2D multirate Bayesian framework for multiscale feature detection

Hazem M. Hajj; Truong Q. Nguyen; Roland T. Chin

doi:10.1117/12.255244

23 October 1996 2D multirate Bayesian framework for multiscale feature detection

Hazem M. Hajj, Truong Q. Nguyen, Roland T. Chin

Proceedings Volume 2825, Wavelet Applications in Signal and Image Processing IV; (1996) https://doi.org/10.1117/12.255244
Event: SPIE's 1996 International Symposium on Optical Science, Engineering, and Instrumentation, 1996, Denver, CO, United States

Abstract

This paper presents a novel methodology for designing a 2D multiscale feature detector, which consists of a filter bank and a maximum a posteriori (MAP) classifier. The framework assumes the availability of a one-scale filter with a particular indicator response to the desired feature. This filter is used to generate a multiscale set of discrete filters by sampling on a rectangular lattice to preserve the indicator responses at all the scales. The net step in the framework consists of designing the filter bank to approximate the generated filters. A 2D MAP detector is then designed to minimize detection errors. With the assumption of known feature, the resulting detector depends only on the filter bank, and not on the noise. Relaxing this assumption yields a detection algorithm that is noise dependent and computationally intensive. The framework is applied to edge detection in a noisy environment, and the results indicate efficient detection. Moreover the 2D MAP can find feature end-points by direct processing of the image. This is unlike conventional methods where edges need to be first detected and then processed to locate the corners. Examples are presented to demonstrate the algorithm.

Citation Download Citation

Hazem M. Hajj, Truong Q. Nguyen, and Roland T. Chin "2D multirate Bayesian framework for multiscale feature detection", Proc. SPIE 2825, Wavelet Applications in Signal and Image Processing IV, (23 October 1996); https://doi.org/10.1117/12.255244

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available