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Lie Group Analysis in Object Recognition
 in Proc. DARPA Image Understanding Workshop
, 1997
"... The techniques of Lie group analysis can be used to determine absolute invariant functions which serve as classifier functions in object recognition problems. Lie group analysis is a powerful tool for analyzing complex systems such as the conservation model used in recent thermophysical invariance ( ..."
Abstract

Cited by 4 (4 self)
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The techniques of Lie group analysis can be used to determine absolute invariant functions which serve as classifier functions in object recognition problems. Lie group analysis is a powerful tool for analyzing complex systems such as the conservation model used in recent thermophysical invariance (TPI) research. We will discuss the mathematics of Lie groups and the application to recognition problems (TPI specifically). The experimental results will demonstrate the validity of the methods and determine the direction of future research. More extensive background and results are available in an extended version of this paper. 1 Introduction In a nutshell here's what these techniques provide and how they can be used in classifying objects: Lie group analysis will determine if there exists a nontrivial function \Phi which assumes a constant value on the set of all roots of an equation f(~z) = 0. The form of the equation remains constant regardless of which particular object we are mea...
Performance Analysis of Order Statistic Constant False Alarm Rate
"... Performance analysis of order statistic constant false alarm rate (CFAR) detectors in generalized Rayleigh environment ..."
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Performance analysis of order statistic constant false alarm rate (CFAR) detectors in generalized Rayleigh environment
www.elsevier.com/locate/patcog Objects based change detection in a pair of graylevel images
"... The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant no ..."
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The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant noise or illumination differences between the input images. For example, one image may be captured in daylight while the other was captured during night with an infrared device. By using a connectivity analysis along graylevel technique, we extract significant blobs from both images. All the extracted blobs are candidates to be classified as changes or part of a change. Then, the candidate blobs from both images are matched. A blob from one image that does not satisfy the matching criteria with its corresponding blob from the other image is considered as an object of change. The algorithm was found to be reliable, fast, accurate, and robust even under extreme changes in illumination and some distortion of the images. The performance of the algorithm is demonstrated using real images. The worstcase time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for realtime applications. � 2004 Published by Elsevier Ltd on behalf of Pattern Recognition Society.
A Comparison of InterFrame Feature Measures For Robust Object Classification in Sector Scan Sonar Image Sequences
"... Abstract—This paper presents an investigation of the robustness of an interframe feature measure classifier for underwater sector scan sonar image sequences. In the initial stages the images are of either divers or remotely operated vehicles (ROV’s). The interframe feature measures are derived fro ..."
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Abstract—This paper presents an investigation of the robustness of an interframe feature measure classifier for underwater sector scan sonar image sequences. In the initial stages the images are of either divers or remotely operated vehicles (ROV’s). The interframe feature measures are derived from sequences of sonar scans to characterize the behavior of the objects over time. The classifier has been shown to produce error rates of 0%–2 % using real nonnoisy images. The investigation looks at the robustness of the classifier with increased noise conditions and changes in the filtering of the images. It also identifies a set of features that are less susceptible to increased noise conditions and changes in the image filters. These features are the mean variance, and the variance of the rate of change in time of the intraframe feature measures area, perimeter, compactness, maximum dimension and the first and second invariant moments of the objects. It is shown how the performance of the classifier can be improved. Success rates of up to 100 % were obtained for a classifier trained under normal noise conditions, signaltonoise ratio (SNR) around 9.5 dB, and a noisy test sequence of SNR 7.6 dB. Index Terms—Robust classification, remotely operated vehicles, sonar images. I.