scholarly journals Revealing the Characteristics of Balinese Dance Maestros by Analyzing Silhouette Sequence Patterns Using Bag of Visual Movement with HoG and SIFT Features

2021 ◽  
Vol 15 (1) ◽  
pp. 89-104
Author(s):  
Made Windu Antara Kesiman ◽  
I Made Dendi Maysanjaya ◽  
I Made Ardwi Pradnyana ◽  
I Made Gede Sunarya ◽  
Putu Hendra Suputra
Keyword(s):  
Feature Extraction ◽  
Movement Patterns ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Dance Movement ◽  
Visual Movement ◽  
Invariant Features ◽  
Dance Movements ◽  
Sequence Patterns ◽  
Sift Features

The aim of this research was to reveal and explore the characteristics of Balinese dance maestros by analyzing silhouette sequence patterns of Balinese dance movements. A method and complete scheme for the extraction and construction of silhouette features of Balinese dance movements are proposed to enable performing quantitative analysis of Balinese dance movement patterns. Two different feature extraction methods, namely the Histogram of Gradient (HoG) feature and the Scale Invariant Features Transform (SIFT) descriptor, were used to build the final feature, called the Bag of Visual Movement (BoVM) feature. This research also makes a technical contribution with the proposal of quantifying measures to analyze the movement patterns of Balinese dances and to create the profile and characteristics of dance maestros/creators. Eight Balinese dances from three different Balinese dance maestros were analyzed in this work. Based on the experimental results, the proposed method was able to visually detect and extract patterns from silhouette sequences of Balinese dance movements. Quantitatively, the pattern measures for profiling of Balinese dances and maestros revealed a number of significant characteristics of different dances and different maestros.

Human–Computer Interactive Gesture Feature Capture and Recognition in Virtual Reality

2020 ◽  
pp. 106480462092413 ◽  
Author(s):  
Fan Zhang
Keyword(s):  
Neural Network ◽  
Virtual Reality ◽  
Feature Extraction ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Extraction Methods ◽  
Scale Invariant ◽  
Feature Vectors ◽  
Accuracy And Precision ◽  
Scale Invariant Feature

With the development of computer technology, the simulation authenticity of virtual reality technology is getting higher and higher, and the accurate recognition of human–computer interaction gestures is also the key technology to enhance the authenticity of virtual reality. This article briefly introduced three different gesture feature extraction methods: scale invariant feature transform, local binary pattern and histogram of oriented gradients (HOG), and back-propagation (BP) neural network for classifying and recognizing different gestures. The gesture feature vectors obtained by three feature extraction methods were used as input data of BP neural network respectively and were simulated in MATLAB software. The results showed that the information of feature gesture diagram extracted by HOG was the closest to the original one; the BP neural network that applied HOG extracted feature vectors converged to stability faster and had the smallest error when it was stable; in the aspect of gesture recognition, the BP neural network that applied HOG extracted feature vector had higher accuracy and precision and lower false alarm rate.

Wavelets-Based Feature Extraction for Texture Classification

2010 ◽  
Vol 97-101 ◽  
pp. 1273-1276 ◽  
Author(s):  
Gang Yu ◽  
Ying Zi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Energy Content ◽  
Texture Classification ◽  
Wavelet Coefficient ◽  
Discrimination Performance ◽  
Extraction Methods ◽  
Rotation Invariant ◽  
Scale Invariant ◽  
Feature Extraction Method

Texture classification is a necessary task in a wider variety of application areas such as manufacturing, textiles, and medicine. In this paper, we propose a novel wavelet-based feature extraction method for robust, scale invariant and rotation invariant texture classification. The method divides the 2-D wavelet coefficient matrices into 2-D clusters and then computes features from the energies inherent in these clusters. The features that contain the information effective for classifying texture images are computed from the energy content of the clusters, and these feature vectors are input to a neural network for texture classification. The results show that the discrimination performance obtained with the proposed cluster-based feature extraction method is superior to that obtained using conventional feature extraction methods, and robust to the rotation and scale invariant texture classification.

scholarly journals Fundus Image Registration Technique Based on Local Feature of Retinal Vessels

