iccke.2014.6993341 ◽

2014 ◽

Cited By ~ 1

Author(s):

Ali Reza Zarei ◽

Reza Safabakhsh

Keyword(s):

Feature Extraction ◽

New Approach ◽

Writer Recognition

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A new approach for spectral feature extraction and for unsupervised classification of hyperspectral data based on the Gaussian mixture model

Remote Sensing Reviews ◽

10.1080/02757250109532431 ◽

2001 ◽

Vol 20 (2) ◽

pp. 123-167 ◽

Cited By ~ 6

Author(s):

A. Koltunov ◽

E. Ben‐Dor

Keyword(s):

Feature Extraction ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Spectral Feature ◽

Unsupervised Classification ◽

Gaussian Mixture ◽

Hyperspectral Data ◽

New Approach

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SYMMETRY AXIS BASED OBJECT RECOGNITION UNDER TRANSLATION, ROTATION AND SCALING

International Journal of Neural Systems ◽

10.1142/s0129065709001811 ◽

2009 ◽

Vol 19 (01) ◽

pp. 25-42 ◽

Cited By ~ 4

Author(s):

MASHUD HYDER ◽

MD. MONIRUL ISLAM ◽

M. A. H. AKHAND ◽

KAZUYUKI MURASE

Keyword(s):

Feature Extraction ◽

Object Recognition ◽

Symmetry Axis ◽

Recognition Performance ◽

Recognition System ◽

New Approach ◽

Invariant Object Recognition ◽

Different Shapes ◽

Axis Of Symmetry ◽

Concentric Circles

This paper presents a new approach, known as symmetry axis based feature extraction and recognition (SAFER), for recognizing objects under translation, rotation and scaling. Unlike most previous invariant object recognition (IOR) systems, SAFER puts emphasis on both simplicity and accuracy of the recognition system. To achieve simplicity, it uses simple formulae for extracting invariant features from an object. The scheme used in feature extraction is based on the axis of symmetry and angles of concentric circles drawn around the object. SAFER divides the extracted features into a number of groups based on their similarity. To improve the recognition performance, SAFER uses a number of neural networks (NNs) instead of single NN are used for training and recognition of extracted features. The new approach, SAFER, has been tested on two of real world problems i.e., English characters with two different fonts and images of different shapes. The experimental results show that SAFER can produce good recognition performance in comparison with other algorithms.

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A NEW APPROACH FOR URBAN ROADS DETECTION USING LASER DATA AND AERIAL DIGITAL IMAGES

Journal of Urban and Environmental Engineering ◽

10.4090/juee.2016.v10n1.083097 ◽

2016 ◽

Vol 10 (1) ◽

pp. 83-97 ◽

Cited By ~ 1

Author(s):

Francisco Assis Da Silva ◽

Anderson Akio Gohara ◽

Mário Augusto Pazoti ◽

Danillo Roberto Pereira ◽

Almir Olivette Artero ◽

...

Keyword(s):

Feature Extraction ◽

Digital Image ◽

Aerial Images ◽

Automatic Extraction ◽

New Approach ◽

Additional Information ◽

Automatic Feature Extraction ◽

Laser Data ◽

Sensitivity Rate ◽

Road Segments

The automatic feature extraction from digital aerial images is not a trivial task mainly due to occlusion problems, shadows and different viewpoints. To obtain an improved feature extraction we used laser data, which have additional information such as height and material type of the surface. In this paper we performed the combination of digital image and laser data in order to improve the results of automatic extraction of urban roads. Initially, the urban roads were detected from the response of laser information; in the sequence we applied two different approaches to connect the disconnected road segments. The results were very promising, with sensitivity rate of 92%.

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