Surgical skill level assessment using automatic feature extraction methods

2018 ◽  
Author(s):  
Robert Rege ◽  
Ann Majewicz ◽  
Marzieh Ershad
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Skill Level ◽  
Surgical Skill ◽  
Automatic Feature Extraction

scholarly journals Offline Signature Identification and Verification Based on Capsule Representations

2020 ◽  
Vol 20 (5) ◽  
pp. 60-67
Author(s):  
Dilara Gumusbas ◽  
Tulay Yildirim
Keyword(s):  
Feature Extraction ◽  
Daily Life ◽  
Extraction Methods ◽  
Signature Verification ◽  
Verification Task ◽  
Feature Representations ◽  
Automatic Feature Extraction ◽  
Offline Signature Verification ◽  
Orientation Information ◽  
Trained Network

AbstractOffline signature is one of the frequently used biometric traits in daily life and yet skilled forgeries are posing a great challenge for offline signature verification. To differentiate forgeries, a variety of research has been conducted on hand-crafted feature extraction methods until now. However, these methods have recently been set aside for automatic feature extraction methods such as Convolutional Neural Networks (CNN). Although these CNN-based algorithms often achieve satisfying results, they require either many samples in training or pre-trained network weights. Recently, Capsule Network has been proposed to model with fewer data by using the advantage of convolutional layers for automatic feature extraction. Moreover, feature representations are obtained as vectors instead of scalar activation values in CNN to keep orientation information. Since signature samples per user are limited and feature orientations in signature samples are highly informative, this paper first aims to evaluate the capability of Capsule Network for signature identification tasks on three benchmark databases. Capsule Network achieves 97 96, 94 89, 95 and 91% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 and 32×32 resolutions, which are lower than usual, respectively. The second aim of the paper is to generalize the capability of Capsule Network concerning the verification task. Capsule Network achieves average 91, 86, and 89% accuracy on CEDAR, GPDS-100 and MCYT databases for 64×64 resolutions, respectively. Through this evaluation, the capability of Capsule Network is shown for offline verification and identification tasks.

Ensemble Musicians’ Synchronization With Conductors’ Gestures: An Automated Feature-Extraction Analysis

2006 ◽  
Vol 24 (2) ◽  
pp. 189-200 ◽  
Author(s):  
Geoff Luck ◽  
Petri Toiviainen
Keyword(s):  
Feature Extraction ◽  
Motion Capture ◽  
Audio Signal ◽  
Vertical Axis ◽  
Extraction Methods ◽  
Automatic Feature Extraction ◽  
Movement Data ◽  
Negative Acceleration ◽  
Optical Motion ◽  
Optical Motion Capture

Previous work suggests that the perception of a visual beat in conductors’ gestures is related to certain physical characteristics of the movements they produce, most notably to periods of negative acceleration, and low position in the vertical axis. These findings are based on studies that have presented participants with somewhat simple gestures, and in which participants have been required to simply tap in time with the beat. Thus, it is not clear how generalizable these findings are to real-world conducting situations, in which a conductor uses considerably more complex gestures to direct an ensemble of musicians playing actual instruments. The aims of the present study were to examine the features of conductors’ gestures with which ensemble musicians synchronize their performance in an ecologically valid setting and to develop automatic feature extraction methods for the analysis of audio and movement data. An optical motion capture system was used to record the gestures of an expert conductor directing an ensemble of expert musicians over a 20-minute period. A simultaneous audio recording of the performance of the ensemble was also made and synchronized with the motion capture data. Four short excerpts were selected for analysis, two in which the conductor communicated the beat with high clarity, and two in which the beat was communicated with low clarity. Twelve movement variables were computationally extracted from the movement data and cross-correlated with the pulse of the ensemble’s performance, the latter based on the spectral flux of the audio signal. Results of the analysis indicated that the ensemble’s performance tended to be most highly synchronized with periods of maximal deceleration along the trajectory, followed by periods of high vertical velocity (a higher correlation than deceleration but a longer delay).

scholarly journals Hybrid CNN-LSTM Model for Answer Identification

2019 ◽  
Vol 8 (3) ◽  
pp. 1163-1166
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Comparative Analysis ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Extraction Methods ◽  
Learning Methods ◽  
Automatic Feature Extraction

User quest for information has led to development of Question Answer (QA) system to provide relevant answers to user questions. The QA task are different than normal NLP tasks as they heavily depend to semantics and context of given data. Retrieving and predicting answers to verity of questions require understanding of question, relevance with context and identifying and retrieving of suitable answers. Deep learning helps to produce impressive performance as it employs deep neural network with automatic feature extraction methods. The paper proposes a hybrid model to identify suitable answer for posed question. The proposes power exploits the power of CNN for extracting features and ability of LSTM for considering long term dependencies and semantic of context and question. Paper provides a comparative analysis on deep learning methods useful for predicting answer with the proposed method .The model is implemented on twenty tasks of babI dataset of Facebook .

