scholarly journals GPU-accelerated image segmentation based on level sets and multiple texture features

2020 ◽  
Author(s):  
Daniel Reska ◽  
Marek Kretowski
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Level Set ◽  
High Performance ◽  
Texture Features ◽  
Extraction Methods ◽  
Multi Stage ◽  
Multiple Feature ◽  
Feature Extraction And Selection ◽  
Contour Evolution

Abstract In this paper, we present a fast multi-stage image segmentation method that incorporates texture analysis into a level set-based active contour framework. This approach allows integrating multiple feature extraction methods and is not tied to any specific texture descriptors. Prior knowledge of the image patterns is also not required. The method starts with an initial feature extraction and selection, then performs a fast level set-based evolution process and ends with a final refinement stage that integrates a region-based model. The presented implementation employs a set of features based on Grey Level Co-occurrence Matrices, Gabor filters and structure tensors. The high performance of feature extraction and contour evolution stages is achieved with GPU acceleration. The method is validated on synthetic and natural images and confronted with results of the most similar among the accessible algorithms.

Feature Extraction of Melanoma Data using Machine Learning Techniques

2021 ◽  
pp. 5352-5360
Author(s):  
R.Veeralakshmi, Dr.K.Merriliance
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Skin Diseases ◽  
Region Of Interest ◽  
Texture Features ◽  
Extraction Methods ◽  
Input Image ◽  
The Body ◽  
Machine Learning Techniques ◽  
Segmentation Technique

In our body the skin is the largest organ, it protects from injury, infection and also helps to maintain the temperature of the body. Melanoma Skin cancer is one of the most dangerous skin diseases and it is caused by an uncontrolled growth of abnormal skin cells, by ultraviolet radiation from sunshine. Melanoma is more common among white skins such as Americans than in darker skins. The digital lesion images have been analyzed based on image acquisition, pre-processing, and image segmentation technique. The image segmentation technique is applied to easily identify the affected portion in the skin input image. The images are enhanced using morphological filters and sharpen region of interest in an image, enhancement method enhanced the non-uniform background illumination and converts the input image into a binary image too easy to identify foreground objects. The mole of melanoma is segmented from the background using Active Contour algorithm. After that, the feature extraction methods such as Kernel PCA, SIFT are used to extract melanoma affected area in an image based on their intensity and texture features.

PIXEL-BASED TEXTURE CLASSIFICATION BY INTEGRATION OF MULTIPLE FEATURE EXTRACTION METHODS EVALUATED OVER MULTISIZED WINDOWS

2007 ◽  
Vol 21 (07) ◽  
pp. 1159-1170 ◽  
Author(s):  
DOMENEC PUIG ◽  
MIGUEL ANGEL GARCIA
Keyword(s):  
Feature Extraction ◽  
Texture Classification ◽  
Texture Features ◽  
Extraction Methods ◽  
Input Image ◽  
Integration Strategy ◽  
Multiple Feature

This paper presents a pixel-based texture classifier oriented to the identification of texture models that can be present in an input image, given a set of models known in advance. The proposed methodology is based on the integration of texture features generated by texture methods that belong to different families, which are evaluated over multiple windows of different sizes. This is a novelty with respect to the current texture classifiers, which are based on specific families of texture methods evaluated over single windows of a size defined empirically. Experiments show that this integration strategy produces better results than classical texture classifiers based on specific families of texture methods.

scholarly journals VESSEL CLASSIFICATION IN COSMO-SKYMED SAR DATA USING HIERARCHICAL FEATURE SELECTION

2015 ◽  
Vol XL-7/W3 ◽  
pp. 975-982 ◽  
Author(s):  
A. Makedonas ◽  
C. Theoharatos ◽  
V. Tsagaris ◽  
V. Anastasopoulos ◽  
S. Costicoglou
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Selection Procedure ◽  
Texture Features ◽  
Statistical Characteristics ◽  
Selection Scheme ◽  
Feature Extraction And Selection ◽  
Sar Data ◽  
Ship Classification ◽  
Analysis Scale

SAR based ship detection and classification are important elements of maritime monitoring applications. Recently, high-resolution SAR data have opened new possibilities to researchers for achieving improved classification results. In this work, a hierarchical vessel classification procedure is presented based on a robust feature extraction and selection scheme that utilizes scale, shape and texture features in a hierarchical way. Initially, different types of feature extraction algorithms are implemented in order to form the utilized feature pool, able to represent the structure, material, orientation and other vessel type characteristics. A two-stage hierarchical feature selection algorithm is utilized next in order to be able to discriminate effectively civilian vessels into three distinct types, in COSMO-SkyMed SAR images: cargos, small ships and tankers. In our analysis, scale and shape features are utilized in order to discriminate smaller types of vessels present in the available SAR data, or shape specific vessels. Then, the most informative texture and intensity features are incorporated in order to be able to better distinguish the civilian types with high accuracy. A feature selection procedure that utilizes heuristic measures based on features’ statistical characteristics, followed by an exhaustive research with feature sets formed by the most qualified features is carried out, in order to discriminate the most appropriate combination of features for the final classification. In our analysis, five COSMO-SkyMed SAR data with 2.2m x 2.2m resolution were used to analyse the detailed characteristics of these types of ships. A total of 111 ships with available AIS data were used in the classification process. The experimental results show that this method has good performance in ship classification, with an overall accuracy reaching 83%. Further investigation of additional features and proper feature selection is currently in progress.

