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
TEMPORALLY CONSISTENT DENSE DEPTH MAP ESTIMATION VIA BELIEF PROPAGATION
Date
2009-01-01
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
217
views
0
downloads
Cite This
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 the models. Next, temporal consistency of the depth maps is achieved in two steps. In the first step, median filtering is applied for the static or background pixels, whose intensity levels are constant in time. Such an approach decreases the number of inconsistent depth values significantly. The second step considers the moving pixels and MRF formulation is updated by the additional information from the depth maps of the consequent frames through motion compensation. For the solution of the MRF formulation for both spatial and temporal consistency, Belief Propagation approach is utilized. The experiments indicate that the proposed method provide reliable dense depth map estimates both in spatial and temporal domains.
Subject Keywords
Dense depth map estimation
,
Multi-view video
,
Belief propagation
,
Temporal consistency
URI
https://hdl.handle.net/11511/33370
DOI
https://doi.org/10.1109/3dtv.2009.5069636
Collections
Department of Electrical and Electronics Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Depth assisted object segmentation in multi-view video
Cigla, Cevahir; Alatan, Abdullah Aydın (2008-01-01)
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 ste...
Image fusion for improving spatial resolution of multispectral satellite images
Ünlüsoy, Deniz; Süzen, Mehmet Lütfi; Department of Geological Engineering (2013)
In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı reg...
TEMPORALLY CONSISTENT LAYER DEPTH ORDERING VIA PIXEL VOTING FOR PSEUDO 3D REPRESENTATION
Turetken, Engin; Alatan, Abdullah Aydın (2009-05-06)
A new region-based depth ordering algorithm is proposed based on the segmented motion layers with affine motion models. Starting from an initial set of layers that are independently extracted for each frame of an input sequence, relative depth order of every layer is determined following a bottom-to-top approach from local pair-wise relations to a global ordering. Layer sets of consecutive time instants are warped in two opposite directions in time to capture pair-wise occlusion relations of neighboring lay...
Recursive Prediction for Joint Spatial and Temporal Prediction in Video Coding
Kamışlı, Fatih (2014-06-01)
Video compression systems use prediction to reduce redundancies present in video sequences along the temporal and spatial dimensions. Standard video coding systems use either temporal or spatial prediction on a per block basis. If temporal prediction is used, spatial information is ignored. If spatial prediction is used, temporal information is ignored. This may be a computationally efficient approach, but it does not effectively combine temporal and spatial information. In this letter, we provide a framewo...
Performance improvement of a 3d reconstruction algorithm using single camera images
Kılıç, Varlık; Platin, Bülent Emre; Department of Mechanical Engineering (2005)
In this study, it is aimed to improve a set of image processing techniques used in a previously developed method for reconstructing 3D parameters of a secondary passive target using single camera images. This 3D reconstruction method was developed and implemented on a setup consisting of a digital camera, a computer, and a positioning unit. Some automatic target recognition techniques were also included in the method. The passive secondary target used is a circle with two internal spots. In order to achieve...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
C. Cigla and A. A. Alatan, “TEMPORALLY CONSISTENT DENSE DEPTH MAP ESTIMATION VIA BELIEF PROPAGATION,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/33370.