NDLI: A cue integration method for anaglyph image partition

Content Provider	Springer Nature Link
Author	Wu, Qin Guo, Guodong Liang, Jiuzhen
Copyright Year	2014
Abstract	Image content analysis is important for automated image organization, labeling, and search. Partitioning an image into meaningful regions is one of the fundamental problems in image analysis. Anaglyph images and videos are more and more popular, such as in Flickr and YouTube. The anaglyph images provide disparity cue in a single image, which could be useful for image analysis. This paper exploits disparity cue for image partition. An image partition method for anaglyph is proposed. The disparity or depth cue is integrated with the traditional single-view image segmentation. A concept called dominant disparity is proposed, corresponding to each single-view image segment, which largely tolerates the disparity errors and image over-segmentations. A cue integration algorithm is developed. The integration is at the level of image segments rather than pixels, and object-level image segmentation is achieved. Experiments on both synthetic and real anaglyph images demonstrate the effectiveness of the proposed image partition method for anaglyph image analysis. To the best of our knowledge, our work is for the first time to perform anaglyph image partition.
Starting Page	983
Ending Page	993
Page Count	11
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	7
Issue Number	6
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2014-10-22
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Anaglyph image analysis Image partition Dominant disparity Cue integration Object-level image segmentation Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Complex Systems Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence 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

A segmentation-based adaptive image enhancement method inspired by the self-adjust features of HVS

Fuzzy clustering with non-local information for image segmentation

Unsupervised segmentation of unknown objects in complex environments

Parity symmetrical collaborative representation-based classification for face recognition

Object segmentation in cluttered and visually complex environments

Sequential conditional entropy maximization semi-supervised hashing for semantic image retrieval

A context-aware semantic modeling framework for efficient image retrieval

Clustering based on Steiner points

Constructing lattice based on irreducible concepts

A cue integration method for anaglyph image partition

Similar Documents

A segmentation-based adaptive image enhancement method inspired by the self-adjust features of HVS

Fuzzy clustering with non-local information for image segmentation

Unsupervised segmentation of unknown objects in complex environments

Parity symmetrical collaborative representation-based classification for face recognition

Object segmentation in cluttered and visually complex environments

Sequential conditional entropy maximization semi-supervised hashing for semantic image retrieval

A context-aware semantic modeling framework for efficient image retrieval

Clustering based on Steiner points

Constructing lattice based on irreducible concepts

A cue integration method for anaglyph image partition