Abstract

Video object extration is one of the most important areas of video processing in which objects from video sequences are extracted and used for many applications such as surveillance systems, pattern recognition etc. In this research work, an object-based technique based on the spatiotemporal independent component analysis (stICA) is developed to extract moving objects from video sequences. Using the stICA, the preliminary source images containing moving objects in the video sequence are extracted. These images are processed using wavelet analysis, edge detection, region growing and multiscale segmentation techniques to improve the accuracy of the extracted objects. A novel compensation method is applied to deal with the nonlinear problem caused by the application of the stICA directly to the video sequences. The recovered objects are indexed by the singular calue decompensation (SVD) and linear combination analysis. Simulation results demonstrate the effectiveness of the stICA-based object extraction technique in content-based video processing applications.

Highlights

  • Object1Back- Object 3 ground Object 1 Mixing Matrix IObject 21IObjoct 21 Objoel31 - - _ -, Back round Object 2 Object 3Fig. 3.1 demonstrates how the spatiotemporal independent component analysis (stICA) model is applied to video frames

  • We introduce a novel statistical analysis method based on the stlCA

  • A novel compensation method to deal with the nonlinear combination problem in the stICA model for video sequences

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Summary

Motivation

HE increasing popularity of video processing is due to the high demand for video in entertainment, security related applications, education, tele-medicine, database and new wireless telecommunications. Video content-based techniques are aimed at achieving significant data reduction of video by applying suitable transformation on video sequences based on their content. This data reduction has two main advantages: video databases work efficiently for searching content-based videos, and processing cost reduces dramatically. The content-based video presentation is an essential need for broadcasting services, Internet and security applications. This thesis develops a framework for automated content-based video processing based on the spatiotemporal independent component analysis (stICA). Both theoretical derivation and simulation results are provided to illustrate the effectiveness of the presented methods

Review of Previous Works
Objectives
Proposed Approaches and Methodologies
Overview of the Thesis
Principle Component Analysis
Singular Value Decomposition
Whitening
Independent Component Analysis
Comparison of PCA, Whitening and ICA
The leA Estimation Methods
Spatiotemporal leA
Summary
Formulation of the stICA Model for Video Sequences
The stICA Based Video Segmentation Approach
Shnulation of the stICA applied to Video Processing in the First Iteration
FindROI
Chapter 4 Post-processing in the First Iteration
Image Edge Detection with Region Grewing
The Mark Matrix element value cannot be 1
Multiscale Image Segmentation
Simulation of Wavelet Analysis to Locate ROIs
Simulations
Findings
Contribution
Full Text
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