Optimal visual motion estimation: a note

The problem of estimating 3D motion in an optimal manner using correspondences of features in two views is analyzed. The importance of having an optimal estimator is twofold: first, for the estimation itself and, second, for the bound it offers on how much sensitivity one can expect from a two-frame, point-based motion algorithm. The optimal estimator turns out to be nonlinear, and for that reason, techniques that provide very good initial guesses for the iterative computation of the optimal estimator are developed     

Visual shape computation

Perceptual processes responsible for computing shape from several cues, including shading, texture, contour, and stereo, are examined. It is noted that these computational problems, as well as that of computing shaping from motion, are ill-posed in the sense of Hadamard. It is suggested that regularization theory can be used along with a priori knowledge to restrict the space of possible solutions, and thus restore the problem's well-prosedness. Some alternative methods are outlined, and the idea of active vision is explored briefly in connection with the problem     

Predictors and correlates of taste preferences in European children: The IDEFICS study

The present study investigated taste preferences in a sample of 1705 children aged 6 to 9 years from survey centres in Italy, Estonia, Cyprus, Belgium, Sweden, Germany, Hungary, and Spain and aimed to identify factors correlated with taste preference. Children’s preferences for varying levels of sucrose (sweet) in apple juice and fat, sodium chloride (salt) and monosodium glutamate (umami) in crackers were assessed using paired-comparison tests. Socio-demographics (age, sex, parental education), early feeding practises (breastfeeding, introduction of fruits), parenting behaviour (TV viewing, using food as a reward) and taste threshold sensitivity for sucrose (sweet), sodium chloride (salt), caffeine (bitter) and monosodium glutamate (umami) were investigated as possible correlates of taste preferences. Parents reported on socio-demographics, early feeding and parenting behaviour. Taste thresholds were determined via a paired-comparison staircase method. Country of residence was the strongest factor related to preferences for all four tastes. Taste preferences also differed by age. Preference for sugar and salt increased between 6 and 9 years of age while preference for monosodium glutamate decreased. The age differences remained significant even after adjustment for sex, country of residence, parental education and early feeding habits. Sex, parental education, early feeding habits, TV viewing, using food as a reward and taste thresholds were not consistently related to taste preferences among the survey centres. In summary, the results highlight the importance of culture and age in taste preferences in children younger than 10 years of age.     

Free and immobilized Lactobacillus casei ATCC 393 on whey protein as starter cultures for probiotic Feta-type cheese production

The use of free and immobilized Lactobacillus casei ATCC 393 on whey protein as starter culture in probiotic Feta-type cheese production was evaluated. The probiotic cultures resulted in significantly higher acidity; lower pH; reduced counts of coliforms, enterobacteria, and staphylococci; and improved quality characteristics compared with cheese with no culture. Microbiological and strain-specific multiplex PCR analysis showed that both free and immobilized L. casei ATCC 393 were detected in the novel products at levels required for conferring a probiotic effect at the end of the ripening. The effect of starter culture on production of volatile compounds was investigated by the solid-phase microextraction gas chromatography-mass spectrometry analysis technique. The immobilized cells resulted in an improved profile of aroma-related compounds and the overall high quality of the novel products was ascertained by the preliminary sensory test. Finally, the high added value produced by exploitation of whey, which is an extremely polluting industrial waste, was highlighted and assessed.     

Structure from Motion: Beyond the Epipolar Constraint

The classic approach to structure from motion entails a clear separation between motion estimation and structure estimation and between two-dimensional (2D) and three-dimensional (3D) information. For the recovery of the rigid transformation between different views only 2D image measurements are used. To have available enough information, most existing techniques are based on the intermediate computation of optical flow which, however, poses a problem at the locations of depth discontinuities. If we knew where depth discontinuities were, we could (using a multitude of approaches based on smoothness constraints) accurately estimate flow values for image patches corresponding to smooth scene patches; but to know the discontinuities requires solving the structure from motion problem first. This paper introduces a novel approach to structure from motion which addresses the processes of smoothing, 3D motion and structure estimation in a synergistic manner. It provides an algorithm for estimating the transformation between two views obtained by either a calibrated or uncalibrated camera. The results of the estimation are then utilized to perform a reconstruction of the scene from a short sequence of images.The technique is based on constraints on image derivatives which involve the 3D motion and shape of the scene, leading to a geometric and statistical estimation problem. The interaction between 3D motion and shape allows us to estimate the 3D motion while at the same time segmenting the scene. If we use a wrong 3D motion estimate to compute depth, we obtain a distorted version of the depth function. The distortion, however, is such that the worse the motion estimate, the more likely we are to obtain depth estimates that vary locally more than the correct ones. Since local variability of depth is due either to the existence of a discontinuity or to a wrong 3D motion estimate, being able to differentiate between these two cases provides the correct motion, which yields the least varying estimated depth as well as the image locations of scene discontinuities. We analyze the new constraints, show their relationship to the minimization of the epipolar constraint, and present experimental results using real image sequences that indicate the robustness of the method.     

A sparse nonparametric hierarchical Bayesian approach towards inductive transfer for preference modeling

In this paper, we present a novel methodology for preference learning based on the concept of inductive transfer. Specifically, we introduce a nonparametric hierarchical Bayesian multitask learning approach, based on the notion that human subjects may cluster together forming groups of individuals with similar preference rationale (but not identical preferences). Our approach is facilitated by the utilization of a Dirichlet process prior, which allows for the automatic inference of the most appropriate number of subject groups (clusters), as well as the employment of the automatic relevance determination (ARD) mechanism, giving rise to a sparse nature for our model, which significantly enhances its computational efficiency. We explore the efficacy of our novel approach by applying it to both a synthetic experiment and a real-world music recommendation application. As we show, our approach offers a significant enhancement in the effectiveness of knowledge transfer in statistical preference learning applications, being capable of correctly inferring the actual number of human subject groups in a modeled dataset, and limiting knowledge transfer only to subjects belonging to the same group (wherein knowledge transferability is more likely).     

Community detection in Social Media

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services.