Improvement of object classification in image sequences

D. Ernst; H. Gross; D. Stricker; Ulrich Thoennessen

doi:10.1117/12.277128

23 June 1997 Improvement of object classification in image sequences

D. Ernst, H. Gross, D. Stricker, Ulrich Thoennessen

Proceedings Volume 3069, Automatic Target Recognition VII; (1997) https://doi.org/10.1117/12.277128
Event: AeroSense '97, 1997, Orlando, FL, United States

Abstract

It is known that the performance of recognition by humans is improved by time integration. We have investigated this phenomenon in image sequences. The object hypotheses are detected in the image sequence utilizing object regions and motion in analogy to human perception. A multiple thresholding segmentation and a change detection process by wavelet transformation are used for detection. The detected segments are tracked over time. Our classification approach assumes, that the objects are recognized as a connected entity. Therefore a structural object description derived from the image sequence was developed. This description contains the geometric relations and shape features of the individual object parts as well as the motion behavior. Normalized difference measures of the structural descriptions have been derived for the classification. The differences are determined and combined by a fuzzy approach. The results have shown, that the classification can be improved and stabilized by the object description derived from the image sequence.

Citation Download Citation

D. Ernst, H. Gross, D. Stricker, and Ulrich Thoennessen "Improvement of object classification in image sequences", Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); https://doi.org/10.1117/12.277128

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