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Fractal Dimension Based Color Texture Analysis for Mangosteen Ripeness Grading
(2019-12-01)
Phothisonothai, Montri
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Tantisatirapong, Suchada
Mangosteen is one of the fruits that has an enormous export potential in Thailand. However, it contains numerous undesirable external as well as internal conditions which result in the shipment rejection and decrease the reliability of the export. Therefore, in this paper for the first time, we propose the method for mangosteen ripeness grading using the spatiotemporal properties of external rind texture analysis on the basis of fractal dimension (FD) approach for three classes: i.e., Glossy (GS), Medium Rough (MR) and Extreme Rough (ER). In this study, for the first time, the five stages of ripening have been extracted using FD based feature with Gaussian Mixture Model (GMM) classifier. The obtained results showed that the proposed method can perform the better results compared with the classical texture feature, i.e., average accuracy rates of 88.0%, 82.0%, and 90.0% for GS, MR, and ER classes, respectively.
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Eye-Tracking Based Visualizations and Metrics Analysis for Individual Eye Movement Patterns
(2019-07-01)
Bhattarai, Rasa
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Phothisonothai, Montri
Uniqueness in the analysis pattern of objects by individual humans has a profound impact on the study of their visual learning and behavior. Eye movement patterns have been effectively emerging as a biometric based key for security systems, product recognition patterns, user identifications, as well as medical research purposes. The modern eye tracking systems are non-invasive and financially affordable. Therefore, in this paper, we proposed eye-Tracking based visualizations and metrics analysis for individual eye movement patterns collected during any kinds of activities depending on the scope of the our experimental paradigms. Individuals can be aware of their own performances during certain task and improve upon their weak areas. The objective of the paper is to utilize the important visual metrics obtained from fixation, saccades and face recognition and use them to analyze for individual categorization. The obtained results shown that the specific features and patterns can be extracted the viewing aspect of individual subjects using naive Bayes classifier. We were successfully able to predict the individual eye movements with an accuracy of 90.22%.
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Integrated Human-Machine Interaction System: ERP-SSVEP and Eye Tracking Based Technologies
(2019-04-10)
Phothisonothai, Montri
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Tantisatirapong, Suchada
Human-Machine Interaction (HMI) requires a multidisciplinary research study mainly focused on interaction modalities between humans and machines. In this paper, we introduced an integrated HMI system using electrical brainwave signal (or called Electroencephalography: EEG) and eye tracking of pupil movement (or called ET), whose are used as an alternative channel to communicate with others for people with disabilities. In this experiment, the target and non-Target visual stimuli of EEG-based HMI system on the basis of event-related potential (ERP) and steady state visually evoked potential (SSVEP) signals have been performed. For the ET based framework, we proposed the user-friendly virtual keyboard typing for Thai language, i.e., free-form and automatic typing modes. The results showed that the integrated HMI using ERP-SSVEP yielded an average accuracy of 97.4% and reaction time approximately was 724.2 millisecond for control commands. The automatic typing mode performed an average accuracy of 97%, with an average printing time of 6.17 seconds per word for ET based virtual Thai keyboard.
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Design of User-Friendly Virtual Thai Keyboard Based on Eye-Tracking Controlled System
(2018-12-24)
Tantisatirapong, Suchada
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Phothisonothai, Montri
Eye trackers are noninvasive and portable devices allowing the application of various assistive technologies. We aim to improve the quality of life of Thai people with disabilities whose eyes are the only mean of communication. Therefore, this paper presents a gaze-controlled virtual Thai keyboard. We compare two typing modes. automatic typing and free-form typing. A survey of often used words for Thai patients is conducted for word prediction system. The system is evaluated with 10 adult participants. The results show that the automatic typing provides faster responses and obtains higher user satisfaction than free-form typing.
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Comparison of feature extraction for accent dependent Thai speech recognition system
(2018-09-13)
Tantisatirapong, Suchada
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Prasoproek, Chalisa
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Phothisonothai, Montri
This paper aims to compare the feature extraction methods for accent dependent Thai speech from three regions including central, southern and northeastern regions. We investigate four frequency analysis methods: i.e., Energy Spectral Density (ESD), Power Spectral Density (PSD), Mel-Frequency Cepstral Coefficients (MFCC) and Spectrogram (SPT). Radial basis function kernel based on support vector machine is used as a classifier with 5-fold cross validation. The isolated speech data sets are recorded from 30 male and 30 female participants speaking the 10 Thai digits from 0 to 9. The MFCC-based feature gives better accuracy than ESD, PSD and SPT respectively. For within the same region, the MFCC-based feature provides average accuracy of 94.9% and 99.1% for male and female voices respectively. For the three regions, the MFCC-based feature provides average accuracy of 89.34% and 93.81% for male and female voices, respectively.
