NDLI: Statistical feature selection model for robust 3D object recognition

Content Provider	IEEE Xplore Digital Library
Author	Woongji Jeong Sukhan Lee Yongho Kim
Copyright Year	2011
Description	Author affiliation: Intelligent System Research Center, School of Information and Communication Engineering, SungKyunKwan University, Suwon Korea (Woongji Jeong; Sukhan Lee; Yongho Kim)
Abstract	This paper presents the feature selection with statistic modeling in real environment for 3D object recognition and pose estimation. For robust object recognition and pose estimation in various environments, we attempt using various features (SIFT, line, and color). However, each feature's reliability changes as environment changes such as illumination, occlusion, and distance. We estimate the changes of features in different environments to make reasonable feature selection using following methods. We predict expected feature quantity by combing detection probability (statistical model) of each feature and idle feature quantity (object model) of an object that we can see current viewpoint. Moreover, we calculate each feature's reliability by combining utility function to decide if expected features are valid in object recognition and pose estimation process. Based on the final probability, we decide the optimal feature. Selecting the optimal feature in environmental change enables fusion and filtering. We can recognize objet and estimate pose under severe environments. Moreover, there is calculation benefit as does not use features with low reliability. Our method verified performance of algorithm through real environment experiments.
Starting Page	402
Ending Page	408
File Size	615236
Page Count	7
File Format	PDF
ISBN	9781457711589
e-ISBN	9781457711596
DOI	10.1109/ICAR.2011.6088606
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-20
Publisher Place	Estonia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Three dimensional displays Image color analysis Color Lighting Reliability Object recognition
Content Type	Text
Resource Type	Article

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

3D object recognition using multi-moment and neural network

3D Selective Search for obtaining object candidates

Feature extraction for 3D face recognition system

3D object matching based on local feature combined with color

Decolorizing images for robust matching

A fast and robust descriptor for multiple-view object recognition

Training neural networks using Clonal Selection Algorithm and Particle Swarm Optimization: A comparisons for 3D object recognition

Scale and rotation invariant color features for weakly-supervised object Learning in 3D space

Target object identification and localization in mobile manipulations

Statistical feature selection model for robust 3D object recognition

Similar Documents

3D object recognition using multi-moment and neural network

3D Selective Search for obtaining object candidates

Feature extraction for 3D face recognition system

3D object matching based on local feature combined with color

Decolorizing images for robust matching

A fast and robust descriptor for multiple-view object recognition

Training neural networks using Clonal Selection Algorithm and Particle Swarm Optimization: A comparisons for 3D object recognition

Scale and rotation invariant color features for weakly-supervised object Learning in 3D space

Target object identification and localization in mobile manipulations

Statistical feature selection model for robust 3D object recognition