Prasanna K. Sahoo

A new method for gray-level picture thresholding using the entropy of the histogram

Abstract Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223–237;Comput. Graphics Image Process.16, 1981, 210–239) have been carefully and critically examined. A new method with a sound theoretical foundation is proposed. Examples are given on a number of real and artifically generated histograms.

A survey of thresholding techniques

Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.

Threshold selection using Renyi's entropy

Image segmentation is an important and fundamental task in many digital image processing systems. Image segmentation by thresholding is the simplest technique and involves the basic assumption that objects and background in the digital image have distinct gray-level distributions. In this paper, we present a general technique for thresholding of digital images based on Renyis entropy. Our method includes two of the previously proposed well known global thresholding methods. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.

A gray-level threshold selection method based on maximum entropy principle

A description is given of a gray-level threshold selection method for image segmentation that is based on the maximum entropy principle. The optimal threshold value is determined by maximizing the a posteriori entropy subject to certain inequality constraints which are derived by means of spectral measures characterizing uniformity and the shape of the regions in the image. For this purpose, the authors use both the gray-level distribution and the spatial information of an image. The effectiveness of the method is demonstrated by its performance on some real-world images. An extension of this method to chromatic images is provided. >

A thresholding method based on two-dimensional Renyi's entropy

In this paper, we present a new thresholding technique based on two-dimensional Renyis entropy. The two-dimensional Renyis entropy was obtained from the two-dimensional histogram which was determined by using the gray value of the pixels and the local average gray value of the pixels. This new method extends a method due to Sahoo et al. (Pattern Recognition 30 (1997) 71) and includes a previously proposed global thresholding method due to Abutaleb (Pattern Recognition 47 (1989) 22). Further, our method extends a global thresholding method due to Chang et al. (IEEE Trans. Image Process. 4 (1995) 370) to the two-dimensional setting. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.

Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy

In this paper, we present a thresholding technique based on two-dimensional Tsallis-Havrda-Charvat entropy. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.

Threshold selection using a minimal histogram entropy difference

A new gray-level threshold selection method for image segmentation is presented. It is based on minimizing the difference between entropies of the object and the background distributions of the gray-level histogram. The proposed method is similar to the maximum entropy method proposed by Kapur et al. (1985), however, the new method provided a good threshold value in many instances where the previous method did not. The effectiveness of our method is demonstrated by its performance on videomicroscopic images of the rat lung. Extension of the method to higher order probability density functions is described.

THE SPACE OF (U, Y)ADDITIVE MAPPINGS ON SEMIGROUPS

In this paper, we introduce the concept of (ψ,γ)-pseudoadditive mappings from a semigroup into a Banach space, and we provide a generalized solution of Ulams problem for approximately additive mappings.

Characterizations of information measures

The branching property recursivity properties the fundamental equation of information and regular recursive measures sum form information measures and additivity properties sum form information measures additive sum form information measures additive sum form information measures of type 1 additive sum form information measures of multiplicative type.

Measures of distance between probability distributions

Abstract In statistical estimation problems measures between probability distributions play significant roles. Hellinger coefficient, Jeffreys distance, Chernoff coefficient, directed divergence, and its symmetrization J -divergence are examples of such measures. Here these and like measures are characterized through a composition law and the sum form they possess. The functional equations f ( pr , qs ) + f ( ps , qr ) = ( r + s ) f ( p , q ) + ( p + q ) f ( r , s ) and f ( pr , qs ) + f ( ps , qr ) = f ( p , q ) f ( r , s ) are instrumental in their deduction.