30 results on '"Seung Min Park"'
Search Results
2. User Evaluation-based Travel Planning Modeling using Genetic Algorithm
- Author
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Sun-Ho Kwon and Seung-min Park
- Subjects
Computer science ,Genetic algorithm ,Data mining ,computer.software_genre ,computer - Published
- 2021
3. Harmony Generation for Optical Music Recognition-based Automatic Arrangement System
- Author
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Minhoon Lee, Mikyeong Moon, Seung-min Park, and Hobin Kim
- Subjects
Optical music recognition ,Harmony (color) ,Computer science ,Speech recognition - Published
- 2021
4. Modelling of Colored-Hearing Synesthesia Using Fuzzy Inference and Neural Network and Its Implementations of Smart Phone App
- Author
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Jeong Yong Woon, Seung-min Park, and KweeBoSIM
- Subjects
Fuzzy inference ,Smart phone ,Colored ,Artificial neural network ,Computer science ,business.industry ,Smartphone app ,medicine ,Artificial intelligence ,business ,Synesthesia ,medicine.disease ,Implementation - Published
- 2020
5. CNN-based Speech Emotion Recognition using Transfer Learning
- Author
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Seung-min Park, KweeBoSIM, and Choee Heewon
- Subjects
Computer science ,business.industry ,Deep learning ,Speech recognition ,Emotion recognition ,Artificial intelligence ,business ,Transfer of learning ,Convolutional neural network - Published
- 2019
6. CNN-based Classification of Brain Connectivity using Motor Imagery
- Author
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Kwee-Bo Sim, Hong Gi Yeom, and Seung-min Park
- Subjects
Motor imagery ,business.industry ,Computer science ,Pattern recognition ,Artificial intelligence ,business - Published
- 2019
7. Visual Speech Recognition of Korean Words Using Convolutional Neural Network
- Author
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Kwee-Bo Sim, Seung-Min Park, Sung-Won Lee, and Je-Hun Yu
- Subjects
Computational Theory and Mathematics ,Artificial Intelligence ,Logic ,Computer science ,Speech recognition ,Viola jones algorithm ,Signal Processing ,Convolutional neural network ,Human–robot interaction ,Computer Science Applications - Published
- 2019
8. Development of Inverter Characteristics Linear Motor Driver Using Embedded Board
- Author
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KweeBoSIM, Houng-Joong Kim, Sung-Won Lee, and Seung-min Park
- Subjects
Raspberry pi ,Computer science ,Inverter ,Motor control ,Linear motor ,Automotive engineering - Published
- 2019
9. Generating a Adhesive Nozzle Path by the Parameter-Setting-Free Harmony Search Algorithm for a Shoe-Upper Assembly Process
- Author
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Zong-Woo Geem, Seung-min Park, Kwee-Bo Sim, Tae-Hyoung Kim, Sung-Won Lee, In-Hoon Jang Geem, and Woo-Young Lee
- Subjects
Computer science ,Nozzle ,Path (graph theory) ,Process (computing) ,Harmony search ,Mechanical engineering ,Adhesive - Published
- 2018
10. Smart Door Lock Systems using encryption technology
- Author
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Kwee-Bo Sim, Seung-min Park, and Sung-Won Lee
- Subjects
Record locking ,Computer science ,business.industry ,0202 electrical engineering, electronic engineering, information engineering ,020207 software engineering ,020201 artificial intelligence & image processing ,02 engineering and technology ,Computer security ,computer.software_genre ,Encryption ,business ,computer - Published
- 2017
11. Implementation of Unsupervised Nonlinear Classifier with Binary Harmony Search Algorithm
- Author
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Kwee-Bo Sim, Tae-Ju Lee, Kwang-Eun Ko, Won-Ki Sung, and Seung-min Park
- Subjects
Nonlinear classifier ,Computer science ,business.industry ,Harmony search ,Binary number ,Pattern recognition ,Artificial intelligence ,Machine learning ,computer.software_genre ,business ,computer - Published
- 2013
12. Half-Against-Half Multi-class SVM Classify Physiological Response-based Emotion Recognition
- Author
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Kwee-Bo Sim, Kwang-Eun Ko, Makara Vanny, and Seung-Min Park
- Subjects
Visual perception ,business.industry ,Emotion classification ,Pattern recognition ,Blood volume pulse ,Machine learning ,computer.software_genre ,Class (biology) ,Disgust ,Support vector machine ,Kernel (statistics) ,Emotion recognition ,Artificial intelligence ,Psychology ,business ,computer - Abstract
The recognition of human emotional state is one of the most important components for efficient human-human and human-computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.
