NDLI: Leveraging stereopsis for saliency analysis

Content Provider	IEEE Xplore Digital Library
Author	Yuzhen Niu Yujie Geng Xueqing Li Feng Liu
Copyright Year	2012
Description	Author affiliation: Department of Computer Science, Portland State University, Portland, OR, 97207 USA (Yuzhen Niu; Yujie Geng; Feng Liu) \|\| School of Computer Science and Technology, Shandong University, Jinan, Shandong, 250101 China (Xueqing Li)
Abstract	Stereopsis provides an additional depth cue and plays an important role in the human vision system. This paper explores stereopsis for saliency analysis and presents two approaches to stereo saliency detection from stereoscopic images. The first approach computes stereo saliency based on the global disparity contrast in the input image. The second approach leverages domain knowledge in stereoscopic photography. A good stereoscopic image takes care of its disparity distribution to avoid 3D fatigue. Particularly, salient content tends to be positioned in the stereoscopic comfort zone to alleviate the vergence-accommodation conflict. Accordingly, our method computes stereo saliency of an image region based on the distance between its perceived location and the comfort zone. Moreover, we consider objects popping out from the screen salient as these objects tend to catch a viewer's attention. We build a stereo saliency analysis benchmark dataset that contains 1000 stereoscopic images with salient object masks. Our experiments on this dataset show that stereo saliency provides a useful complement to existing visual saliency analysis and our method can successfully detect salient content from images that are difficult for monocular saliency analysis methods.
Starting Page	454
Ending Page	461
File Size	1078021
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247708
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stereo image processing Visualization Cameras Photography Benchmark testing Image color analysis
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Hierarchical Saliency Detection

Stereo analysis using individual evolution strategy

Learning to rank in person re-identification with metric ensembles

Large-Scale Data for Multiple-View Stereopsis

Saliency Optimization from Robust Background Detection

Time-Mapping Using Space-Time Saliency

Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

Colour correction for panoramic imaging

Recovering Stereo Pairs from Anaglyphs

Leveraging stereopsis for saliency analysis

Similar Documents

Hierarchical Saliency Detection

Stereo analysis using individual evolution strategy

Learning to rank in person re-identification with metric ensembles

Large-Scale Data for Multiple-View Stereopsis

Saliency Optimization from Robust Background Detection

Time-Mapping Using Space-Time Saliency

Large-Scale Optimization of Hierarchical Features for Saliency Prediction in Natural Images

Colour correction for panoramic imaging

Recovering Stereo Pairs from Anaglyphs

Leveraging stereopsis for saliency analysis