Current Search:   Culibrk, Dubravko (x)

View All Items

  • CSV Spreadsheet
(1 - 3 of 3)
A hybrid color‐based foreground object detection method for automated marine surveillance
Title
A hybrid color‐based foreground object detection method for automated marine surveillance.
Creator
Furht, Borko, Kalva, Hari, Marques, Oge, Culibrk, Dubravko, Socek, Daniel
Date Issued
2005
PURL
http://purl.flvc.org/fcla/dt/358420
Subject Headings
Computer vision., Automatic tracking., Digital video., Image processing., Optical pattern recognition.
Format
Document (PDF)
New approaches to encryption and steganography for digital videos
Title
New approaches to encryption and steganography for digital videos.
Creator
Furht, Borko, Socek, Daniel, Kalva, Hari, Magliveras, Spyros S., Marques, Oge, Culibrk, Dubravko
Date Issued
2007
PURL
http://purl.flvc.org/fcla/dt/337435
Subject Headings
Multimedia systems --Security measures., Digital video., Digital watermarking., Data encryption (Computer science) --Technological innovations., Cryptography.
Format
Document (PDF)
Neural network approach to Bayesian background modeling for video object segmentation
Title
Neural network approach to Bayesian background modeling for video object segmentation.
Creator
Culibrk, Dubravko., Florida Atlantic University, Furht, Borko, Marques, Oge, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Object segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based...
Show moreObject segmentation in a video sequence is an essential task in video processing and forms the foundation of content analysis, scene understanding, object-based video encoding (e.g. MPEG-4), various surveillance and 2D-to-pseudo-3D conversion applications. Popularization and availability of video sequences with increased spatial resolution requires development of new, more efficient algorithms for object detection and segmentation. This dissertation discusses a novel neural-network-based approach to background modeling for motion-based object segmentation in video sequences. In particular, we show how Probabilistic Neural Network (PNN) architecture can be extended to form an unsupervised Bayesian classifier for the domain of video object segmentation. The constructed Background Modeling Neural Network (BNN) is capable of efficiently handling segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed neural network serve as an exclusive model of the background and are temporally updated to reflect the observed background statistics. The proposed approach is designed to enable an efficient, highly-parallelized hardware implementation. Such a system would be able to achieve real-time segmentation of high-resolution image sequences.
Show less
Date Issued
2006
PURL
http://purl.flvc.org/fcla/dt/12214
Subject Headings
Neural networks (Computer science), Application software--Development, Data structures (Computer science), Bayesian field theory
Format
Document (PDF)