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
Efficient visibility estimation for distributed virtual urban environments
Download
index.pdf
Date
2008
Author
Koldaş, Gürkan
Metadata
Show full item record
Item Usage Stats
136
views
6443
downloads
Cite This
This research focuses on the utilization of occlusion culling for the real-time visualization of distributed virtual urban environments. Today's graphics hardware renders all the primitives in any order and uses z-buffer to determine which primitives are visible on a per-pixel basis. However, visibility is computed in the last stage of rendering pipeline and every rendered primitive is not visible in the final image. Early culling of the invisible primitives in a complex scene is valuable for efficiency in the conventional rendering pipeline. This may reduce the number of primitives that will be processed in the rest of the pipeline. In this thesis, we propose an efficient visibility estimation method for distributed virtual urban environments. The proposed method is based on occlusion culling to identify and cull the occluded parts of the scene. This not only computes conservative potential visible set (PVS) for each client but also assures the computed PVS to be available at the client on-time and reduces the network traffic by grouping the clients which may see each others dynamically.
URI
http://etd.lib.metu.edu.tr/upload/12609389/index.pdf
https://hdl.handle.net/11511/17595
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
Robust feature space separation for deep convolutional neural network training
Sekmen, Ali; Parlaktuna, Mustafa; Abdul-Malek, Ayad; Erdemir, Erdem; Koku, Ahmet Buğra (2021-11-01)
This paper introduces two deep convolutional neural network training techniques that lead to more robust feature subspace separation in comparison to traditional training. Assume that dataset has M labels. The first method creates M deep convolutional neural networks called {DCNNi}i=1M" role="presentation">{DCNNi}Mi=1. Each of the networks DCNNi" role="presentation">DCNNi is composed of a convolutional neural network (CNNi" role="presentation">CNNi) and a fully connected neural network (FCNNi" role="pre...
A new approach to multivariate adaptive regression splines by using Tikhonov regularization and continuous optimization
TAYLAN, PAKİZE; Weber, Gerhard Wilhelm; Ozkurt, Fatma Yerlikaya (2010-12-01)
This paper introduces a model-based approach to the important data mining tool Multivariate adaptive regression splines (MARS), which has originally been organized in a more model-free way. Indeed, MARS denotes a modern methodology from statistical learning which is important in both classification and regression, with an increasing number of applications in many areas of science, economy and technology. It is very useful for high-dimensional problems and shows a great promise for fitting nonlinear multivar...
Robust background normalization method for one-channel microarrays
AKAL, TÜLAY; Purutçuoğlu Gazi, Vilda; Weber, Gerhard-Wilhelm (Walter de Gruyter GmbH, 2017-04-01)
Background: Microarray technology, aims to measure the amount of changes in transcripted messages for each gene by RNA via quantifying the colour intensity on the arrays. But due to the different experimental conditions, these measurements can include both systematic and random erroneous signals. For this reason, we present a novel gene expression index, called multi-RGX (Multiple-probe Robust Gene Expression Index) for one-channel microarrays.
EFFICIENT SPARSITY-BASED INVERSION FOR PHOTON-SIEVE SPECTRAL IMAGERS WITH TRANSFORM LEARNING
Kamaci, Ulas; Akyon, Fatih C.; Alkanat, Tunc; Öktem, Sevinç Figen (2017-01-01)
We develop an efficient and adaptive sparse reconstruction approach for the recovery of spectral images from the measurements of a photon-sieve spectral imager (PSSI). PSSI is a computational imaging technique that enables higher resolution than conventional spectral imagers. Each measurement in PSSI is a superposition of the blurred spectral images; hence, the inverse problem can be viewed as a type of multi-frame deconvolution problem involving multiple objects. The transform learning-based approach recon...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
G. Koldaş, “Efficient visibility estimation for distributed virtual urban environments,” Ph.D. - Doctoral Program, Middle East Technical University, 2008.