IEICE TRANSACTIONS on Information
Hand Gesture Recognition Using T-CombNET: A New Neural Network Model
Summary :
This paper presents a new neural network structure, called Temporal-CombNET (T-CombNET), dedicated to the time series analysis and classification. It has been developed from a large scale Neural Network structure, CombNET-II, which is designed to deal with a very large vocabulary, such as Japanese character recognition. Our specific modifications of the original CombNET-II model allow it to do temporal analysis, and to be used in large set of human movements recognition system. In T-CombNET structure one of most important parameter to be set is the space division criterion. In this paper we analyze some practical approaches and present an Interclass Distance Measurement based criterion. The T-CombNET performance is analyzed applying to in a practical problem, Japanese Kana finger spelling recognition. The obtained results show a superior recognition rate when compared to different neural network structures, such as Multi-Layer Perceptron, Learning Vector Quantization, Elman and Jordan Partially Recurrent Neural Networks, CombNET-II, k-NN, and the proposed T-CombNET structure.
- Publication
- IEICE TRANSACTIONS on Information Vol.E83-D No.11 pp.1986-1995
- Publication Date
- 2000/11/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Biocybernetics, Neurocomputing
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Marcus Vinicius LAMAR, Md. Shoaib BHUIYAN, Akira IWATA, "Hand Gesture Recognition Using T-CombNET: A New Neural Network Model" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 11, pp. 1986-1995, November 2000, doi: .
Abstract: This paper presents a new neural network structure, called Temporal-CombNET (T-CombNET), dedicated to the time series analysis and classification. It has been developed from a large scale Neural Network structure, CombNET-II, which is designed to deal with a very large vocabulary, such as Japanese character recognition. Our specific modifications of the original CombNET-II model allow it to do temporal analysis, and to be used in large set of human movements recognition system. In T-CombNET structure one of most important parameter to be set is the space division criterion. In this paper we analyze some practical approaches and present an Interclass Distance Measurement based criterion. The T-CombNET performance is analyzed applying to in a practical problem, Japanese Kana finger spelling recognition. The obtained results show a superior recognition rate when compared to different neural network structures, such as Multi-Layer Perceptron, Learning Vector Quantization, Elman and Jordan Partially Recurrent Neural Networks, CombNET-II, k-NN, and the proposed T-CombNET structure.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_11_1986/_p
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@ARTICLE{e83-d_11_1986,
author={Marcus Vinicius LAMAR, Md. Shoaib BHUIYAN, Akira IWATA, },
journal={IEICE TRANSACTIONS on Information},
title={Hand Gesture Recognition Using T-CombNET: A New Neural Network Model},
year={2000},
volume={E83-D},
number={11},
pages={1986-1995},
abstract={This paper presents a new neural network structure, called Temporal-CombNET (T-CombNET), dedicated to the time series analysis and classification. It has been developed from a large scale Neural Network structure, CombNET-II, which is designed to deal with a very large vocabulary, such as Japanese character recognition. Our specific modifications of the original CombNET-II model allow it to do temporal analysis, and to be used in large set of human movements recognition system. In T-CombNET structure one of most important parameter to be set is the space division criterion. In this paper we analyze some practical approaches and present an Interclass Distance Measurement based criterion. The T-CombNET performance is analyzed applying to in a practical problem, Japanese Kana finger spelling recognition. The obtained results show a superior recognition rate when compared to different neural network structures, such as Multi-Layer Perceptron, Learning Vector Quantization, Elman and Jordan Partially Recurrent Neural Networks, CombNET-II, k-NN, and the proposed T-CombNET structure.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - Hand Gesture Recognition Using T-CombNET: A New Neural Network Model
T2 - IEICE TRANSACTIONS on Information
SP - 1986
EP - 1995
AU - Marcus Vinicius LAMAR
AU - Md. Shoaib BHUIYAN
AU - Akira IWATA
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2000
AB - This paper presents a new neural network structure, called Temporal-CombNET (T-CombNET), dedicated to the time series analysis and classification. It has been developed from a large scale Neural Network structure, CombNET-II, which is designed to deal with a very large vocabulary, such as Japanese character recognition. Our specific modifications of the original CombNET-II model allow it to do temporal analysis, and to be used in large set of human movements recognition system. In T-CombNET structure one of most important parameter to be set is the space division criterion. In this paper we analyze some practical approaches and present an Interclass Distance Measurement based criterion. The T-CombNET performance is analyzed applying to in a practical problem, Japanese Kana finger spelling recognition. The obtained results show a superior recognition rate when compared to different neural network structures, such as Multi-Layer Perceptron, Learning Vector Quantization, Elman and Jordan Partially Recurrent Neural Networks, CombNET-II, k-NN, and the proposed T-CombNET structure.
ER -
English
