1 edition of Region of Interest Coding for Aerial Video Sequences Using Landscape Models found in the catalog.
by INTECH Open Access Publisher
Written in English
|Contributions||Jörn Ostermann, author, Julia Schmidt, author, Marco Munderloh, author|
|The Physical Object|
|Pagination||1 online resource|
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Holger Meuel, Julia Schmidt, Marco Munderloh and Jörn Ostermann (January 9th ). Region of Interest Coding for Aerial Video Sequences Using Landscape Models, Advanced Video Coding for Next-Generation Multimedia Services, Yo-Sung Ho, IntechOpen, DOI: / Available from:Cited by: 2.
Region of Interest Coding for Aerial Video Sequences Using Landscape Models 3 0 2 4 6 8 10 12 Data rate in Mbit/s. This book aims to bring together recent advances and applications of video coding. All chapters can be useful for researchers, engineers, graduate and postgraduate students, experts in this area, and hopefully also for people who are generally interested in video coding.
The book includes nine carefully selected chapters. The chapters deal with advanced compression techniques for multimedia Author: Yo-Sung Ho. Jul 28, · Robust Long-Term Aerial Video Mosaicking by Weighted Feature-Based Global Motion Estimation. Authors; J., Munderloh, M., Ostermann, J.: Region of interest coding for aerial video sequences using landscape models.
In: Advanced Video Coding for Next-Generation Multimedia Services. Ostermann J. () Robust Long-Term Aerial Video Author: Holger Meuel, Stephan Ferenz, Florian Kluger, Jörn Ostermann. The title of his thesis is "Analysis of Affine Motion Compensated Prediction and its Application in Aerial Video Coding".
Links of Interest: Aerial Video Testset. TNT Aerial Video Testset (TAVT) Demo Laptop: ROI Coding using AVC and HEVC. Region of Interest (ROI)-based Video Coding; Open Hiwi positions.
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HBased Depth Map Sequence Coding Using Motion Information of Corresponding Texture Video. results show that the proposed algorithm reduces the complexity to 60% of the previous scheme that encodes two sequences separately and coding performance is also improved up to 1dB at low bit rates.
HBased Depth Map Sequence Coding Using Cited by: Mar 20, · Terrain Analysis: A Guide to Site Selection Using Aerial Photographic Interpretation (Community Development Series) [Douglas S.
Way] on texasbestchambers.com *FREE* shipping on 3/5(3). MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING. Jung a, *, K.
Bang, G. Sohn a, C. Armenakis a. a Dept. of Earth and Space Science and Engineering, York University Keele Street, Toronto, ON, M3J1P3, Canada - (jwjung kiinbang, gsohn, armenc)@texasbestchambers.com Commission I, WG I/3Author: J.
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We welcome scientists, artists, journalists, policymakers, or anyone interested in. Automatic Building Extraction in Aerial Scenes Using Convolutional Networks Jiangye Yuan Computational Sciences & Engineering Division Oak Ridge National Laboratory Oak Ridge, Tennessee Email: [email protected] Abstract—Automatic building extraction from aerial and satel-lite imagery is highly challenging due to extremely large vari.
Integrated position estimation using aerial image sequences Article (PDF Available) in IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1) · February with Reads. An Approach to Distributed Video Coding Using 3D Face Models 3D face model is used in the decoder SI estimation for head and shoulder video sequences.
Here, by warping the 3D face model to the. Matching Aerial Images to 3D Building Models Using Context-Based Geometric Hashing In a similar way, Avbelj et al. used point features to align 3D wire-frame building models with infrared video sequences using a subsequent Figure 1 illustrates the proposed method for registering a single image with existing 3D building models using Cited by: 7.
A regular point sampling procedure, using a “Densify” tool (distance m), and a subsequent iterative “Extract Values to Points” process allowed us to collect the related landscape type sequences (cluster IDs), and the local trends of the four spatial index values Cited by: Interpreting Models of Arithmetic Sequences You can model real-world situations and solve problems using models of arithmetic sequences.
For example, suppose watermelons cost $ each at the local market. The total cost, in dollars, of n watermelons can be found using c(n) = m A Complete the table of values for 1, 2, 3, and 4 watermelons. May 25, · 1. Introduction: digital landscape modeling. Digital computer models are routinely used in landscape architecture, design and planning and other allied disciplines for visualization of proposals, evaluation of alternatives, and simulation of impacts, broadly texasbestchambers.com by: the landscape and visualize the modified scene by simulation.
The generation of digital terrain models from aerial images using stereo matching techniques is described by various authors . However this approach hardly works for complex scenes and fails when a high level of detail is required. Natural culture.
Computer Analysis of Images and Patterns: 15th International Conference, CAIPYork, UK, Augustproceedings. Part II / The two volume set LNCS and constitutes the refereed proceedings of the 15th International Conference on Computer Analysis of Images and Patterns, CAIPheld in York, UK, in August G06T is the functional place for image data processing or generation.
Image data processing or generation specially adapted for a particular application is classified in the relevant subclass. Documents which merely mention the general use of image processing or generation without detailing of the underlying details of such, are classified in the application place.photographs to model and discriminate green small landscape elements.
Lucien Davids. Using LiDAR in combination with aerial photographs to model and discriminate green small landscape elements. Master Thesis. detection by using a region growing algorithm) so that all small landscape elements can be detected.
Keywords: GIS, LiDAR, remote.