Clustering of noisy image data using an adaptive neuro-fuzzy system

S. Pemmaraju; Sunanda Mitra

Clustering of noisy image data using an adaptive neuro-fuzzy system

S. Pemmaraju, Sunanda Mitra

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noise real data.

Original language	English
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Publisher	Publ by Int Soc for Optical Engineering
Pages	327-334
Number of pages	8
ISBN (Print)	0819410276
State	Published - 1993
Event	Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods - Boston, MA, USA Duration: Nov 18 1992 → Nov 20 1992

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	1826
ISSN (Print)	0277-786X

Conference

Conference	Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods
City	Boston, MA, USA
Period	11/18/92 → 11/20/92

Cite this

@inproceedings{8f5cde70e02247349f3d999d8d0bedc4,

title = "Clustering of noisy image data using an adaptive neuro-fuzzy system",

abstract = "Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noise real data.",

author = "S. Pemmaraju and Sunanda Mitra",

year = "1993",

language = "English",

isbn = "0819410276",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

publisher = "Publ by Int Soc for Optical Engineering",

pages = "327--334",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "null ; Conference date: 18-11-1992 Through 20-11-1992",

}

Pemmaraju, S & Mitra, S 1993, Clustering of noisy image data using an adaptive neuro-fuzzy system. in Proceedings of SPIE - The International Society for Optical Engineering. Proceedings of SPIE - The International Society for Optical Engineering, vol. 1826, Publ by Int Soc for Optical Engineering, pp. 327-334, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods, Boston, MA, USA, 11/18/92.

Clustering of noisy image data using an adaptive neuro-fuzzy system. / Pemmaraju, S.; Mitra, Sunanda.

Proceedings of SPIE - The International Society for Optical Engineering. Publ by Int Soc for Optical Engineering, 1993. p. 327-334 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 1826).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Clustering of noisy image data using an adaptive neuro-fuzzy system

AU - Pemmaraju, S.

AU - Mitra, Sunanda

PY - 1993

Y1 - 1993

N2 - Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noise real data.

AB - Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noise real data.

UR - http://www.scopus.com/inward/record.url?scp=0027205993&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0027205993

SN - 0819410276

T3 - Proceedings of SPIE - The International Society for Optical Engineering

SP - 327

EP - 334

BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Publ by Int Soc for Optical Engineering

Y2 - 18 November 1992 through 20 November 1992

ER -

Clustering of noisy image data using an adaptive neuro-fuzzy system

Abstract

Publication series

Conference

Other files and links

Fingerprint

Cite this