Modelling and classification of shapes in two-dimensions using vector quantization

Author

Lee, Simon ; Lovell, Brian

Author_Institution

Dept. of Electr. & Comput. Eng., Queensland Univ., Qld., Australia

fYear

1994

fDate

19-22 Apr 1994

Abstract

This paper is an extension of work by He and Kundu (1991) in which the task of object recognition is performed on silhouettes or outlines. He and Kundu reduce a 2-dimensional image to a 1-dimensional segment sequence and use the autoregressive (AR) model for feature extraction and hidden Markov model (HMM) for classification. We show that due to the AR model´s inability to estimate abrupt changes (i.e. where the signal is not bandlimited), poor image modelling is obtained. By direct application of vector quantization (VQ) to the normalized data segments, the image features are retained better than with AR modelling. Furthermore, by replacing the HMM classification with VQ distortion, better recognition results are obtained with reduced training times as compared to the HMM algorithm

Keywords

autoregressive processes; feature extraction; hidden Markov models; image classification; image segmentation; image sequences; object recognition; vector quantisation; 1-dimensional segment sequence; 2-dimensional image; AR modelling; HMM; VQ distortion; autoregressive model; feature extraction; hidden Markov model; image features; image modelling; normalized data segments; object recognition; outlines; recognition results; shape classification; silhouettes; vector quantization; Computer vision; Feature extraction; Helium; Hidden Markov models; Image segmentation; Information processing; Object recognition; Shape; Signal processing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389428

Filename

389428