- Published
- 2013
13. Binary Classification Method using Invariant CSP for Hand Movements Analysis in EEG-based BCI System
- Author
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Kwang-Eun Ko, Thanh Ha Nguyen, Seung-min Park, and Kwee-Bo Sim
- Subjects
Left and right ,medicine.diagnostic_test ,Computer science ,business.industry ,Speech recognition ,Pattern recognition ,Electroencephalography ,Independent component analysis ,Hand movements ,Binary classification ,Auditory stimuli ,medicine ,Artificial intelligence ,Invariant (mathematics) ,business ,Brain–computer interface - Abstract
In this study, we proposed a method for electroencephalogram (EEG) classification using invariant CSP at special channels for improving the accuracy of classification. Based on the naive EEG signals from left and right hand movement experiment, the noises of contaminated data set should be eliminate and the proposed method can deal with the de-noising of data set. The considering data set are collected from the special channels for right and left hand movements around the motor cortex area. The proposed method is based on the fit of the adjusted parameter to decline the affect of invariant parts in raw signals and can increase the classification accuracy. We have run the simulation for hundreds time for each parameter and get averaged value to get the last result for comparison. The experimental results show the accuracy is improved more than the original method, the highest result reach to 89.74%.
- Published
- 2013
14. EEG Analysis Following Change in Hand Grip Force Level for BCI Based Robot Arm Force Control
- Author
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Kwee-Bo Sim, Kwang-Eun Ko, Tae-Ju Lee, Seung-min Park, and Dong-Eun Kim
- Subjects
Artifact (error) ,medicine.diagnostic_test ,business.industry ,Fast Fourier transform ,Spectral density ,Electroencephalography ,Linear discriminant analysis ,Signal ,body regions ,medicine ,Computer vision ,Artificial intelligence ,business ,Robotic arm ,Mathematics ,Brain–computer interface - Abstract
With Brain Computer Interface (BCI) system, a person with disabled limb could use this direct brain signal like electro- encephalography (EEG) to control a device such as the artifact arm. The precise force control for the artifact arm is neces- sary for this artificial limb system. To understand the relationship between control EEG signal and the gripping force of hands, We proposed a study by measuring EEG changes of three grades (25%, 50%, 75%) of hand grip MVC (Maximal Voluntary Contract). The acquired EEG signal was filtered to obtain power of three wave bands (alpha, beta, gamma) by using fast fourier transformation (FFT) and computed power spectrum. Then the power spectrum of three bands (alpha, beta and gamma) of three classes (MVC 25%, 50%, 75%) was classified by using PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The result showed that the power spectrum of EEG is increased at MVC 75% more than MVC 25%, and the correct classification rate was 52.03% for left hand and 77.7% for right hand.
