BING: Binarized normed gradients for objectness estimation at 300fps
Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a s...
Saved in:
Main Authors: | CHENG, Ming-Ming, LIU, Yun, LIN, Wen-yan, ZHANG, Ziming, ROSIN, Paul L. TORR |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2014
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/4803 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
BING: Binarized normed gradients for objectness estimation at 300fps
by: CHENG, Ming-Ming, et al.
Published: (2019) -
BING: Binarized normed gradients for objectness estimation at 300fps
by: CHENG, Ming-Ming, et al.
Published: (2019) -
Stereo object proposals
by: HUANG, Shao, et al.
Published: (2017) -
Adobe Boxes: Locating Object Proposals using Object Adobes
by: Xiao, Yang, et al.
Published: (2017) -
Interactive hierarchical object proposals
by: CHEN, Mingliang, et al.
Published: (2019)