Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system

Shi An Chen, Jen Feng Chung, Sheng-Fu Liang, Chin Teng Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper proposes a novel CNN-based biological visual processing for hybrid-order texture boundary detection. The texture boundary detection is based on the first- and second-order features to model pre-attentive stage of human visual system. This system is implemented by using a parallel computing neural network, called cellular neural networks (CNN). This CNN design adopts the multi-layer architecture involving a 5×5 large neighborhood and is extended to be the 16×16 array size for image processing. The proposed circuit models have been verified and the proposed method can successfully detect the texture boundary in an image.

Original languageEnglish
Title of host publication2004 IEEE International Workshop on Biomedical Circuits and Systems
Publication statusPublished - 2004 Dec 1
Event2004 IEEE International Workshop on Biomedical Circuits and Systems - Singapore, Singapore
Duration: 2004 Dec 12004 Dec 3

Publication series

Name2004 IEEE International Workshop on Biomedical Circuits and Systems

Other

Other2004 IEEE International Workshop on Biomedical Circuits and Systems
CountrySingapore
CitySingapore
Period04-12-0104-12-03

Fingerprint

Cellular neural networks
Textures
Networks (circuits)
Parallel processing systems
Image processing
Neural networks
Processing

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Chen, S. A., Chung, J. F., Liang, S-F., & Lin, C. T. (2004). Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system. In 2004 IEEE International Workshop on Biomedical Circuits and Systems (2004 IEEE International Workshop on Biomedical Circuits and Systems).
Chen, Shi An ; Chung, Jen Feng ; Liang, Sheng-Fu ; Lin, Chin Teng. / Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system. 2004 IEEE International Workshop on Biomedical Circuits and Systems. 2004. (2004 IEEE International Workshop on Biomedical Circuits and Systems).
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title = "Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system",
abstract = "This paper proposes a novel CNN-based biological visual processing for hybrid-order texture boundary detection. The texture boundary detection is based on the first- and second-order features to model pre-attentive stage of human visual system. This system is implemented by using a parallel computing neural network, called cellular neural networks (CNN). This CNN design adopts the multi-layer architecture involving a 5×5 large neighborhood and is extended to be the 16×16 array size for image processing. The proposed circuit models have been verified and the proposed method can successfully detect the texture boundary in an image.",
author = "Chen, {Shi An} and Chung, {Jen Feng} and Sheng-Fu Liang and Lin, {Chin Teng}",
year = "2004",
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Chen, SA, Chung, JF, Liang, S-F & Lin, CT 2004, Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system. in 2004 IEEE International Workshop on Biomedical Circuits and Systems. 2004 IEEE International Workshop on Biomedical Circuits and Systems, 2004 IEEE International Workshop on Biomedical Circuits and Systems, Singapore, Singapore, 04-12-01.

Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system. / Chen, Shi An; Chung, Jen Feng; Liang, Sheng-Fu; Lin, Chin Teng.

2004 IEEE International Workshop on Biomedical Circuits and Systems. 2004. (2004 IEEE International Workshop on Biomedical Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chen SA, Chung JF, Liang S-F, Lin CT. Cellular Neural Network (CNN) circuit design for modeling of early-stage human visual system. In 2004 IEEE International Workshop on Biomedical Circuits and Systems. 2004. (2004 IEEE International Workshop on Biomedical Circuits and Systems).