- Published
- 2013
15. Discriminative Power Feature Selection Method for Motor Imagery EEG Classification in Brain Computer Interface Systems
- Author
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Xinyang Yu, Kwang-Eun Ko, Seung-Min Park, and Kwee-Bo Sim
- Subjects
Logic ,business.industry ,Computer science ,Feature vector ,Feature selection ,Pattern recognition ,Computer Science Applications ,Support vector machine ,ComputingMethodologies_PATTERNRECOGNITION ,Motor imagery ,Computational Theory and Mathematics ,Discriminative model ,Artificial Intelligence ,Feature (computer vision) ,Signal Processing ,Principal component analysis ,Artificial intelligence ,business ,Brain–computer interface - Abstract
Motor imagery classification in electroencephalography (EEG)-based brain-computer interface (BCI) systems is an important research area. To simplify the complexity of the classification, selected power bands and electrode channels have been widely used to extract and select features from raw EEG signals, but there is still a loss in classification accuracy in the state-of-the-art approaches. To solve this problem, we propose a discriminative feature extraction algorithm based on power bands with principle component analysis (PCA). First, the raw EEG signals from the motor cortex area were filtered using a bandpass filter with μ and β bands. This research considered the power bands within a 0.4 second epoch to select the optimal feature space region. Next, the total feature dimensions were reduced by PCA and transformed into a final feature vector set. The selected features were classified by applying a support vector machine (SVM). The proposed method was compared with a state-of-art power band feature and shown to improve classification accuracy.
- Published
- 2013
16. Optimal EEG Channel Selection using BPSO with Channel Impact Factor
- Author
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Jun-Yeup Kim, Seung-Min Park, Kwang-Eun Ko, and Kwee-Bo Sim
- Subjects
Imagination ,Computer science ,business.industry ,Interface (computing) ,media_common.quotation_subject ,Pattern recognition ,Overfitting ,computer.software_genre ,Support vector machine ,Motor imagery ,Data mining ,Artificial intelligence ,business ,computer ,Selection (genetic algorithm) ,Brain–computer interface ,media_common ,Communication channel - Abstract
Brain-computer interface based on motor imagery is a system that transforms a subject`s intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject`s limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).
- Published
- 2012
17. Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface
- Author
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Seung-Min Park, Kwang-Eun Ko, Kwee-Bo Sim, and Pharino Chum
- Subjects
education.field_of_study ,business.industry ,Feature extraction ,Population ,Pattern recognition ,Feature selection ,Support vector machine ,Motor imagery ,Binary classification ,Feature (computer vision) ,Linear regression ,Artificial intelligence ,education ,business ,Mathematics - Abstract
In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.
- Published
- 2012
18. ERS Feature Extraction using STFT and PSO for Customized BCI System
- Author
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Yong-Hoon Kim, Jun-Yeup Kim, Seung-Min Park, Kwee-Bo Sim, and Kwang-Eun Ko
- Subjects
Motor imagery ,medicine.diagnostic_test ,Computer science ,Speech recognition ,Feature extraction ,medicine ,Short-time Fourier transform ,Feature selection ,Neurophysiology ,Electroencephalography ,Synchronization ,Brain–computer interface - Abstract
This paper presents a technology for manipulating external devices by Brain Computer Interface (BCI) system. Recently, BCI based rehabilitation and assistance system for disabled people, such as patient of Spinal Cord Injury (SCI), general paralysis, and so on, is attracting tremendous interest. Especially, electroencephalogram (EEG) signal is used to organize the BCI system by analyzing the signals, such as evoked potential. The general findings of neurophysiology support an availability of the EEG-based BCI system. We concentrate on the event-related synchronization of motor imagery EEG signal, which have an affinity with an intention for moving control of external device. To analyze the brain activity, short-time Fourier transform and particle swarm optimization are used to optimal feature selection from the preprocessed EEG signals. In our experiment, we can verify that the power spectral density correspond to range mu-rhythm(~12Hz) have maximum amplitude among the raw signals and most of particles are concentrated in the corresponding region. Result shows accuracy of subject left hand 40% and right hand 38%.
