Relations Between Regularization and Diffusion Filtering

Regularization may be regarded as diffusion filtering with an implicit time discretization where one single step is used. Thus, iterated regularization with small regularization parameters approximates a diffusion process. The goal of this paper is to analyse relations between noniterated and iterated regularization and diffusion filtering in image processing. In the linear regularization framework, we show that with iterated Tikhonov regularization noise can be better handled than with noniterated. In the nonlinear framework, two filtering strategies are considered: the total variation regularization technique and the diffusion filter technique of Perona and Malik. It is shown that the Perona-Malik equation decreases the total variation during its evolution. While noniterated and iterated total variation regularization is well-posed, one cannot expect to find a minimizing sequence which converges to a minimizer of the corresponding energy functional for the Perona–Malik filter. To overcome this shortcoming, a novel regularization technique of the Perona–Malik process is presented that allows to construct a weakly lower semi-continuous energy functional. In analogy to recently derived results for a well-posed class of regularized Perona–Malik filters, we introduce Lyapunov functionals and convergence results for regularization methods. Experiments on real-world images illustrate that iterated linear regularization performs better than noniterated, while no significant differences between noniterated and iterated total variation regularization have been observed.     

Group awareness and self-presentation in computer-supported information exchange

A common challenge in many situations of computer-supported collaborative learning is increasing the willingness of those involved to share their knowledge with other group members. As a prototypical situation of computer-supported information exchange, a shared-database setting was chosen for the current study. This information-exchange situation represented a social dilemma: while the contribution of information to a shared database induced costs and provided no benefit for the individual, the entire group suffered when all members decided to withhold information. In order to alleviate the information-exchange dilemma, a group-awareness tool was employed. It was hypothesized that participants would use group awareness for self-presentational purposes. For the examination of this assumption, the personality variable ‘protective self-presentation’ (PSP) was measured. An interaction effect of group awareness and PSP was found: when an awareness tool provided information concerning the contribution behavior of each individual, this tool was used as a self-presentation opportunity. In order to understand this effect in more detail, single items of the PSP-scale were analyzed.     

Real-time scheduling for energy harvesting sensor nodes

Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First scheduling.

Artists portray human faces with the Fourier statistics of complex natural scenes

When artists portray human faces, they generally endow their portraits with properties that render the faces esthetically more pleasing. To obtain insight into the changes introduced by artists, we compared Fourier power spectra in photographs of faces and in portraits by artists. Our analysis was restricted to a large set of monochrome or lightly colored portraits from various Western cultures and revealed a paradoxical result. Although face photographs are not scale-invariant, artists draw human faces with statistical properties that deviate from the face photographs and approximate the scale-invariant, fractal-like properties of complex natural scenes. This result cannot be explained by systematic differences in the complexity of patterns surrounding the faces or by reproduction artifacts. In particular, a moderate change in gamma gradation has little influence on the results. Moreover, the scale-invariant rendering of faces in artists' portraits was found to be independent of cultural variables, such as century of origin or artistic techniques. We suggest that artists have implicit knowledge of image statistics and prefer natural scene statistics (or some other rules associated with them) in their creations. Fractal-like statistics have been demonstrated previously in other forms of visual art and may be a general attribute of esthetic visual stimuli.     

Conjoint analysis to measure the perceived quality in volume rendering

Visualization algorithms can have a large number of parameters, making the space of possible rendering results rather high-dimensional. Only a systematic analysis of the perceived quality can truly reveal the optimal setting for each such parameter. However, an exhaustive search in which all possible parameter permutations are presented to each user within a study group would be infeasible to conduct. Additional complications may result from possible parameter co-dependencies. Here, we will introduce an efficient user study design and analysis strategy that is geared to cope with this problem. The user feedback is fast and easy to obtain and does not require exhaustive parameter testing. To enable such a framework we have modified a preference measuring methodology, conjoint analysis, that originated in psychology and is now also widely used in market research. We demonstrate our framework by a study that measures the perceived quality in volume rendering within the context of large parameter spaces.     

Proteomics in ocular fluids

The focus of this article is to review recent techniques in proteomic analysis of ocular fluids. These fluids include tears, aqueous humor, and vitreous, they will also be compared to serum analysis. Furthermore, we attempt to summarize some disease correlated biomarkers in ocular fluids that were discovered through different proteomic techniques in eye diseases like dry eye, glaucoma, age-related macular degeneration, uveitis, or diabetic retinopathy. This review is trying to point out the importance of these biomarkers for clinical applications.     

Detection of colorectal adenoma and cancer based on transthyretin and C3a-desArg serum levels

Colorectal cancer is the second leading cause of cancer death, and it develops from benign colorectal adenomas in over 95% of patients. Early detection of these cancer precursors by screening tests and their removal can potentially eradicate more than 95% of colorectal cancers before they develop. To discover sensitive and specific biomarkers for improvement of pre‐clinical diagnosis of colorectal adenoma and cancer, we analysed in two independent studies (n = 87 and n = 83 patients) serum samples from colorectal cancer (stage III), colorectal adenoma and control patients using SELDI‐TOF‐MS. Extensive statistical analysis was performed to establish homogeneous patient groups based on their clinical data. Two biomarkers that were each able to distinguish control patients from either colorectal adenoma or colorectal cancer patients (p<0.001) were identified as transthyretin (pre‐albumin) and C3a‐desArg by MS/MS and were further validated by antibody‐based assays (radial immunodiffusion, ELISA). A combination of both proteins clearly indicated the presence of colorectal adenoma or carcinoma. Using a cut‐off of <0.225 g/L for transthyretin and >1974 ng/mL for C3a‐desArg, we found a sensitivity and specificity for colorectal adenoma of 96% and 70%, respectively.     

Subacute proteome changes following traumatic injury of the developing brain: Implications for a dysregulation of neuronal migration and neurite arborization

Traumatic brain injury (TBI) is a major cause of morbidity and mortality among children and adolescents. To gain insight into developmental events influenced by TBI, we analyzed subacute mouse brain proteome changes in a percussion head trauma model at P7 ipsi- and contralateral to the site of injury. The comparison of brain proteomes of trauma mice and controls revealed reproducible changes in the intensity of 28 proteins (30 protein spots) in response to trauma. The changes detected suggest that TBI leads to apoptosis, inflammation, and oxidative stress. These changes were consistent with our results of histological and biochemical evaluation of the brains which revealed widespread apoptotic neurodegeneration, microglia activation, and increased levels of protein carbonyls. Furthermore, we detected changes in proteins involved in neuronal migration as well as axonal and dendritic growth and guidance, suggesting interference of trauma with these developmental events.     

Dense versus Sparse Approaches for Estimating the Fundamental Matrix

There are two main strategies for solving correspondence problems in computer vision: sparse local feature based approaches and dense global energy based methods. While sparse feature based methods are often used for estimating the fundamental matrix by matching a small set of sophistically optimised interest points, dense energy based methods mark the state of the art in optical flow computation. The goal of our paper is to show that this separation into different application domains is unnecessary and can be bridged in a natural way. As a first contribution we present a new application of dense optical flow for estimating the fundamental matrix. Comparing our results with those obtained by feature based techniques we identify cases in which dense methods have advantages over sparse approaches. Motivated by these promising results we propose, as a second contribution, a new variational model that recovers the fundamental matrix and the optical flow simultaneously as the minimisers of a single energy functional. In experiments we show that our coupled approach is able to further improve the estimates of both the fundamental matrix and the optical flow. Our results prove that dense variational methods can be a serious alternative even in classical application domains of sparse feature based approaches.