Download PDF by B. V. K. Vijaya Kumar: Correlation Pattern Recognition

By B. V. K. Vijaya Kumar

ISBN-10: 0521571030

ISBN-13: 9780521571036

Correlation is a strong and common procedure for development acceptance and is utilized in many functions, similar to computerized objective popularity, biometric popularity and optical personality popularity. The layout, research and use of correlation trend popularity algorithms calls for historical past details, together with linear structures concept, random variables and techniques, matrix/vector tools, detection and estimation thought, electronic sign processing and optical processing. This publication offers a wanted assessment of this various heritage fabric and develops the sign processing concept, the trend acceptance metrics, and the sensible program information from simple premises. It exhibits either electronic and optical implementations. It additionally includes expertise offered by means of the group that built it and contains case stories of important curiosity, akin to face and fingerprint reputation. compatible for graduate scholars taking classes in development reputation concept, while attaining technical degrees of curiosity to the pro practitioner.

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Thus the PDF of y can be related to the PDF of x as follows: fY ðyÞ ¼ 1 À À1 Á fX A y jAj (2:104) Applying the above result and the N-variate Gaussian PDF in Eq. 101), we can see that the transformed random vector is also Gaussian, but with a new mean vector and new covariance matrix. 5 Probability and random variables ! ÁT À1 À À1 Á 1 1 1 À À1 qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp À A y À mX CX A y À mX fY ðyÞ ¼ 2 jAj ð2pÞN jCX j ! 1 1 T À1 ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi exp À ðy À mY Þ CY ðy À mY Þ 2 ð2pÞN jCY j 45 (2:105) where mY ¼ AmX and CY ¼ ACX AT The above result clearly shows that the result of an invertible affine transformation is also a Gaussian vector.

0]) as the eigenvectors. Let E denote a square matrix whose columns are ei, the normalized eigenvectors, and let L denote a diagonal matrix whose diagonal entries are the N eigenvalues. Then Eq. , A ¼ ET. If y ¼ Ax, then the new covariance is a diagonal matrix as shown below. CY ¼ ACX AT ¼ ET CX E ¼ ET EL ¼ L ¼ Diagfl1 ; l2 ; : : : ; lN g (2:108) Thus, using A ¼ ET results in new RVs y that are uncorrelated since the new covariance matrix is diagonal. If the random vector x is Gaussian, then A ¼ ETx is also Gaussian since the transformation is linear.

This experiment has a random outcome in that the output can be any one of the six numbers. We define an event as a set containing some of these outcomes. , {2, 4, 6}). , the set with no elements in it. Probability theory provides a framework to define probabilities of such events. 5 Probability and random variables 29 every event. The probability of null event 1 is zero and the probability of the sample set S is one, because the set S includes all possible outcomes. We also need a method to determine the probabilities of more complicated events such as {A \ B}.

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Correlation Pattern Recognition by B. V. K. Vijaya Kumar


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