Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Depth assisted object segmentation in multi-view video
Date
2008-01-01
Author
Cigla, Cevahir
Alatan, Abdullah Aydın
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
210
views
0
downloads
Cite This
In this work, a novel and unified approach for multi-view video (MVV) object segmentation is presented. In the first stage, a region-based graph-theoretic color segmentation algorithm is proposed, in which the popular Normalized Cuts segmentation method is improved with some modifications on its graph structure. Segmentation is obtained by recursive bi-partitioning of a weighted graph of an initial over-segmentation mask. The available segmentation mask is also utilized during dense depth map estimation step, based on a novel modified plane- and angle-sweeping strategy for each of these regions. Dense depth estimation is achieved by region-wise planarity assumption for the whole scene, in which depth models are estimated for sub-regions. Finally, the multi-view image segmentation algorithm is extended to object segmentation in MVV by the additional optical flow information. The required motion field is obtained via region-based matching that has consistent parameterization with color segmentation and dense depth map estimation algorithms. Experimental results indicate that proposed approach segments semantically meaningful objects in MVV with high precision.
Subject Keywords
Graph-theoretic image segmentation
,
Dense depth map estimation
,
Multi-view video object segmentation
URI
https://hdl.handle.net/11511/36476
DOI
https://doi.org/10.1109/3dtv.2008.4547839
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Streaming Multiscale Deep Equilibrium Models
Ertenli, Can Ufuk; Akbaş, Emre; Cinbiş, Ramazan Gökberk (2022-1-01)
We present StreamDEQ, a method that infers frame-wise representations on videos with minimal per-frame computation. In contrast to conventional methods where compute time grows at least linearly with the network depth, we aim to update the representations in a continuous manner. For this purpose, we leverage the recently emerging implicit layer models, which infer the representation of an image by solving a fixed-point problem. Our main insight is to leverage the slowly changing nature of videos and use the...
Image segmentation with unified region and boundary characteristics within recursive shortest spanning tree
Esen, E.; Alp, Y. K. (2007-06-13)
The lack of boundary information in region based image segmentation algorithms resulted in many hybrid methods that integrate the complementary information sources of region and boundary, in order to increase the segmentation performance. In compliance with this trend, we propose a novel method to unify the region and boundary characteristics within the canonical Recursive Shortest Spanning Tree algorithm. The main idea is to incorporate the boundary information in the distance metric of RSST with minor cha...
TEMPORALLY CONSISTENT DENSE DEPTH MAP ESTIMATION VIA BELIEF PROPAGATION
Cigla, Cevahir; Alatan, Abdullah Aydın (2009-01-01)
A method for estimating temporally and spatially consistent dense depth maps in multiple camera setups is presented which is important for reduction of perception artifacts in 3D displays. For this purpose, initially, depth estimation is performed for each camera with the piece-wise planarity assumption and Markov Random Field (MRF) based relaxation at each time instant independently. During the relaxation step, the consistency of depth maps for different cameras is also considered for the reliability of th...
Summarizing video: Content, features, and HMM topologies
Yasaroglu, Y; Alatan, Abdullah Aydın (2003-01-01)
An algorithm is proposed for automatic summarization of multimedia content by segmenting digital video into semantic scenes using HMMs. Various multi-modal low-level features are extracted to determine state transitions in HMMs for summarization. Advantage of using different model topologies and observation sets in order to segment different content types is emphasized and verified by simulations. Performance of the proposed algorithm is also compared with a deterministic scene segmentation method. A better...
QUALITY EVALUATION OF STEREOSCOPIC VIDEOS USING DEPTH MAP SEGMENTATION
Sarikan, Selim S.; Olgun, Ramazan F.; Akar, Gözde (2011-09-09)
This paper presents a new quality evaluation model for stereoscopic videos using depth map segmentation. This study includes both objective and subjective evaluation. The goal of this study is to understand the effect of different depth levels on the overall 3D quality. Test sequences with different coding schemes are used. The results show that overall quality has a strong correlation with the quality of the background, where disparity is smaller relative to the foreground. The results also showed that con...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
C. Cigla and A. A. Alatan, “Depth assisted object segmentation in multi-view video,” 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36476.