Chang, Yun Fah (2012) Astatistical performance in dicator in some image processing problems / Chang Yun Fah. PhD thesis, University of Malaya.
Abstract
The ability to compare or relate two digital images may be useful in developing performance evaluation algorithms. This thesis investigates the use of a particular correlation measure, 2 p R developed from the multidimensional unreplicated linear functional relationship (MULFR) model with single slope, as a measure or indicator of performance. This MULFR model is an extended version of the ULFR model introduced by Adcock in 1877. A literature survey was carried out showing that 2 p R has not been used before. The coefficient 2 p R was investigated in its ability to handle the issues of non-perfect reference image, multiple image attributes and combining image local-global information simultaneously. This survey is followed with the maximum likelihood estimation of parameters and a brief discussion of some theoretical properties of 2 p R . To investigate robust properties of 2 p R , an extensive simulation exercise was then carried out. Promising results, thus far, motivate the use of 2 p R in two image analysis problems; firstly a character recognition problem and secondly a particular data compression problem. In a handwritten Chinese character recognition problem, the 2 p R achieved the highest recognition rates even the pre-processing stage is removed from the recognition system. A substantial reduction of processing time, approximately 40.36% to 75.31%, was achieved using 2 p R . In JPEG compression problem, 2 p R is used as a measure of image quality which in turn indicates the performance of the compression method. It is shown that 2 p R performs well and satisfies the monotonicity, accuracy and consistency properties when perfect reference image was used. 2 p R was also shown to perform better than some frequently used similarity measures when imperfect reference image was used.
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