A Novel Approach to Tongue Standardization and Feature Extraction

2020 ◽  
pp. 36-45
Author(s):  
Chenhao Wang ◽  
Camilla Cattaneo ◽  
Jing Liu ◽  
Wender Bredie ◽  
Ella Pagliarini ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Novel Approach

scholarly journals Unmanned Aerial Vehicle Operating Mode Classification Using Deep Residual Learning Feature Extraction

Aerospace ◽  
2021 ◽  
Vol 8 (3) ◽  
pp. 79
Author(s):  
Carolyn J. Swinney ◽  
John C. Woods
Keyword(s):  
Feature Extraction ◽  
Transfer Learning ◽  
Operating Mode ◽  
Classification Problem ◽  
Large Signal ◽  
Signal Representations ◽  
Novel Approach ◽  
Power Spectral ◽  
Aerial Vehicle ◽  
Deep Convolution Neural Network

Unmanned Aerial Vehicles (UAVs) undoubtedly pose many security challenges. We need only look to the December 2018 Gatwick Airport incident for an example of the disruption UAVs can cause. In total, 1000 flights were grounded for 36 h over the Christmas period which was estimated to cost over 50 million pounds. In this paper, we introduce a novel approach which considers UAV detection as an imagery classification problem. We consider signal representations Power Spectral Density (PSD); Spectrogram, Histogram and raw IQ constellation as graphical images presented to a deep Convolution Neural Network (CNN) ResNet50 for feature extraction. Pre-trained on ImageNet, transfer learning is utilised to mitigate the requirement for a large signal dataset. We evaluate performance through machine learning classifier Logistic Regression. Three popular UAVs are classified in different modes; switched on; hovering; flying; flying with video; and no UAV present, creating a total of 10 classes. Our results, validated with 5-fold cross validation and an independent dataset, show PSD representation to produce over 91% accuracy for 10 classifications. Our paper treats UAV detection as an imagery classification problem by presenting signal representations as images to a ResNet50, utilising the benefits of transfer learning and outperforming previous work in the field.

scholarly journals Optimized Periocular Template Selection for Human Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Sambit Bakshi ◽  
Pankaj K. Sa ◽  
Banshidhar Majhi
Keyword(s):  
Feature Extraction ◽  
Biometric System ◽  
Human Recognition ◽  
Novel Approach ◽  
Database Size ◽  
Periocular Region ◽  
Template Selection ◽  
Selection For ◽  
Computationally Intensive

A novel approach for selecting a rectangular template around periocular region optimally potential for human recognition is proposed. A comparatively larger template of periocular image than the optimal one can be slightly more potent for recognition, but the larger template heavily slows down the biometric system by making feature extraction computationally intensive and increasing the database size. A smaller template, on the contrary, cannot yield desirable recognition though the smaller template performs faster due to low computation for feature extraction. These two contradictory objectives (namely, (a) to minimize the size of periocular template and (b) to maximize the recognition through the template) are aimed to be optimized through the proposed research. This paper proposes four different approaches for dynamic optimal template selection from periocular region. The proposed methods are tested on publicly available unconstrained UBIRISv2 and FERET databases and satisfactory results have been achieved. Thus obtained template can be used for recognition of individuals in an organization and can be generalized to recognize every citizen of a nation.

scholarly journals A Novel Approach of CT Images Feature Analysis and Prediction to Screen for Corona Virus Disease (COVID-19)

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Ibrahim Selim ◽  
Hatem Awad A. Khater
Keyword(s):  
Feature Extraction ◽  
Virus Disease ◽  
Real Data ◽  
Ct Images ◽  
Learning Tools ◽  
Corona Virus ◽  
Novel Approach ◽  
Hybrid Classification ◽  
Hybrid Feature Extraction ◽  
First Time

The paper demonstrates the analysis of Corona Virus Disease based on a probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed approach for feature extraction. The combination of the conventional statistical and machine learning tools is applied for feature extraction from CT images through four images filters in combination with proposed composite hybrid feature extraction (CHFS). The selected features were classified by the stack hybrid classification system(SHC). Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

scholarly journals Spatial Domain-Based Nonlinear Residual Feature Extraction for Identification of Image Operations

2020 ◽  
Vol 10 (16) ◽  
pp. 5582
Author(s):  
Xiaochen Yuan ◽  
Tian Huang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Extraction Method ◽  
Spatial Domain ◽  
Feature Extraction Method ◽  
Novel Approach ◽  
Image Postprocessing ◽  
Learning Technique ◽  
Residual Values

In this paper, a novel approach that uses a deep learning technique is proposed to detect and identify a variety of image operations. First, we propose the spatial domain-based nonlinear residual (SDNR) feature extraction method by constructing residual values from locally supported filters in the spatial domain. By applying minimum and maximum operators, diversity and nonlinearity are introduced; moreover, this construction brings nonsymmetry to the distribution of SDNR samples. Then, we propose applying a deep learning technique to the extracted SDNR features to detect and classify a variety of image operations. Many experiments have been conducted to verify the performance of the proposed approach, and the results indicate that the proposed method performs well in detecting and identifying the various common image postprocessing operations. Furthermore, comparisons between the proposed approach and the existing methods show the superiority of the proposed approach.

scholarly journals A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

A Novel Approach for Detecting and Classifying Breast Cancer in Mammogram Images

2013 ◽  
Vol 9 (1) ◽  
pp. 21-39 ◽  
Author(s):  
S. Shanthi ◽  
V. Murali Bhaskaran
Keyword(s):  
Feature Extraction ◽  
Cancer Detection ◽  
Discrete Wavelet ◽  
Feature Extraction Method ◽  
Benchmark Data ◽  
Optimal Decisions ◽  
Novel Approach ◽  
Step Procedure ◽  
Aided Diagnosis ◽  
Mammogram Images

This study uses data mining techniques for computer-aided diagnosis that involves the feature extraction for cancer detection, so as to help doctors towards making optimal decisions quickly and accurately. Features play an important role in detecting the cancer in the digital mammogram and feature extraction stage is the most vital and difficult stage. In this paper, an enhanced feature extraction method named Multiscale Surrounding Region Dependence Method (MSRDM) is proposed to be effective in classifying the mammogram images into normal or benign or malignant. This proposed system is based on a four-step procedure: Regions of Interest specification, two dimensional discrete wavelet transformation, and multiscale surrounding region dependence matrix computation and feature extraction. The performance of the proposed feature set is compared with the conventional texture-analysis methods such as gray level cooccurence matrix features and surrounding region dependence method features. Experiments have been conducted on both real and benchmark data and the results have been proved to be progressive.

A Novel Approach to Detect Note Onset Using Musical Wavelet

2014 ◽  
Vol 651-653 ◽  
pp. 2094-2098
Author(s):  
Wen Ming Gui ◽  
Yu Feng Chen ◽  
Rui Fan Liu
Keyword(s):  
Feature Extraction ◽  
Decomposition Method ◽  
Experimental Results ◽  
Signal Decomposition ◽  
Onset Detection ◽  
Musical Note ◽  
Frequency Structure ◽  
Novel Approach ◽  
Extraction Stage

In this paper, a novel approach to detect note onset was proposed. At the signal transform stage of the note onset detection, a new musical signal decomposition method based on musical wavelet was bring forward according to the frequency structure of the musical note. At the feature extraction stage, the partial flux feature was proposed in light of the point that some of the partials must change at the time of the onsets. The experimental results indicted that the proposed approach was theoretically feasible and practically effective.

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