• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.758-766
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• 2003

This paper proposes a tree-based hierarchical image mosaicing system using camera and object parameters for efficient video database construction. Gray level histogram difference and average intensity difference are proposed for scene change detection of input video. Camera parameter measured by utilizing least sum of square difference and affine model, and difference image is used for similarity measure of two input images. Also, dynamic objects are searched by through macro block setting and extracted by using region splitting and 4-split detection methods. Dynamic trajectory evaluation function is used for expression of dynamic objects, and blurring is performed for construction of soft and slow mosaic image.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.167-175
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• 2002

Image Mosaicing creates a new image by composing video frames or still images that are related, and performed by arrangement, composition and redundancy analysis of images. This paper proposes a hierarchical image mosaicing system using camera and object parameters far efficient video database construction. A tree-based image mosiacing has implemented for high-speed computation time and for construction of static and dynamic image mosaic. Camera parameters are measured by using least sum of squared difference and affine model. Dynamic object detection algorithm has proposed for extracting dynamic objects. For object extraction, difference image, macro block, region splitting and 4-split detection methods are proposed and used. Also, a dynamic positioning method is used for presenting dynamic objects and a blurring method is used for creating flexible mosaic image.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.431-440
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• 2013

We consider the problem of estimating the parameters of heavy tailed distributions. In general, maximum likelihood estimation(MLE) is the most preferred method of parameter estimation because it has good properties such as asymptotic consistency, normality and efficiency. However, MLE is not always the best solution because MLE is unstable or does not exist in some cases. This paper proposes another parameter estimation method, non-linear least squares(NLS) and compares its performance to MLE. The NLS estimator is achieved by minimizing sum of squared difference between empirical cumulative distribution function(CDF) and a theoretical distribution function. In this article, we compare the NLS method to MLE using simulated data from heavy tailed distributions. The NLS method is shown to perform better than MLE in Burr distribution when the sample size is small; in addition, it performs well in a Frechet distribution.