The paper describes the adaptive prediction algorithm for differential methods of image compression. This prediction algorithm can automatically use a variety of calculation ways, depending on the smoothness of local image area. The parameters of proposed prediction algorithm are adjusted automatically by simple recursive scheme before the actual processing. An experimental research of the prop...

Shape From Focus (SFF) methods frequently use a single focus measure to obtain a depth map. Common focus measures are fixed and spatially invariant. In this paper we present a framework to create an adaptive focus measure based on ensemble of basis focus operators. Using the proposed framework we derive a new spatially variant focus measure obtained from linear combination of image derivatives....

The properties of the space A of regular connections as a subset of the space A of generalized connections in the Ashtekar framework are studied. For every choice of compact structure group and smoothness category for the paths it is determined whether A is dense in A or not. Moreover, it is proven that A has Ashtekar-Lewandowski measure zero for every nontrivial structure group and every smoot...

The classical Morse-Sard Theorem says that the set of critical values of f : R → R has Lebesgue measure zero if f ∈ Ck+1. We show the Ck+1 smoothness requirement can be weakened to Ck+Zygmund. This is corollary to the following theorem: For integers n > m > r > 0, let s = (n− r)/(m− r); if f : R → R belongs to the Lipschitz class Λs and E is a set of rank r for f , then f(E) has measure zero. 0...

Regularization o ers a powerful framework for signal recon struction by enforcing weak constraints through the use of stabilizers Stabilizers are functionals measuring the degree of smoothness of a sur face The nature of those functionals constrains the properties of the reconstructed signal In this paper we rst analyze the invariance of stabilizers with respect to size transformation and their...

Smoothness is a quality that feels aesthetic and pleasing to the human eye. We present an algorithm for finding “as-smoothas-possible” sequences in image collections. In contrast to previous work, our method does not assume that the images show a common 3D scene, but instead may depict different object instances with varying deformations, and significant variation in lighting, texture, and colo...

aries as a model-based segmentation procedure. This enSegmentation of (noisy) images containing a complex ensemhances the quality of segmentation, as not only local image ble of objects is difficult to achieve on the basis of local image information is used, but also the global shape of the object information only. It is advantageous to attack the problem as captured in the model. This can be u...

The aim of this paper is to show how image points can be extracted accurately. We will restrict our search to speciic points identiied by corners, which are stable given a sequence. Our approach makes use of a model{based corner detector. It matches a part of the image containing a corner against a predeened corner model. Once the tting is accomplished, the position of the corner in the image c...

We present a generative, probabilistic model that decomposes an image into reflectance and shading components. The proposed approach uses a Dirichlet process Gaussian mixture model where the mean parameters evolve jointly according to a Gaussian process. In contrast to prior methods, we eliminate the Retinex term and adopt more general smoothness assumptions for the shading image. Markov chain ...

This paper examines how commonly used earnings quality measures fulfill a key objective of financial reporting, i.e., improving decision usefulness for investors. We propose a stock-price-based measure for assessing the quality of earnings quality measures. We predict that firms with higher earnings quality will be less mispriced than other firms. Mispricing is measured by the difference of the...