Novel metrics for object discrimination in overhead real and synthetic color images

Prakash Duraisamy; Amr H. Yousef; Khan M. Iftekharuddin

doi:10.1117/12.2052887

13 June 2014 Novel metrics for object discrimination in overhead real and synthetic color images

Prakash Duraisamy, Amr H. Yousef, Khan M. Iftekharuddin

Proceedings Volume 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX; 908810 (2014) https://doi.org/10.1117/12.2052887
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States

Abstract

The evaluation and discrimination of similar objects in real versus synthetically generated aerial color images is needed for security and surveillance purposes among other applications. Identification of appropriate discrimination metrics between real versus synthetic images may also help in more robust generation of these synthetic images. In this paper, we investigate the effectiveness of three different metrics based on Gaussian Blur, Differential Operators and singular value decomposition (SVD) to differentiate between a pair of same objects contained in real and synthetic overhead aerial color images. We use nine pairs of images in our tests. The real images were obtained in the visible aerial color image domain. The proposed metrics are used to discriminate between pairs of real and synthetic objects such as cooling units, industrial buildings, houses, conveyors, stacks, piles, railroads and ponds in these real and synthetically generated images respectively. The proposed method successfully discriminates between the real and synthetic objects in aerial color images without any apriori knowledge or extra information such as optical flow. We ranked these metrics according to their effectiveness to discriminate between synthetic and real objects in overhead images.

Citation Download Citation

Prakash Duraisamy, Amr H. Yousef, and Khan M. Iftekharuddin "Novel metrics for object discrimination in overhead real and synthetic color images", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 908810 (13 June 2014); https://doi.org/10.1117/12.2052887

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image compression

Buildings

Computer vision technology

Machine vision

Wavelets

Shape analysis

Image analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years