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A Comparative Study of Video Segmentation Techniques for Graduate Detection in Thai Graduation Ceremonies
(2025-01-01)
Chungmarisakul, Chanasorn
;
Chawuthai, Rathachai
Graduation ceremonies are significant occasions usually documented on lengthy, difficult-to-navigate videos. To make more satisfaction of the video needs to remove other participants and restore clear areas into short segments focused on particular graduates. By using YOLOv8 for efficient participant segmentation and the Segment Anything Model (SAM2) for accurate tracking, this study expands on earlier research by preparing videos for inpainting. The results establish the foundation for a complete system that combines inpainting, segmentation, and detection to produce polished, customized graduation video clips.
1
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DIP-CBML: A New Classification of Thai Dragon Fruit Species from Images
(2023-01-01)
Yusamran, Naruwan
;
Hiransakolwong, Nualsawat
The attractiveness of dragon fruit is that it has a strange exterior, beautiful colors, and high nutritional value. In Thailand, there is both import and export of dragon fruit. Each package for export must contain only one species of dragon fruit. From the survey, there are seven species of dragon fruit cultivated in Thailand and only some farmers can identify them on his/her farm. Therefore, this research focuses on the classification of Thai dragon fruit from laboratory images and outdoor images; which is different from the previous works which studied only laboratory images. This method was named DIP-CBML that stands for digital image processing with content-based and machine learning. The method consists of image type identification, pre-processing, red and yellow classification, image background removal, and six classes of red species classification. From the results, DIP-CBML can work with both datasets. It gave 100%, 100% and 95.53% accuracy for the image type identification, red and yellow classification, and the classification of six red species respectively. Hopefully, this research will lead to the innovation for the pre-harvest classification of Thai dragon fruit cultivars, applied to industrial applications, and robot harvesting. In the future, may add value to the yield of Thai dragon fruit cultivation.
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The Effect of Preprocessing on U-Net for Bladder Segmentation in CT Images
(2023-01-01)
Apivanichkul, Kamonchat
;
Phasukkit, Pattarapong
;
Dankulchai, Pittaya
This research proposes preprocessing techniques for computed tomography (CT) slices with the aim of improving the performance of a deep-learning segmentation model (U-Net model). The preprocessing techniques used in this study include window leveling, histogram equalization, Gaussian blurring, and cropping (incorporating mathematical morphology and histogram projection). The U-Net model is applied to three groups of input data sets (B-I to B-III) for training, validation, and testing. The segmentation performance is evaluated using metrics such as Dice similarity coefficient (DSC) and intersection over union (IoU). The trained U-Net model achieves the highest DSC (95.15%) and IoU (91.09%) under B-III dataset, utilizing cropped and enhanced CT input datasets. Window leveling, histogram equalization, Gaussian blurring, and cropping are identified as the optimal preprocessing techniques for bladder segmentation. This novel research applies diverse image processing techniques in medical image preprocessing to enhance the deep-learning U-Net model's segmentation performance.
2
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Classification of in Vitro Blood Stages of Plasmodium Falciparum Based on Fuzzy Inference System
(2018-08-06)
Tantisatirapong, Suchada
;
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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Automated detection of plasmodium falciparum from Giemsa-stained thin blood films
(2016-03-23)
Preedanan, Wongsakorn
;
Phothisonothai, Montri
;
Senavongse, Wongwit
;
Tantisatirapong, Suchada
This paper investigates automated detection of malaria parasites in images of Giemsa-stained thin blood films. We aim to determine parasitemia based on automatic segmentation, feature extraction and classification methods. Segmentation relies on adaptive thresholding and watershed methods. Statistical features are then computed for each cell and classified using SVM binary classifier. Accuracy of classification is validated based on the leave-one-out cross-validation technique. This processing pipeline is applied on total 15 images of Giemsa-stained thin blood films and yields 92.71% sensitivity, 97.35% specificity and 97.17% accuracy.

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