- Published
- 2012
19. Improved Feature Extraction of Hand Movement EEG Signals based on Independent Component Analysis and Spatial Filter
- Author
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Kwee-Bo Sim, Kwang-Eun Ko, Seung-Min Park, and Thanh Ha Nguyen
- Subjects
Spatial filter ,Computer science ,business.industry ,Feature extraction ,Context (language use) ,Linear discriminant analysis ,Independent component analysis ,Noise ,ComputingMethodologies_PATTERNRECOGNITION ,Filter (video) ,Computer vision ,Artificial intelligence ,business ,Brain–computer interface - Abstract
In brain computer interface (BCI) system, the most important part is classification of human thoughts in order to translate into commands. The more accuracy result in classification the system gets, the more effective BCI system is. To increase the quality of BCI system, we proposed to reduce noise and artifact from the recording data to analyzing data. We used auditory stimuli instead of visual ones to eliminate the eye movement, unwanted visual activation, gaze control. We applied independent component analysis (ICA) algorithm to purify the sources which constructed the raw signals. One of the most famous spatial filter in BCI context is common spatial patterns (CSP), which maximize one class while minimize the other by using covariance matrix. ICA and CSP also do the filter job, as a raw filter and refinement, which increase the classification result of linear discriminant analysis (LDA).
- Published
- 2012
20. Development of Intelligent Green Fountain Culture System for Healthy Emotional Self-Concept
- Author
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Kwee-Bo Sim, Young-Hwan Lee, Jun-Yeup Kim, Seung-Min Park, and Kwang-Eun Ko
- Subjects
Limelight ,Engineering ,Architectural engineering ,business.industry ,Self-concept ,Programmable logic controller ,Space (commercial competition) ,law.invention ,Industrial PC ,law ,Control system ,The Internet ,Fountain ,business ,Telecommunications - Abstract
In the growing standard of people`s lives, we want desire to create eco-friendly water space what is called the Green Technology that is in the limelight. These green space is introduced the cultural contents and we use the water, music, and nature as tool of emotional verbalism. Presently, when we want to make scenario, water landscape scenario is made by director. but these systems have some disadvantages as the cost and limitation of direction. There is a growing interest in the integrated control system based on PC and Internet. In this paper, it is about fountain control system. Previous research area was only one using programmable logic controller or industrial PC. we proposed the development of intelligent green fountain culture system for healthy emotional self-concept. And we made automatic weather sensing system that is designed by the intelligent green fountain culture system to estimate the time-variant system.
- Published
- 2012
21. HMM-based Intent Recognition System using 3D Image Reconstruction Data
- Author
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Kwee-Bo Sim, Jun-Yeup Kim, Seung-Min Park, and Kwang-Enu Ko
- Subjects
business.industry ,Computer science ,Feature vector ,Feature extraction ,Optical flow ,Imitative learning ,Pattern recognition ,Kalman filter ,Gesture recognition ,Computer vision ,Artificial intelligence ,business ,Hidden Markov model ,Stereo camera - Abstract
The mirror neuron system in the cerebrum, which are handled by visual information-based imitative learning. When we observe the observer`s range of mirror neuron system, we can assume intention of performance through progress of neural activation as specific range, in include of partially hidden range. It is goal of our paper that imitative learning is applied to 3D vision-based intelligent system. We have experiment as stereo camera-based restoration about acquired 3D image our previous research Using Optical flow, unscented Kalman filter. At this point, 3D input image is sequential continuous image as including of partially hidden range. We used Hidden Markov Model to perform the intention recognition about performance as result of restoration-based hidden range. The dynamic inference function about sequential input data have compatible properties such as hand gesture recognition include of hidden range. In this paper, for proposed intention recognition, we already had a simulation about object outline and feature extraction in the previous research, we generated temporal continuous feature vector about feature extraction and when we apply to Hidden Markov Model, make a result of simulation about hand gesture classification according to intention pattern. We got the result of hand gesture classification as value of posterior probability, and proved the accuracy outstandingness through the result.
