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Video and Image Analysis using Statistical and Machine Learning Techniques
Title
Video and Image Analysis using Statistical and Machine Learning Techniques.
Creator
Luo, Qiming, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Digital videos and images are effective media for capturing spatial and ternporal information in the real world. The rapid growth of digital videos has motivated research aimed at developing effective algorithms, with the objective of obtaining useful information for a variety of application areas, such as security, commerce, medicine, geography, etc. This dissertation presents innovative and practical techniques, based on statistics and machine learning, that address some key research...
Show moreDigital videos and images are effective media for capturing spatial and ternporal information in the real world. The rapid growth of digital videos has motivated research aimed at developing effective algorithms, with the objective of obtaining useful information for a variety of application areas, such as security, commerce, medicine, geography, etc. This dissertation presents innovative and practical techniques, based on statistics and machine learning, that address some key research problems in video and image analysis, including video stabilization, object classification, image segmentation, and video indexing. A novel unsupervised multi-scale color image segmentation algorithm is proposed. The basic idea is to apply mean shift clustering to obtain an over-segmentation, and then merge regions at multiple scales to minimize the MDL criterion. The performance on the Berkeley segmentation benchmark compares favorably with some existing approaches. This algorithm can also operate on one-dimensional feature vectors representing each frame in ocean survey videos, which results in a novel framework for building a hierarchical video index. The advantage is to provide the user with the flexibility of browsing the videos at arbitrary levels of detail, which makes it more efficient for users to browse a long video in order to find interesting information based on the hierarchical index. Also, an empirical study on classification of ships in surveillance videos is presented. A comparative performance study on three classification algorithms is conducted. Based on this study, an effective feature extraction and classification algorithm for classifying ships in coastline surveillance videos is proposed. Finally, an empirical study on video stabilization is presented, which includes a comparative performance study on four motion estimation methods and three motion correction methods. Based on this study, an effective real-time video stabilization algorithm for coastline surveillance is proposed, which involves a novel approach to reduce error accumulation.
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Date Issued
2007
PURL
http://purl.flvc.org/fau/fd/FA00012574
Subject Headings
Image processing--Digital techniques, Electronic surveillance, Computational learning theory
Format
Document (PDF)
A Study in Implementing Autonomous Video Surveillance Systems Based on Optical Flow Concept
Title
A Study in Implementing Autonomous Video Surveillance Systems Based on Optical Flow Concept.
Creator
Fonseca, Alvaro A., Zhuang, Hanqi, Marques, Oge, Florida Atlantic University
Abstract/Description
Autonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on...
Show moreAutonomous video surveillance systems are usually built with several functional blocks such as motion detection, foreground and background separation, object tracking, depth estimation, feature extraction and behavioral analysis of tracked objects. Each of those blocks is usually designed with different techniques and algorithms, which may need significant computational and hardware resources. In this thesis we present a surveillance system based on an optical flow concept, as a main unit on which other functional blocks depend. Optical flow limitations, capabilities and possible problem solutions are discussed in this thesis. Moreover, performance evaluation of various methods in handling occlusions, rigid and non-rigid object classification, segmentation and tracking is provided for a variety of video sequences under different ambient conditions. Finally, processing time is measured with software that shows an optical flow hardware block can improve system performance and increase scalability while reducing the processing time by more than fifty percent.
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Date Issued
2007
PURL
http://purl.flvc.org/fau/fd/FA00012516
Subject Headings
Electronic surveillance, Optical pattern recognition, Computer vision, Optical flow--Image analysis
Format
Document (PDF)