Scene Analysis Using Scale Invariant Feature Extraction and Probabilistic Modeling

Use of this dissertation is restricted to the UNT Community. Off-campus users must log in to read.

Description

Conventional pattern recognition systems have two components: feature analysis and pattern classification. For any object in an image, features could be considered as the major characteristic of the object either for object recognition or object tracking purpose. Features extracted from a training image, can be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable scene analysis, it is important that the features extracted from the training image are detectable even under changes in image scale, noise and illumination. Scale invariant feature has wide applications such as image ... continued below

Creation Information

Shen, Yao August 2011.

Context

This dissertation is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 112 times . More information about this dissertation can be viewed below.

Who

People and organizations associated with either the creation of this dissertation or its content.

Author

Chairs

Committee Members

Publisher

Rights Holder

For guidance see Citations, Rights, Re-Use.

  • Shen, Yao

Provided By

UNT Libraries

Library facilities at the University of North Texas function as the nerve center for teaching and academic research. In addition to a major collection of electronic journals, books and databases, five campus facilities house just under six million cataloged holdings, including books, periodicals, maps, documents, microforms, audiovisual materials, music scores, full-text journals and books. A branch library is located at the University of North Texas Dallas Campus.

Contact Us

What

Descriptive information to help identify this dissertation. Follow the links below to find similar items on the Digital Library.

Degree Information

Description

Conventional pattern recognition systems have two components: feature analysis and pattern classification. For any object in an image, features could be considered as the major characteristic of the object either for object recognition or object tracking purpose. Features extracted from a training image, can be used to identify the object when attempting to locate the object in a test image containing many other objects. To perform reliable scene analysis, it is important that the features extracted from the training image are detectable even under changes in image scale, noise and illumination. Scale invariant feature has wide applications such as image classification, object recognition and object tracking in the image processing area. In this thesis, color feature and SIFT (scale invariant feature transform) are considered to be scale invariant feature. The classification, recognition and tracking result were evaluated with novel evaluation criterion and compared with some existing methods. I also studied different types of scale invariant feature for the purpose of solving scene analysis problems. I propose probabilistic models as the foundation of analysis scene scenario of images. In order to differential the content of image, I develop novel algorithms for the adaptive combination for multiple features extracted from images. I demonstrate the performance of the developed algorithm on several scene analysis tasks, including object tracking, video stabilization, medical video segmentation and scene classification.

Language

Collections

This dissertation is part of the following collection of related materials.

UNT Theses and Dissertations

Theses and dissertations represent a wealth of scholarly and artistic content created by masters and doctoral students in the degree-seeking process. __Some ETDs in this collection are restricted to use by the UNT community__.

What responsibilities do I have when using this dissertation?

When

Dates and time periods associated with this dissertation.

Creation Date

  • August 2011

Added to The UNT Digital Library

  • May 17, 2012, 9:47 p.m.

Description Last Updated

  • Oct. 9, 2012, 11:46 a.m.

Usage Statistics

When was this dissertation last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 112

Interact With This Dissertation

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

Citations, Rights, Re-Use

Shen, Yao. Scene Analysis Using Scale Invariant Feature Extraction and Probabilistic Modeling, dissertation, August 2011; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc84275/: accessed February 26, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .