Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques

One of the most important steps of document image processing is binarization. The computational requirements of locally adaptive binarization techniques make them unsuitable for devices with limited computing facilities. In this paper, we have presented a computationally efficient implementation of convolution based locally adaptive binarization techniques keeping the performance comparable to the original implementation. The computational complexity has been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the image size. Experiments over benchmark datasets show that the computation time has been reduced by 5 to 15 times depending on the window size while memory consumption remains the same with respect to the state-of-the-art algorithmic implementation

Mathematical statistics/ Kapur

xvi, 645 hal.: ill, tab.; 23 cm

Normalized measures of entropy

A number of “normalized” measures ol entropy have been obtained to measure the “intrinsic” uncertainty of a probability distribution. Their graphs have been drawn and it has been shown that the normalized measures of a given probability distribution are much closer to one another than the corresponding absolute measures of entropy

GENERALIZATION OF THE ENTROPY MODEL FOR BRAND PURCHASE BEHAVIOR.

J. D. Herniter's entropy model for brand purchase behavior has been generalized for A. Renyi's measure of entropy which is a more general concept than C. E. Shannon's measure of entropy used by Herniter and which includes Shannon's measure as a limiting case. The generalized model considered here is more flexible than Herniter's model since it can give different marketing statistics for different products and it can give these statistics even when only some of the brands are considered

Some normalized measures of directed divergence

In a recent paper, we discussed normalized measures of entropy,13 i.e. measures of entropy all of which lie between 0 and 1. This leads to standardized measures which can be compared with one another. In the present paper, we discuss normalized measures of directed divergence, all of which lie between 0 and 1 and as such are comparable with one another