An improved real-time object proposals generation method based on local binary pattern. (11th August 2017)
- Record Type:
- Journal Article
- Title:
- An improved real-time object proposals generation method based on local binary pattern. (11th August 2017)
- Main Title:
- An improved real-time object proposals generation method based on local binary pattern
- Authors:
- Jiang, Yanting
Yan, Jia
Fan, Ci'en
Shi, Wenxuan
Deng, Dexiang - Abstract:
- Generating a group of category-independent proposals of objects in an image within a very short time is an effective approach to accelerate traditional sliding window search, which has been widely used in preprocessing step of object recognition. In this article, we propose a novel object proposals generation method to produce an order set of candidate windows covering most of object instances. With combination of gradient and local binary pattern, our approach achieves better performance than BING in finding occluded objects and objects in dim lighting conditions. In experiments on the challenging PASCAL VOC 2007 data set, we show that our approach is significantly more accurate than BING. In particular, using 2000 proposals, we achieve 97.6% object detection rate and 69.3% mean average best overlap. Moreover, our proposed method is very efficient and takes only about 0.006 s per image on a laptop central processing unit. The detection speed and high accuracy of proposed method mean that it can be applied to recognizing specific objects in robot visions.
- Is Part Of:
- International journal of advanced robotic systems. Volume 14:Number 4(2017:Jul./Aug.)
- Journal:
- International journal of advanced robotic systems
- Issue:
- Volume 14:Number 4(2017:Jul./Aug.)
- Issue Display:
- Volume 14, Issue 4 (2017)
- Year:
- 2017
- Volume:
- 14
- Issue:
- 4
- Issue Sort Value:
- 2017-0014-0004-0000
- Page Start:
- Page End:
- Publication Date:
- 2017-08-11
- Subjects:
- Objectness -- proposal generation -- local binary pattern -- computer vision -- object detection
Robotics -- Periodicals
Robotics
Periodicals
629.892 - Journal URLs:
- http://arx.sagepub.com/ ↗
http://search.epnet.com/direct.asp?db=bch&jid=13CR&scope=site ↗
http://www.intechweb.org/journal.php?id=3 ↗
http://www.uk.sagepub.com/home.nav ↗ - DOI:
- 10.1177/1729881417724679 ↗
- Languages:
- English
- ISSNs:
- 1729-8806
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 8204.xml