Periodic clustered-dot screens are widely used for electrophotographic printers due to their print stability. However, moiré is a ubiquitous problem that arises in color printing due to the beating together of the clustered-dot, periodic halftone patterns that are used to represent different colorants. The traditional solution in the graphic arts and printing industry is to rotate identical square screens to angles that are maximally separated from each other. However, the effectiveness of this approach is limited when printing with more than four colorants, i.e., N -color printing, where N > 4 . Moreover, accurately achieving the angles that have maximum angular separation requires a very high-resolution plate writer, as is used in commercial offset printing. Commercially available high-end digital printers cannot achieve this resolution. In this paper, we propose a systematic way to design color screen sets for periodic, clustered-dot screens that offer more explicit control of the moiré properties of the resulting screens when used in color printing. We develop a principled approach for the moiré-free screen design that is called lattice-based screen design. The basic concept behind our approach is the creation of the screen set on a 2D lattice in the frequency domain, and then picking each fundamental frequency vector of the individual colorant planes in the created spectral lattice according to the desired properties. The lattice-based screen design offers more flexibility in designing N -color screen sets with different halftone geometries, and all of them are guaranteed to be all-orders moiré-free. We demonstrate the efficacy of our proposed method by introducing several new screen designs, and a comparison with published screen designs.

Print quality (PQ) is a composite attribute defined by human perception. As such, the ultimate way to determine and quantify PQ is by human survey. However, repeated surveys are time consuming and often represent a burden on processes that involve repeated evaluations. A desired alternative would be an automatic quality rating tool. Once such quality evaluation measure is proposed, it should be qualified. That is, it should be shown to reflect human assessment. If two of the human opinions conflict, the tool cannot possibly agree with both. Conflicts between human opinions are common, which complicates the evaluation of tool's success in reflecting human judgment. There are many optional ways for measuring the agreement between human assessment and tool evaluation, but different methods may have conflicting results. It is, therefore, important to pre-establish the appropriate method for the evaluation of quality-evaluation-tools, a method that takes the disagreement among the survey participants into account. In this paper, we model human quality preference and derive the most appropriate method to qualify quality evaluation tools. We demonstrate the resulting qualification method in a real life scenario-the qualification of the mechanical band meter.

We introduce a new similarity measure that is insensitive to sub-pixel misregistration. The proposed measure is essential in some differences detection scenarios. For example, in a setting where a digital reference is compared to an image, where the imaging process introduces deformations that appear as non constant misregistration between the two images. Our goal is to ignore image differences that result from misregistration and detect only the true, albeit minute, defects. In order to define a misregistration insensitive similarity, we argue that a similarity measure must respect convex combinations. We show that the well known SSIM does not hold this property and propose a modified version of SSIM that respects convex combinations. We then use this measure to define Sub-Pixel misregistration aware SSIM (SPSSIM).

In this work, we propose noise and signal activity estimation method that discriminates noise from signal based on local and global properties of the image data. The method yields pixel-wise maps of the noise variance and of the signal activity. Using these maps to guide imaging algorithms such as image enhancement and print defect detection improves their performance. The proposed method does not assume a white Gaussian noise model; it is very efficient computationally and, as such, is useful for a wide variety of applications.

We propose a measure for image sharpness, which facilitates automatic image sharpness enhancement. This way blurry images will be sharpened more whereas sufficiently sharp images will not be sharpened at all. The measure employs localized frequency content analysis in a feature-based context. The proposed sharpness measure correlates well with perceived sharpness, and is to a large degree invariant to image content. Furthermore, we show that the proposed measure can be used to drive an enhancement algorithm, which will sharpen an input image to a nominal measure. Last but not least, the proposed sharpness measure is computationally efficient, and requires fewer computations than a 3/spl times/3 convolution.

Gamut mapping deals with the need to adjust a color image to fit into the constrained color gamut of a given rendering medium. A typical use for this tool is the reproduction of a color image prior to its printing, such that it exploits best the given printer/medium color gamut, namely the colors the printer can produce on the given medium. Most of the classical gamut mapping methods involve a pixel-by-pixel mapping and ignore the spatial color configuration. Recently proposed spatial-dependent approaches for gamut mapping are either based on heuristic assumptions or involve a high computational cost. In this paper, we present a new variational approach for space-dependent gamut mapping. Our treatment starts with the presentation of a new measure for the problem, closely related to a recent measure proposed for Retinex. We also link our method to recent measures that attempt to couple spectral and spatial perceptual measures. It is shown that the gamut mapping problem leads to a quadratic programming formulation, guaranteed to have a unique solution if the gamut of the target device is convex. An efficient numerical solution is proposed with promising results.

We present an authenticate graphical barcode symbology, and describe a system for its reliable encoding and decoding is presented. The graphical barcode is generated by modulating halftone patterns according to the message to be embedded in the barcode during the rendering of a base image. The generated barcode has the appearance of the base image, and when it is printed and scanned back in, the message can be retrieved. A graphical indicium is a graphical barcode embedded with a digitally signed message such that the integrity and authenticity of the message can be verified.

Evolutions of closed planar polygons are studied in this work. In the first part of the paper, the general theory of linear polygon evolutions is presented, and two specific problems are analyzed. The first one is a polygonal analog of a novel affine-invariant differential curve evolution, for which the convergence of planar curves to ellipses was proved. In the polygon case, convergence to polygonal approximation of ellipses, polygo nal ellipses, is proven. The second one is related to cyclic pursuit problems, and convergence, either to polygonal ellipses or to polygonal circles, is proven. In the second part, two possible polygonal analogues of the well-known Euclidean curve shortening flow are presented. The models follow from geometric considerations. Experimental results show that an arbitrary initial polygon converges to either regular or irregular polygonal approximations of circles when evolving according to the proposed Euclidean flows.

In probabilistic Hough transforms computation is accelerated by polling instead of voting. A small part of the data set is selected at random and used as input to the algorithm. Most probabilistic Hough algorithms use a fixed poll size. It has been experimentally demonstrated that adaptive termination of voting can lead to improved performance in terms of the error rate versus average poll size tradeoff. However, the lack of a solid theoretical foundation made general performance evaluation and optimal design of adaptive stopping rules nearly impossible. In this paper we suggest two novel adaptive stopping rules in the framework of the statistical theory of sequential hypothesis testing. The performance of the suggested stopping rules is verified using real images. It is shown that the extension suggested in this paper to Wald's one sided alternative sequential test (1947) performs better than previously available adaptive (or fixed) stopping rules.