scholarly journals Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

2021 ◽  
Author(s):  
B Peng ◽  
S Wan ◽  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Genetic Programming ◽  
Domain Knowledge ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
K Nearest Neighbors ◽  
Automatic Feature Extraction ◽  
Diagnosis Approach

IEEE Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

scholarly journals Beyond Speech: Generalizing D-Vectors for Biometric Verification

2019 ◽  
Vol 33 ◽  
pp. 842-849 ◽  
Author(s):  
Jacob Baldwin ◽  
Ryan Burnham ◽  
Andrew Meyer ◽  
Robert Dora ◽  
Robert Wright
Keyword(s):  
Feature Extraction ◽  
Cyber Security ◽  
Speaker Identification ◽  
State Of The Art ◽  
Extraction Methods ◽  
High Accuracy ◽  
Automatic Feature Extraction ◽  
Biometric Verification ◽  
Generalized Framework ◽  
Data Requirements

Deep learning based automatic feature extraction methods have radically transformed speaker identification and facial recognition. Current approaches are typically specialized for individual domains, such as Deep Vectors (D-Vectors) for speaker identification. We provide two distinct contributions: a generalized framework for biometric verification inspired by D-Vectors and novel models that outperform current stateof-the-art approaches. Our approach supports substitution of various feature extraction models and improves the robustness of verification tests across domains. We demonstrate the framework and models for two different behavioral biometric verification problems: keystroke and mobile gait. We present a comprehensive empirical analysis comparing our framework to the state-of-the-art in both domains. Our models perform verification with higher accuracy using orders of magnitude less data than state-of-the-art approaches in both domains. We believe that the combination of high accuracy and practical data requirements will enable application of behavioral biometric models outside of the laboratory in support of much-needed improvements to cyber security.

scholarly journals Automatic Feature Extraction and Construction Using Genetic Programming for Rotating Machinery Fault Diagnosis

2021 ◽  
Author(s):  
B Peng ◽  
S Wan ◽  
Ying Bi ◽  
Bing Xue ◽  
Mengjie Zhang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Genetic Programming ◽  
Domain Knowledge ◽  
Rolling Bearing ◽  
Extraction Methods ◽  
Rotating Machinery ◽  
K Nearest Neighbors ◽  
Automatic Feature Extraction ◽  
Diagnosis Approach

IEEE Feature extraction is an essential process in the intelligent fault diagnosis of rotating machinery. Although existing feature extraction methods can obtain representative features from the original signal, domain knowledge and expert experience are often required. In this article, a novel diagnosis approach based on evolutionary learning, namely, automatic feature extraction and construction using genetic programming (AFECGP), is proposed to automatically generate informative and discriminative features from original vibration signals for identifying different fault types of rotating machinery. To achieve this, a new program structure, a new function set, and a new terminal set are developed in AFECGP to allow it to detect important subband signals and extract and construct informative features, automatically and simultaneously. More important, AFECGP can produce a flexible number of features for classification. Having the generated features, k-Nearest Neighbors is employed to perform fault diagnosis. The performance of the AFECGP-based fault diagnosis approach is evaluated on four fault diagnosis datasets of varying difficulty and compared with 14 baseline methods. The results show that the proposed approach achieves better fault diagnosis accuracy on all the datasets than the competitive methods and can effectively identify different fault conditions of rolling bearing, gear, and rotor.

A Review on Feature Extraction Methods for Image Analysis

2020 ◽  
Author(s):  
Vricha Chavan ◽  
​Jimit Shah ◽  
Mrugank Vora ◽  
Mrudula Vora ◽  
Shubhashini Verma
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Extraction Methods

scholarly journals Feature Extraction for Finger-Vein-Based Identity Recognition

2021 ◽  
Vol 7 (5) ◽  
pp. 89
Author(s):  
George K. Sidiropoulos ◽  
Polixeni Kiratsa ◽  
Petros Chatzipetrou ◽  
George A. Papakostas
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Methods ◽  
Identity Recognition ◽  
Learning Framework ◽  
Finger Vein ◽  
The Past ◽  
Biometric Systems ◽  
Vein Recognition ◽  
Finger Vein Recognition

This paper aims to provide a brief review of the feature extraction methods applied for finger vein recognition. The presented study is designed in a systematic way in order to bring light to the scientific interest for biometric systems based on finger vein biometric features. The analysis spans over a period of 13 years (from 2008 to 2020). The examined feature extraction algorithms are clustered into five categories and are presented in a qualitative manner by focusing mainly on the techniques applied to represent the features of the finger veins that uniquely prove a human’s identity. In addition, the case of non-handcrafted features learned in a deep learning framework is also examined. The conducted literature analysis revealed the increased interest in finger vein biometric systems as well as the high diversity of different feature extraction methods proposed over the past several years. However, last year this interest shifted to the application of Convolutional Neural Networks following the general trend of applying deep learning models in a range of disciplines. Finally, yet importantly, this work highlights the limitations of the existing feature extraction methods and describes the research actions needed to face the identified challenges.

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