tomasi @ cs.stanford.edu Bilateral filtering smooths images while preserving edges, by means of a nonlinear combination of nearby image values. The method is noniterative, local, and sim-ple. It combines gray levels or colors based on both their geometric closeness and their photometric similariv

OptiMUSIG: An optimized gray level image segmentor

of embedding a gray-level digital watermark into an image is proposed. A gray-level digital watermark is decomposed into a series of binary digital images for implementing multiple watermark. This watermark is self-extractable. 1.

This paper proposes a new method for reduction of the number of gray-levels

This paper summarizes three algorithms used for the analysis of gray level histograms. Two of them are easily developed from standard techniques and the other is a completely new method. We will study the advantages of each and give examples of real-world use. Gray level histogram analysis (mainly

A variational approach for filling-in regions of missing data in digital images is introduced in this paper. The approach is based on joint interpolation of the image gray-levels and gradient/isophotes directions, smoothly extending in an automatic fashion the isophote lines into the holes

. In this paper, a new visual cryptography scheme suitable for gray-level images is proposed. Instead of using gray subpixels directly to construct shares, a dithering technique is used first to convert a gray-level image into an approximate binary image. Then existing visual cryptography schemes for binary

1•~~~ ~ Various methods have been proposed for improvingthe gray level histogram of an image so as to make it easier to select thresholds for segmenting the image. u_i This note describes how (gray level, local average gray level) feature space can be used to define im-proved image histograms

AbstractÐWe describe a new method of matching statistical models of appearance to images. A set of model parameters control modes of shape and gray-level variation learned from a training set. We construct an efficient iterative matching algorithm by learning the relationship between perturbations

For some gray-level images, the boundary between the foreground and the background is perceived in correspondence with the locally maximal changes in gray-level through the image. In this framework, this paper proposes a method to extract the objects of interest from an image and, hence