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License Plate Detection of Myanmar Vehicle Images from Dissimilar Angle Conditions
(2018-08-21)
Khin, Ohnmar
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Phothisonothai, Montri
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Choomchuay, Somsak
It has been studied that there is no established LPR (License Plate Recognition) to detect and identify the license plates from dissimilar angles. The aim of the paper is to detect the dissimilar angles of the license plate with the non-fixed LPR system. Therefore, the horizontal and vertical dilation, skew angle detection and automatic bounding box have been proposed to detect the license plate. The proposed method has been applied to the four different types of Myanmar license plates, e.g., private cars, taxi, tour buses and religion cars. One car each is taken into four different types of angles on the dissimilar conditions. Experimental result indicated that this method can detect the disparate types of license plates with a high accuracy, i.e., the proposed approach achieved a favorable outcome rate of 97% at 100 license plates.
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Detection and Recognition of Myanmar Characters from the Dissimilar Images
(2018-08-06)
Khin, Ohnmar
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Phothisonothai, Montri
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Choomchuay, Somsak
The impartial of this paper is to spread the License Plate Recognition for the traffic light development of Myanmar. In this paper, image processing toolbox, controlling and take boxes were proposed to extract the numbers and characters from the input images. Our method consists of three steps. In the first step, the input license plate image is increased by doing some the action or noise modification. Then the features of the characters are extracted to overcome the sameness problems of the Myanmar characters. Finally, neural network is used for the character recognition. The experiments were carried out on the dissimilar Myanmar vehicle images and results showed the efficiency of 93% which was substantially applied for the license plate detection, letters and numbers extraction.
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Classification of in Vitro Blood Stages of Plasmodium Falciparum Based on Fuzzy Inference System
(2018-08-06)
Tantisatirapong, Suchada
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Phothisonothai, Montri
This paper proposes the automated texture based classification of Malaria parasites in Giemsa-stained thin blood film images based on fuzzy inference system (FIS). The proposed expert and knowledge based framework includes the segmentation, feature extraction and classification of erythrocytes. First-order statistical analysis includes mean, standard deviation, skewness and kurtosis have been proposed as input parameters of FIS. The effectiveness of classifier is compared to find appropriate parame- ters for classification of normal cells and infected cells, both ring and trophozoite stages. The proposed method can provide 96.28% accuracy rate for binary classification of normal and infected cells. The results also yield 97.55% accuracy for ring stage classification, and 98.54% accuracy for trophozoite stage classification.
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Surface roughness classification of mangosteen with gray level co-occurrence matrix based texture analysis
(2018-07-02)
Acharya, Anjali
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Phothisonothai, Montri
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Tantisatirapong, Suchada
Mangosteen is one of the fruits that has an enormous export potential in Thailand. It is well-known as the queen of fruit. Mangosteen export generates large revenue; however, fruit is not defect free it contains many undesirable external as well as internal condition which results in the shipment rejection and decrease the reliability of the export. Therefore, this research investigates an approach for texture image analysis based surface roughness detection and classification into 3 classes: i.e., Glossy Surface, Mid Rough Surface and Extreme Rough Surface. In this study, for the first time, we propose the textural features extracted using Gray-Level Co-occurrence Matrix (GLCM) for surface roughness classification of mangosteen.
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License Plate Detection of Myanmar Vehicle Images Captured from the Dissimilar Environmental Conditions
(2018-06-21)
Khin, Ohnmar
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Phothisonothai, Montri
;
Choomchuay, Somsak
Due to the difficulty in defecting the images, in this paper, we proposed the method that can detect region of interest of the license plate for Myanmar vehicles captured from the dissimilar environmental conditions, e.g., different type of license plates, angle of image capturing, and real environmental conditions. In this paper, the horizontal and vertical dilation, skew angle detec-tion and automatic bounding box were proposed to detect the license number from input images. In our experiment, the ob-tained results showed that an average detection accuracy of 99% which was substantially applied for the license plate detection from the dissimilar environmental conditions.

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