Efficient artificial targets estimation via BING

Lin Wang; Can Ding

doi:10.1117/12.2264977

8 March 2017 Efficient artificial targets estimation via BING

Lin Wang, Can Ding

Proceedings Volume 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016; 102551X (2017) https://doi.org/10.1117/12.2264977
Event: Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 2016, Jinhua, Suzhou, Chengdu, Xi'an, Wuxi, China

Abstract

Objects with well-defined closed boundary can be discriminated by looking at the norm of gradients. With suitable resizing of their corresponding image windows into a small fixed size(8×8), and further binarized the normed gradients (BING) of images can describe the generic objectness measure. Inspired by the “BING” and considered the character that the artifical targets have many obvious corner points, in this paper we propose to predict candidate windows based on corner points instead of non-maximal suppression the BING used. We can generate a small set of high quality target windows and yield 96.2% object detection rate (DR) like the BING dose but need only half time. This is because of the number of corner point is much less than the number of non-maximal suppression point. Our method generate a small set of high quality target window.

Citation Download Citation

Lin Wang and Can Ding "Efficient artificial targets estimation via BING", Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102551X (8 March 2017); https://doi.org/10.1117/12.2264977

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