INVESTIGATING LONG-TERM EFFECTS OF FEATURE EXTRACTION METHODS FOR CONTINUOUS EMG PATTERN CLASSIFICATION

2012 ◽  
Vol 11 (04) ◽  
pp. 1250028 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
PORNCHAI PHUKPATTARANONT ◽  
CHUSAK LIMSAKUL
Keyword(s):  
Feature Extraction ◽  
Dimensionality Reduction ◽  
Pattern Classification ◽  
Time Domain ◽  
Time Scale ◽  
Extraction Methods ◽  
Feature Sets ◽  
Scale Feature ◽  
Multiple Feature

Based on recent advances in modern multifunction myoelectric control devices, a combination of effective feature extraction and classification methods is required to enhance the high classification performance, especially in accuracy viewpoint. However, for realizing practical applications of myoelectric control, the effect of long-term usage or reusability is one of the challenging issues that should be more carefully considered, whereas only a few works have investigated this effect in recent. In this study, the behavior of the state-of-the-art multiple feature extraction methods was investigated with the fluctuating electromyography (EMG) signals recorded during four different days with a large number of trials and subjects. To this end, seven multiple feature sets were compared consisting features based on time domain and time-scale representation. Two major points were emphasized: (1) the optimal robust feature set for continuous (both transient and steady-state signals) EMG pattern classification and (2) the effect of fluctuating EMG signals with feature extraction methods for long-term usage. From the classification results, time domain feature sets yielded better performance than time-scale feature sets. The classification accuracies of the time-domain-feature sets had always achieved above 80% by using linear discriminant analysis (LDA) as a classifier and uncorrelated LDA (ULDA) as a dimensionality reduction, whereas the classification accuracies of the time-scale-feature sets were lower than 70% for the fluctuating EMG signals. The effect of dimensionality reduction for the classification of fluctuating EMG signals was also discussed.

scholarly journals Multi-Stage Feature Extraction and Classification for Ship-Radiated Noise

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 112
Author(s):  
Hamada Esmaiel ◽  
Dongri Xie ◽  
Zeyad A. H. Qasem ◽  
Haixin Sun ◽  
Jie Qi ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Extraction Methods ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Radiated Noise ◽  
Passive Sonar ◽  
Multi Stage ◽  
Mode Decomposition

Due to the complexity and unique features of the hydroacoustic channel, ship-radiated noise (SRN) detected using a passive sonar tends mostly to distort. SRN feature extraction has been proposed to improve the detected passive sonar signal. Unfortunately, the current methods used in SRN feature extraction have many shortcomings. Considering this, in this paper we propose a new multi-stage feature extraction approach to enhance the current SRN feature extractions based on enhanced variational mode decomposition (EVMD), weighted permutation entropy (WPE), local tangent space alignment (LTSA), and particle swarm optimization-based support vector machine (PSO-SVM). In the proposed method, first, we enhance the decomposition operation of the conventional VMD by decomposing the SRN signal into a finite group of intrinsic mode functions (IMFs) and then calculate the WPE of each IMF. Then, the high-dimensional features obtained are reduced to two-dimensional ones by using the LTSA method. Finally, the feature vectors are fed into the PSO-SVM multi-class classifier to realize the classification of different types of SRN sample. The simulation and experimental results demonstrate that the recognition rate of the proposed method overcomes the conventional SRN feature extraction methods, and it has a recognition rate of up to 96.6667%.

scholarly journals fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network

2021 ◽  
Vol 12 (1) ◽  
pp. 81-105 ◽  
Author(s):  
Senuri De Silva ◽  
Sanuwani Udara Dayarathna ◽  
Gangani Ariyarathne ◽  
Dulani Meedeniya ◽  
Sampath Jayarathna
Keyword(s):  
Feature Extraction ◽  
Low Frequency ◽  
Extraction Methods ◽  
Functional Magnetic Resonance ◽  
Hyperactivity Disorder ◽  
Specificity And Sensitivity ◽  
Multiple Feature ◽  
Clinical Data Analysis ◽  
Extraction Model ◽  
Neuroimaging Techniques

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to obtain the specificity and sensitivity. This helps to determine the best features for ADHD classification using convolutional neural networks (CNN). The methodology using fALFF and ReHo resulted in an accuracy of 67%, while SBC gained an accuracy between 84% and 86% and sensitivity between 65% and 75%.

scholarly journals Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

2017 ◽  
Vol 232 (15) ◽  
pp. 2711-2722 ◽  
Author(s):  
Juan Jose Saucedo-Dorantes ◽  
Miguel Delgado-Prieto ◽  
Roque Alfredo Osornio-Rios ◽  
Rene de Jesus Romero-Troncoso
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Condition Monitoring ◽  
High Performance ◽  
Industrial Applications ◽  
Feature Reduction ◽  
Data Set ◽  
Multi Stage ◽  
Statistical Time ◽  
Reduction Approach

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the linear discriminant analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology are evaluated by considering a complete data set of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.

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