Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

Decision boundary for discrete Bayesian network classifiers

Bayesian network classifiers are a powerful machine learning tool. In order to evaluate the expressive power of these models, we compute families of polynomials that sign-represent decision functions induced by Bayesian network classifiers. We prove that those families are linear combinations of products of Lagrange basis polynomials. In absence of V -structures in the predictor sub-graph, we are also able to prove that this family of polynomials does indeed characterize the specific classifier considered. We then use this representation to bound the number of decision functions representable by Bayesian network classifiers with a given structure.

Análise da aderência de práticas ágeis na cultura de startups de software: o mapeamento de práticas através do SEMAT Kernel.

Este trabalho analisa os principais métodos ágeis utilizados em empresas startup, como scrum, extreme programming, kanban e lean, isolando suas práticas e mapeando-as no Kernel do SEMAT para escolher os elementos essenciais da engenharia de software que estão relacionados a cada prática de forma independente. Foram identificadas 34 práticas que foram reduzidas a um conjunto de 26 pelas similaridades. Um questionário foi desenvolvido e aplicado no ambiente de startups de software para a avaliação do grau de utilização de cada determinada prática. Através das respostas obtidas foi possível a identificação de um subconjunto de práticas com utilização acima de 60% onde todos os elementos essenciais da engenharia de software são atendidos, formando um conjunto mínimo de práticas capazes de sustentar este tipo específico de ambiente.

An integral security kernel

As the user base of the Internet has grown tremendously, the need for secure services has increased accordingly. Most secure protocols, in digital business and other fields, use a combination of symmetric and asymmetric cryptography, random generators and hash functions in order to achieve confidentiality, integrity, and authentication. Our proposal is an integral security kernel based on a powerful mathematical scheme from which all of these cryptographic facilities can be derived. The kernel requires very little resources and has the flexibility of being able to trade off speed, memory or security; therefore, it can be efficiently implemented in a wide spectrum of platforms and applications, either software, hardware or low cost devices. Additionally, the primitives are comparable in security and speed to well known standards.

Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

KDENS: Stata module for univariate kernel density estimation

kdens produces univariate kernel density estimates and graphs the result. kdens supplements official Stata's kdensity. Important additions are: adaptive (i.e. variable bandwidth) kernel density estimation, several automatic bandwidth selectors including the Sheather-Jones plug-in estimator, pointwise variability bands and confidence intervals, boundary correction for variables with bounded domain, fast binned approximation estimation. Note that the moremata package, also available from SSC, is required.

A virtual address kernel modification for UNIX /

"February 1980."

Predictive vaccinology: Optimisation of predictions using support vector machine classifiers

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 super-type molecules with excellent accuracy, even for molecules where no binding data are currently available.

Palm kernel meal in broiler diets: effect on chicken performance and health

An increase in the production of palm kernel meal (PKM) coupled with the concern for continued availability of conventional feedstuffs in some parts of the world has led to research to establish the maximum inclusion level of palm kernel meal in broiler diets. The results suggested that palm kernel meal has no anti-nutritional properties and thus its inclusion is safe up to at least 40% in the diet, provided the diet is balanced in amino acids and metabolisable energy. Although feed digestibility is decreased due to high dietary fibre when PKM is included in the diet, the feed intake is increased. This makes total digestible nutrient intake relatively high. beta-mannan is the main component of palm kernel meal non-starch polysaccharide (NSP). Both mannose and manno-oligosaccharides have been reported to act as prebiotics. The inclusion of palm kernel meal in the diet improves the immune system of birds and reduces pathogenic bacteria and increases the population of nonpathogenic bacteria in the intestine. These two benefits should be considered as strong recommendations for using palm kernel meal in broiler diets, particularly in palm kernel meal producing countries, not only for increasing bird productivity but also to improve chicken health. Selective enzyme addition increases feed efficiency and digestibility as well as decreasing the moisture content of faeces.

Performance analysis using equivariant kernel density estimator in nonlinear mixture

This paper investigates the performance analysis of separation of mutually independent sources in nonlinear models. The nonlinear mapping constituted by an unsupervised linear mixture is followed by an unknown and invertible nonlinear distortion, are found in many signal processing cases. Generally, blind separation of sources from their nonlinear mixtures is rather difficult. We propose using a kernel density estimator incorporated with equivariant gradient analysis to separate the sources with nonlinear distortion. The kernel density estimator parameters of which are iteratively updated to minimize the output independence expressed as a mutual information criterion. The equivariant gradient algorithm has the form of nonlinear decorrelation to perform the convergence analysis. Experiments are proposed to illustrate these results.