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80 results on '"Piamsa-nga, Punpiti"'

1. Design a Curriculum with User-Experience Analysis: Case Study Computing Science Curriculum

Author

Piamsa-nga, Punpiti, Poovarawan, Yuen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Cheung, Simon K. S., editor, Li, Richard, editor, Phusavat, Kongkiti, editor, Paoprasert, Naraphorn, editor, and Kwok, Lam‑For, editor

Published
2020
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2. A Dataset for Evaluating Pedestrian Event Classification Algorithms

Author

Preechasuk, Jitdumrong, Piamsa-nga, Punpiti, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Abawajy, Jemal H., editor, Othman, Mohamed, editor, Ghazali, Rozaida, editor, Deris, Mustafa Mat, editor, Mahdin, Hairulnizam, editor, and Herawan, Tutut, editor

Published
2019
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10. Intelligent Data Recognition of DNA Sequences Using Statistical Models

Author

Keinduangjun, Jitimon, Piamsa-nga, Punpiti, Poovorawan, Yong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Pal, Sankar K., editor, Bandyopadhyay, Sanghamitra, editor, and Biswas, Sambhunath, editor

Published
2005
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11. Signature Recognition Methods for Identifying Influenza Sequences

Author

Keinduangjun, Jitimon, Piamsa-nga, Punpiti, Poovorawan, Yong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Miksch, Silvia, editor, Hunter, Jim, editor, and Keravnou, Elpida T., editor

Published
2005
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12. DNA Sequence Identification by Statistics-Based Models

Author

Keinduangjun, Jitimon, Piamsa-nga, Punpiti, Poovorawan, Yong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Lipo, editor, and Jin, Yaochu, editor

Published
2005
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36. Toward High-Level Visual Content Interpretation and Annotation for Sport Events

Author

Piamsa-nga Punpiti, Stefan Poslad, Sutasinee Chimlek, and Kraisak Kesorn

Subjects
Event (computing), business.industry, Computer science, Interpretation (philosophy), Knowledge engineering, Cognitive neuroscience of visual object recognition, computer.software_genre, Semantics, Visualization, Spatial relation, Artificial intelligence, business, computer, Natural language, Natural language processing
Abstract

This paper presents a novel framework for visual content interpretation and aims to generate meaningful descriptions for visual content based on the aggregate information of the detected primitive objects, spatial relations, and specific relevant prior knowledge to aid visual content interpretation. The main contributions of this paper include: (1) a novel approach to generate semantic descriptions for visual data at natural language level whereas the state of the art frameworks perform just simple object labeling which is not informative; (2) an integration of the detected primitive objects, spatial relations and contextual knowledge to detect the action scene (competition phase) in sport events e.g. flying action in a pole vault event. The experimental results show that the presented approach can discover semantically meaningful visual content descriptions and recognize sport event and action in the visual data efficiently.

Published
2010

45. The Selection of Useful Visual Words in Class-Imbalanced Image Classification.

Author

Chimlek, Sutasinee, Pramokchon, Part, and Piamsa-nga, Punpiti

Subjects
IMAGE analysis, COMPUTER engineering, CLASSIFICATION algorithms, PERFORMANCE evaluation, DISCRIMINANT analysis, QUANTITATIVE research
Abstract

The bag of visual words (BOVW) has recently been used for image classification in large datasets. A major problem of image classification using BOVW is high dimensionality, with most features usually being irrelevant and different BOVW for multi-view images in each class. Therefore, the selection of significant visual words for multi-view images in each class is an essential method to reduce the size of BOVW while retaining the high performance of image classification. Many feature scores for ranking produce low classification performance for class imbalanced distributions and multi-views in each class. We propose a feature score based on the statistical t-test technique, which is a statistical evaluation of the difference between two sample means, to assess the discriminating power of each individual feature. The multi-class image classification performance of the proposed feature score is compared with four modern feature scores, such as Document Frequency (DF), Mutual information (MI), Pointwise Mutual information (PMI) and Chi-square statistics (CHI). The results show that the average F1-measure performance on the Paris dataset and the SUN397 dataset using the proposed feature score are 92% and 94%, respectively, while all other feature scores do not exceed 80%. [ABSTRACT FROM AUTHOR]

Published
2016
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46. Event Detection on Motion Activities Using a Dynamic Grid.

Author

Preechasuk, Jitdumrong and Piamsa-nga, Punpiti

Subjects
DYNAMICAL systems, FEATURE extraction, PROBLEM solving, DATA security, INFORMATION & communication technologies
Abstract

Event detection based on using features from a static grid can give poor results from the viewpoint of two main aspects: the position of the camera and the position of the event that is occurring in the scene. The former causes problems when training and test events are at different distances from the camera to the actual position of the event. The latter can be a source of problems when training events take place in any position in the scene, and the test events take place in a position different from the training events. Both issues degrade the accuracy of the static grid method. Therefore, this work proposes a method called a dynamic grid for event detection, which can tackle both aspects of the problem. In our experiment, we used the dynamic grid method to detect four types of event patterns: implosion, explosion, two-way, and one-way using a Multimedia Analysis and Discovery (MAD) pedestrian dataset. The experimental results show that the proposed method can detect the four types of event patterns with high accuracy. Additionally, the performance of the proposed method is better than the static grid method and the proposed method achieves higher accuracy than the previous method regarding the aforementioned aspects. [ABSTRACT FROM AUTHOR]

Published
2015
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