Paper
27 February 1996 Multiresolution sequential image change detection with wavelets
Yawgeng A. Chau, Jar-Chi Shee
Author Affiliations +
Proceedings Volume 2727, Visual Communications and Image Processing '96; (1996) https://doi.org/10.1117/12.233265
Event: Visual Communications and Image Processing '96, 1996, Orlando, FL, United States
Abstract
Multiresolution image change detection based on the wavelet expansion is addressed. The multiresolution change detection is modeled as a sequential hypothesis test problem. A modified multistage truncated sequential probability ratio test (TSPRT) is developed for the change detection problem. With the multistage TSPRT, the devised scheme for change detection employs multiresolution images with increasing sample sizes. The maximum likelihood (ML) estimation is used to obtain the mean, variance, and the relevant correlation coefficients of the image signals for the test. To determine the thresholds of the TSPRT, a suboptimal technique in accordance with the constant false alarm and missing probabilities for the hypothesis test problem is considered. To illustrate the performance of the developed multiresolution change detection scheme, experimental results are presented. From the experimental results, it is asserted that the developed multiresolution change detection algorithm can accurately disclose the changing areas in a consecutive image sequence.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yawgeng A. Chau and Jar-Chi Shee "Multiresolution sequential image change detection with wavelets", Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); https://doi.org/10.1117/12.233265
Lens.org Logo
CITATIONS
Cited by 1 scholarly publication.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image resolution

Wavelets

Image processing

Algorithm development

Detection and tracking algorithms

Detector development

Image analysis

Back to Top