NDLI: Accurate and Robust Moving-Object Segmentation for Telepresence Systems

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ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

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Visual Understanding with RGB-D Sensors: An Introduction to the Special Issue

Kinect Depth Recovery Using a Color-Guided, Region-Adaptive, and Depth-Selective Framework

Depth Error Elimination for RGB-D Cameras

A Combined Approach Toward Consistent Reconstructions of Indoor Spaces Based on 6D RGB-D Odometry and KinectFusion

Robust Multiview Feature Learning for RGB-D Image Understanding

From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models

Accurate and Robust Moving-Object Segmentation for Telepresence Systems

Fusing Multiple Features for Depth-Based Action Recognition

An Evaluation of Gamesourced Data for Human Pose Estimation

Latent Support Vector Machine Modeling for Sign Language Recognition with Kinect

A Real-Time Hand Posture Recognition System Using Deep Neural Networks

Real-Time System for Driver Fatigue Detection by RGB-D Camera

An Approach to Ballet Dance Training through MS Kinect and Visualization in a CAVE Virtual Reality Environment

Exploring Spatial Correlation for Visual Object Retrieval

Pattern Matching Techniques for Replacing Missing Sections of Audio Streamed across Wireless Networks

An Association-Based Unified Framework for Mining Features and Opinion Words

A Hybrid Multigroup Coclustering Recommendation Framework Based on Information Fusion

Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population

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Accurate and Robust Moving-Object Segmentation for Telepresence Systems

Content Provider	ACM Digital Library
Author	Huang, Meiyu Miao, Chunyan Chen, Yiqiang Ji, Wen
Copyright Year	2015
Abstract	Moving-object segmentation is the key issue of Telepresence systems. With monocular camera--based segmentation methods, desirable segmentation results are hard to obtain in challenging scenes with ambiguous color, illumination changes, and shadows. Approaches based on depth sensors often cause holes inside the object and missegmentations on the object boundary due to inaccurate and unstable estimation of depth data. This work proposes an adaptive multi-cue decision fusion method based on Kinect (which integrates a depth sensor with an RGB camera). First, the algorithm obtains an initial foreground mask based on the depth cue. Second, the algorithm introduces a postprocessing framework to refine the segmentation results, which consists of two main steps: (1) automatically adjusting the weight of two weak decisions to identify foreground holes based on the color and contrast cue separately; and (2) refining the object boundary by integrating the motion probability weighted temporal prior, color likelihood, and smoothness constraint. The extensive experiments we conducted demonstrate that our method can segment moving objects accurately and robustly in various situations in real time.
Starting Page	1
Ending Page	28
Page Count	28
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2629480
Volume Number	6
Issue Number	2
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-03-31
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Foreground segmentation Adaptive multi-cue decision fusion Foreground hole detection Object boundary refining
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

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

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Shape-Based Object Detection via Boundary Structure Segmentation

Learning Complementary Saliency Priors for Foreground Object Segmentation in Complex Scenes

A fuzzy spatial coherence-based approach to background/foreground separation for moving object detection

Accurate and Robust Moving-Object Segmentation for Telepresence Systems

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