2021 ◽  
Vol 11 (23) ◽  
pp. 11201
Author(s):  
Roziana Ramli ◽  
Khairunnisa Hasikin ◽  
Mohd Yamani Idna Idris ◽  
Noor Khairiah A. Karim ◽  
Ainuddin Wahid Abdul Wahab
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Extraction Methods ◽  
Local Feature ◽  
Retinal Vessels ◽  
Feature Points ◽  
Fundus Image ◽  
Scale Invariant ◽  
Feature Based ◽  
Image Pairs

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

scholarly journals Inter-Device Periocular Recognition Under Near-Infrared Light

2016 ◽  
Vol 21 (4) ◽  
pp. 33-44
Author(s):  
Michał Włodarczyk ◽  
Paweł Krotewicz ◽  
Damian Kacperski ◽  
Wojciech Sankowski ◽  
Kamil Grabowski
Keyword(s):  
Feature Extraction ◽  
Near Infrared ◽  
Local Binary Patterns ◽  
Extraction Methods ◽  
Infrared Light ◽  
Scale Invariant ◽  
Histograms Of Oriented Gradients ◽  
Decision Module ◽  
The Impact ◽  
Periocular Recognition

Abstract Periocular biometrics is a relatively new field of research, and only several publications on this topic can be found in the literature. It can become a promising feature that can be used independently or as a complement to other biometrics. In this work, the recognition rates of periocular biometrics on a single acquisition device and inter-device database is verified and the impact of different image sources on the performance of recognition algorithms is investigated. For this purpose a NearInfrared Light database was collected. The database contains images taken by two acquisition devices. In order to test the periocular biometric trait, three feature extraction methods are chosen: Histograms of Oriented Gradients, Local Binary Patterns and Scale Invariant Feature Transform. The fusion of these methods is also proposed and it is tested on inter-device database. The feasibility of applying periocular recognition as an individual decision module for a biometric system is assessed. Experimental results yield Equal Error Rate of 17.65 for right eye using inter-device database of 640 gallery periocular images for each eye side taken from 32 different individuals (20 images per individual for each eye side). These results are obtained by the optimal weighted sum fusion of the three feature extraction methods.

Movement Analysis of Philippine Folk Dance Itik-itik

2019 ◽  
Vol 2 (1) ◽  
pp. 56-63
Author(s):  
Pangilinan Math C ◽  
Fontanilla Lyndo V ◽  
Pineda Israel C ◽  
Rocelle E Agtang ◽  
Soriano Ria M ◽  
...  
Keyword(s):  
Movement Analysis ◽  
Adductor Muscle ◽  
Mechanical Analysis ◽  
Muscle Group ◽  
Upper Body ◽  
Folk Dance ◽  
Dance Movement ◽  
Dance Teacher ◽  
Hip Muscle ◽  
Dance Movements

The purpose of the study was to describe and analyze the dance movements of the Philippine folk dance Itik-itik. The researchers adopted the movement analysis method similar to that of Mackenzie that involves the (1) description of the actual movements which occur at the joints involved; (2) the plane in which the movement occurs; and (3) the muscles producing the movement (agonist & antagonist). In addition, similar to the study of Martin and Miller, the researchers also had done a mechanical analysis on the lever type involved in the execution of the dance movement in terms of force, axis, and resistance. Results revealed that the prominent dance steps in the Philippine local dance Itik-itik are the (1) running, (2) cross step, slide close, slide close step, (3) heel, close-ball, close arm, (4) step, slide-close, slide, (5) arms extension/flexion, and (6) flapping of the arms. The joints involved are the shoulder and hip muscle which are ball and socket type of joints; and elbow, knee and ankle which are hinge joints. The major muscles involved in the dance for the lower body include the quadriceps, hamstring muscle group, adductor muscle group, calves and gluts. While for the upper body muscles involved are the pectoralis major, latissimus dorsi, deltoid, trapezius, biceps, and triceps muscles. The type of lever used in performing the dance comprise majority of 1st class and 3rd class levers. By knowing the muscles involved in the dance the dance teacher may be able to devise activities to gradually prepare the prime mover muscles before the actual execution for injury prevention. Thus, the movements in the dance may improve the health and skill related fitness of the performers.

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.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Sign in / Sign up

Export Citation Format

Share Document