Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform
Autore:
Acharyya, M; Kundu, MK;
Indirizzi:
Indian Stat Inst, Machine Intelligence Unit, Calcutta 700035, W Bengal, India Indian Stat Inst Calcutta W Bengal India 700035 a 700035, W Bengal, India
Titolo Testata:
SIGNAL PROCESSING
fascicolo: 7, volume: 81, anno: 2001,
pagine: 1337 - 1356
SICI:
0165-1684(200107)81:7<1337:AAATUT>2.0.ZU;2-B
Fonte:
ISI
Lingua:
ENG
Soggetto:
GABOR FILTERS; DISCRIMINATION; CLASSIFICATION; BASES;
Keywords:
M-band wavelets; texture segmentation; feature extraction; multiscale representation;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
36
Recensione:
Indirizzi per estratti:
Indirizzo: Kundu, MK Indian Stat Inst, Machine Intelligence Unit, 203 Barrackpore Trunk Rd, Calcutta 700035, W Bengal, India Indian Stat Inst 203 Barrackpore Trunk Rd Calcutta W Bengal India 700035
Citazione:
M. Acharyya e M.K. Kundu, "An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform", SIGNAL PROC, 81(7), 2001, pp. 1337-1356

Abstract

The M-band wavelet decomposition, which is a direct generalization of the standard 2-band wavelet decomposition is applied to the problem of an unsupervised segmentation of two texture images. Orthogonal and linear phase Ill-band wavelet transform is used to decompose the image into M x M channels. Various combinations of these bandpass sections are taken to obtain different scales and orientations in the frequency plane. Texture features are obtained by subjecting each bandpass section to a nonlinear transformation and computing the measure of energy in a window around each pixel of the filtered texture images. The window size in turn is adaptively selected depending on the frequency content of the images. Unsupervised texture segmentation is obtained by simple K-means clustering. Statistical tests are used to evaluate the average performance of features extracted from the decomposed subbands. (C) 2001 Elsevier Science B.V. All rights reserved.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 31/03/20 alle ore 03:02:37