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  Access Rights: Use restricted to UNT Community
 Decade: 2010-2019
 Degree Discipline: Computer Science and Engineering
 Collection: UNT Theses and Dissertations
Scene Analysis Using Scale Invariant Feature Extraction and Probabilistic Modeling

Scene Analysis Using Scale Invariant Feature Extraction and Probabilistic Modeling

Access: Use of this item is restricted to the UNT Community.
Date: August 2011
Creator: Shen, Yao
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 ...
Contributing Partner: UNT Libraries