- Published
- 2012
22. Occluded Object Motion Estimation System based on Particle Filter with 3D Reconstruction
- Author
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Jun-Yeup Kim, Seung-Min Park, Junheong Park, Kwang-Eun Ko, and Kwee-Bo Sim
- Subjects
Logic ,business.industry ,Computer science ,3D reconstruction ,Optical flow ,Tracking system ,Tracing ,Object (computer science) ,Motion vector ,Computer Science Applications ,Computational Theory and Mathematics ,Artificial Intelligence ,Computer Science::Computer Vision and Pattern Recognition ,Motion estimation ,Signal Processing ,Computer vision ,Artificial intelligence ,business ,Particle filter - Abstract
This paper presents a method for occluded object based motion estimation and tracking system in dynamic image sequences using particle filter with 3D reconstruction. A unique characteristic of this study is its ability to cope with partial occlusion based continuous motion estimation using particle filter inspired from the mirror neuron system in human brain. To update a prior knowledge about the shape or motion of objects, firstly, fundamental 3D reconstruction based occlusion tracing method is applied and object landmarks are determined. And optical flow based motion vector is estimated from the movement of the landmarks. When arbitrary partial occlusions are occurred, the continuous motion of the hidden parts of object can be estimated by particle filter with optical flow. The resistance of the resulting estimation to partial occlusions enables the more accurate detection and handling of more severe occlusions.
- Published
- 2012
23. Development of Mirror Neuron System-based BCI System using Steady-State Visually Evoked Potentials
- Author
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Kwee-Bo Sim, Sang-Kyung Lee, Jun-Yeup Kim, Seung-Min Park, and Kwang-Enu Ko
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Visual perception ,medicine.diagnostic_test ,Computer science ,business.industry ,Bandwidth (signal processing) ,Electroencephalography ,Linear discriminant analysis ,medicine ,Computer vision ,Artificial intelligence ,Occipital lobe ,Affordance ,business ,Mirror neuron ,Brain–computer interface - Abstract
Steady-State Visually Evoked Potentials (SSVEP) are natural response signal associated with the visual stimuli with specific frequency. By using SSVEP, occipital lobe region is electrically activated as frequency form equivalent to stimuli frequency with bandwidth from 3.5Hz to 75Hz. In this paper, we propose an experimental paradigm for analyzing EEGs based on the properties of SSVEP. At first, an experiment is performed to extract frequency feature of EEGs that is measured from the image-based visual stimuli associated with specific objective with affordance and object-related affordance is measured by using mirror neuron system based on the frequency feature. And then, linear discriminant analysis (LDA) method is applied to perform the online classification of the objective pattern associated with the EEG-based affordance data. By using the SSVEP measurement experiment, we propose a Brain-Computer Interface (BCI) system for recognizing user`s inherent intentions. The existing SSVEP application system, such as speller, is able to classify the EEG pattern based on grid image patterns and their variations. However, our proposed SSVEP-based BCI system performs object pattern classification based on the matters with a variety of shapes in input images and has higher generality than existing system.
- Published
- 2012
24. Optimal EEG Feature Extraction using DWT for Classification of Imagination of Hands Movement
- Author
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Kwang-Eun Ko, Pharino Chum, Kwee-Bo Sim, and Seung-Min Park
- Subjects
Discrete wavelet transform ,medicine.diagnostic_test ,Computer science ,business.industry ,Feature extraction ,Feature selection ,Pattern recognition ,Electroencephalography ,Support vector machine ,Data set ,Feature (computer vision) ,medicine ,Artificial intelligence ,business ,Brain–computer interface - Abstract
An optimal feature selection and extraction procedure is an important task that significantly affects the success of brain activity analysis in brain-computer interface (BCI) research area. In this paper, a novel method for extracting the optimal feature from electroencephalogram (EEG) signal is proposed. At first, a student’s-t-statistic method is used to normalize and to minimize statistical error between EEG measurements. And, 2D time-frequency data set from the raw EEG signal was extracted using discrete wavelet transform (DWT) as a raw feature, standard deviations and mean of 2D time-frequency matrix were extracted as a optimal EEG feature vector along with other basis feature of sub-band signals. In the experiment, data set 1 of BCI competition IV are used and classification using SVM to prove strength of our new method.
- Published
- 2011
25. Occluded Object Motion Tracking Method based on Combination of 3D Reconstruction and Optical Flow Estimation
- Author
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Kwee-Bo Sim, Seung-Min Park, and Junheong Park
- Subjects
business.industry ,3D reconstruction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Optical flow ,Kalman filter ,Tracking (particle physics) ,Object (computer science) ,Geography ,Match moving ,Computer vision ,Artificial intelligence ,Noise (video) ,business ,Stereo camera ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
A mirror neuron is a neuron fires both when an animal acts and when the animal observes the same action performed by another. We propose a method of 3D reconstruction for occluded object motion tracking like Mirror Neuron System to fire in hidden condition. For modeling system that intention recognition through fire effect like Mirror Neuron System, we calculate depth information using stereo image from a stereo camera and reconstruct three dimension data. Movement direction of object is estimated by optical flow with three-dimensional image data created by three dimension reconstruction. For three dimension reconstruction that enables tracing occluded part, first, picture data was get by stereo camera. Result of optical flow is made be robust to noise by the kalman filter estimation algorithm. Image data is saved as history from reconstructed three dimension image through motion tracking of object. When whole or some part of object is disappeared form stereo camera by other objects, it is restored to bring image date form history of saved past image and track motion of object.
- Published
- 2011
26. Optimal Facial Emotion Feature Analysis Method based on ASM-LK Optical Flow
- Author
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Junheong Park, Kwee-Bo Sim, Seung-Min Park, and Kwang-Eun Ko
- Subjects
Pixel ,business.industry ,Feature extraction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Optical flow ,Pattern recognition ,Motion vector ,Facial Action Coding System ,Naive Bayes classifier ,Feature (computer vision) ,ComputerApplications_MISCELLANEOUS ,Active shape model ,Computer vision ,Artificial intelligence ,Psychology ,business - Abstract
In this paper, we propose an Active Shape Model (ASM) and Lucas-Kanade (LK) optical flow-based feature extraction and analysis method for analyzing the emotional features from facial images. Considering the facial emotion feature regions are described by Facial Action Coding System, we construct the feature-related shape models based on the combination of landmarks and extract the LK optical flow vectors at each landmarks based on the centre pixels of motion vector window. The facial emotion features are modelled by the combination of the optical flow vectors and the emotional states of facial image can be estimated by the probabilistic estimation technique, such as Bayesian classifier. Also, we extract the optimal emotional features that are considered the high correlation between feature points and emotional states by using common spatial pattern (CSP) analysis in order to improvise the operational efficiency and accuracy of emotional feature extraction process.
- Published
- 2011
27. Specified Object Tracking Problem in an Environment of Multiple Moving Objects
- Author
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Hyung-Bok Kim, Seung-Min Park, Kwee-Bo Sim, and Junheong Park
- Subjects
Logic ,Computer science ,business.industry ,Image processing ,Tracking system ,Tracking (particle physics) ,Object (computer science) ,Object detection ,Computer Science Applications ,Computational Theory and Mathematics ,Artificial Intelligence ,Video tracking ,Signal Processing ,Viola–Jones object detection framework ,Computer vision ,Artificial intelligence ,Particle filter ,business - Abstract
Video based object tracking normally deals with non-stationary image streams that change over time. Robust and real time moving object tracking is considered to be a problematic issue in computer vision. Multiple object tracking has many practical applications in scene analysis for automated surveillance. In this paper, we introduce a specified object tracking based particle filter used in an environment of multiple moving objects. A differential image region based tracking method for the detection of multiple moving objects is used. In order to ensure accurate object detection in an unconstrained environment, a background image update method is used. In addition, there exist problems in tracking a particular object through a video sequence, which cannot rely only on image processing techniques. For this, a probabilistic framework is used. Our proposed particle filter has been proved to be robust in dealing with nonlinear and non-Gaussian problems. The particle filter provides a robust object tracking framework under ambiguity conditions and greatly improves the estimation accuracy for complicated tracking problems.
- Published
- 2011
28. Study of Music Classification Optimized Environment and Atmosphere for Intelligent Musical Fountain System
- Author
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Young-Hwan Lee, Seung-Min Park, Kwang-Eun Ko, Junheong Park, and Kwee-Bo Sim
- Subjects
business.industry ,Pop music automation ,Musical ,Variance (accounting) ,computer.software_genre ,Atmosphere (architecture and spatial design) ,Support vector machine ,Musicology ,ComputingMethodologies_PATTERNRECOGNITION ,Categorization ,Music theory ,Artificial intelligence ,Psychology ,business ,computer ,Natural language processing - Abstract
Various research studies are underway to explore music classification by genre. Because sound professionals define the criterion of music to categorize differently each other, those classification is not easy to come up clear result. When a new genre is appeared, there is onerousness to renew the criterion of music to categorize. Therefore, music is classified by emotional adjectives, not genre. We classified music by light and shade in precedent study. In this paper, we propose the music classification system that is based on emotional adjectives to suitable search for atmosphere, and the classification criteria is three kinds; light and shade in precedent study, intense and placid, and grandeur and trivial. Variance Considered Machines that is an improved algorithm for Support Vector Machine was used as classification algorithm, and it represented 85% classification accuracy with the result that we tried to classify 525 songs.
- Published
- 2011
29. Development of Music Classification of Light and Shade using VCM and Beat Tracking
- Author
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Young-Hwan Lee, Kwee-Bo Sim, Kwang-Eun Ko, Seung-Min Park, and Junheong Park
- Subjects
Support vector machine ,Frequency analysis ,Music theory ,Categorization ,law ,Speech recognition ,Fast Fourier transform ,Musical ,Psychology ,Timbre ,Beat (music) ,law.invention - Abstract
Recently, a music genre classification has been studied. However, experts use different criteria to classify each of these classifications is difficult to derive accurate results. In addition, when the emergence of a new genre of music genre is a newly re-defined. Music as a genre rather than to separate search should be classified as emotional words. In this paper, the feelings of people on the basis of brightness and darkness tries to categorize music. The proposed classification system by applying VCM(Variance Considered Machines) is the contrast of the music. In this paper, we are using three kinds of musical characteristics. Based on surveys made throughout the learning, based on musical attributes(beat, timbre, note) was used to study in the VCM. VCM is classified by the trained compared with the results of the survey were analyzed. Note extraction using the MATLAB, sampled at regular intervals to share music via the FFT frequency analysis by the sector average is defined as representing the element extracted note by quantifying the height of the entire distribution was identified. Cumulative frequency distribution in the entire frequency rage, using the difference in Timbre and were quantified. VCM applied to these three characteristics with the experimental results by comparing the survey results to see the contrast of the music with a probability of 95.4% confirmed that the two separate.
- Published
- 2010
30. Development of EEG Signals Measurement and Analysis Method based on Timbre
- Author
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Kwang-Eun Ko, Kwee-Bo Sim, Seung-Min Park, and Young-Hwan Lee
- Subjects
Noise ,Tone (musical instrument) ,Cultural industry ,medicine.diagnostic_test ,Computer science ,Speech recognition ,medicine ,Added value ,Process (computing) ,Electroencephalography ,Timbre ,Field (computer science) - Abstract
Cultural Content Technology(CT, Culture Technology) for the development of cultural industry and the commercialization of technology, cultural contents, media, mount, pass the value chain process and increase the added value of cultural products that are good for all forms of intangible technology. In the field of Culture Technology, Music by analyzing the characteristics of the development of a variety of applications has been studied. Associated with EEG measures and the results of their research in response to musical stimuli are used to detect and study is getting attention. In this paper, the musical stimuli in EEG signals by amplifying the corresponding reaction to the averaging method, ERP (Event-Related Potentials) experiments based on the process of extracting sound methods for removing noise from the ICA algorithm to extract the tone and noise removal according to the results are applied to analyze the characteristics of EEG.
- Published
- 2010
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