Versatile Bayesian classifier for moving object detection by non-parametric background-foreground modeling

Along the recent years, several moving object detection strategies by non-parametric background-foreground modeling have been proposed. To combine both models and to obtain the probability of a pixel to belong to the foreground, these strategies make use of Bayesian classifiers. However, these classifiers do not allow to take advantage of additional prior information at different pixels. So, we propose a novel and efficient alternative Bayesian classifier that is suitable for this kind of strategies and that allows the use of whatever prior information. Additionally, we present an effective method to dynamically estimate prior probability from the result of a particle filter-based tracking strategy.

Background noise and its influence on the brain waves related to attention

In this work, we present a study whose objective is to prove the influence of background noise produced inside university facilities on the brain waves related to attention processes. Recordings of background noise were carried out in study areas inside university facilities. Volunteers were asked to perform an attention test without any background noise but also while being exposed to the sound recordings, and their cerebral activity was recorded through electroencephalography (EEG). After the application of the test in both conditions, changes in the frequency bands related to attention processes (beta 13-30 Hz and theta 4-7 Hz) were studied. The results of this study show that when the students were performing the test while being exposed to background noise, both beta and theta frequency bands decreased statistically significantly. Because attentional improvement is related to increases of the beta and theta waves, we believe that those decreases are directly related to a lack of attention caused by the exposure to background noise. Nevertheless, the results do not allow us to conclude that background noise produced inside university facilities has an influence on the attentional processes.

Background and approach to a definition of smart buildings

There is no doubt that there is no possibility of finding a single reference about domotics in the first half of the 20th century. The best known authors and those who have documented this discipline, set its origin in the 1970’s, when the x-10 technology began to be used, but it was not until 1988 when Larousse Encyclopedia decided to include the definition of "Smart Building". Furthermore, even nowadays, there is not a single definition widely accepted, and for that reason, many other expressions, namely "Intelligent Buildings" "Domotics" "Digital Home" or "Home Automation" have appeared to describe the automated buildings and homes. The lack of a clear definition for "Smart Buildings" causes difficulty not only in the development of a common international framework to develop research in this field, but it also causes insecurity in the potential user of these buildings. That is to say, the user does not know what is offered by this kind of buildings, hindering the dissemination of the culture of building automation in society. Thus, the main purpose of this paper is to propose a definition of the expression “Smart Buildings” that satisfactorily describes the meaning of this discipline. To achieve this aim, a thorough review of the origin of the term itself and the historical background before the emergence of the phenomenon of domotics was conducted, followed by a critical discussion of existing definitions of the term "Smart Buildings" and other similar terms. The extent of each definition has been analyzed, inaccuracies have been discarded and commonalities have been compared. Throughout the discussion, definitions that bring the term "Smart Buildings" near to disciplines such as computer science, robotics and also telecommunications have been found. However, there are also many other definitions that emphasize in a more abstract way the role of these new buildings in the society and the future of mankind.

The effects of cocaine self-administration on dendritic spine density in the rat hippocampus are dependent on genetic background

Chronic exposure to cocaine induces modifications to neurons in the brain regions involved in addiction. Hence, we evaluated cocaine-induced changes in the hippocampal CA1 field in Fischer 344 (F344) and Lewis (LEW) rats, 2 strains that have been widely used to study genetic predisposition to drug addiction, by combining intracellular Lucifer yellow injection with confocal microscopy reconstruction of labeled neurons. Specifically, we examined the effects of cocaine self-administration on the structure, size, and branching complexity of the apical dendrites of CA1 pyramidal neurons. In addition, we quantified spine density in the collaterals of the apical dendritic arbors of these neurons. We found differences between these strains in several morphological parameters. For example, CA1 apical dendrites were more branched and complex in LEW than in F344 rats, while the spine density in the collateral dendrites of the apical dendritic arbors was greater in F344 rats. Interestingly, cocaine self-administration in LEW rats augmented the spine density, an effect that was not observed in the F344 strain. These results reveal significant structural differences in CA1 pyramidal cells between these strains and indicate that cocaine self-administration has a distinct effect on neuron morphology in the hippocampus of rats with different genetic backgrounds.

Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.

Multi-sensor background subtraction by fusing multiple region-based probabilistic classifiers

In the recent years, the computer vision community has shown great interest on depth-based applications thanks to the performance and flexibility of the new generation of RGB-D imagery. In this paper, we present an efficient background subtraction algorithm based on the fusion of multiple region-based classifiers that processes depth and color data provided by RGB-D cameras. Foreground objects are detected by combining a region-based foreground prediction (based on depth data) with different background models (based on a Mixture of Gaussian algorithm) providing color and depth descriptions of the scene at pixel and region level. The information given by these modules is fused in a mixture of experts fashion to improve the foreground detection accuracy. The main contributions of the paper are the region-based models of both background and foreground, built from the depth and color data. The obtained results using different database sequences demonstrate that the proposed approach leads to a higher detection accuracy with respect to existing state-of-the-art techniques.

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.

Human Search for a Target on a Textured Background is Consistent with a Stochastic Model

This research was supported by the James S. McDonnell Foundation (ARH). Early version was supported by EPSRC grants EP/F02553X/1 and EP/D059364/1.

Effect of reduced myristoylated alanine-rich C kinase substrate expression on hippocampal mossy fiber development and spatial learning in mutant mice: Transgenic rescue and interactions with gene background

The myristoylated alanine-rich C kinase substrate (MARCKS) is a prominent protein kinase C (PKC) substrate in brain that is expressed highly in hippocampal granule cells and their axons, the mossy fibers. Here, we examined hippocampal infrapyramidal mossy fiber (IP-MF) limb length and spatial learning in heterozygous Macs mutant mice that exhibit an ≈50% reduction in MARCKS expression relative to wild-type controls. On a 129B6(N3) background, the Macs mutation produced IP-MF hyperplasia, a significant increase in hippocampal PKCɛ expression, and proficient spatial learning relative to wild-type controls. However, wild-type 129B6(N3) mice exhibited phenotypic characteristics resembling inbred 129Sv mice, including IP-MF hypoplasia relative to inbred C57BL/6J mice and impaired spatial-reversal learning, suggesting a significant contribution of 129Sv background genes to wild-type and possibly mutant phenotypes. Indeed, when these mice were backcrossed with inbred C57BL/6J mice for nine generations to reduce 129Sv background genes, the Macs mutation did not effect IP-MF length or hippocampal PKCɛ expression and impaired spatial learning relative to wild-type controls, which now showed proficient spatial learning. Moreover, in a different strain (B6SJL(N1), the Macs mutation also produced a significant impairment in spatial learning that was reversed by transgenic expression of MARCKS. Collectively, these data indicate that the heterozygous Macs mutation modifies the expression of linked 129Sv gene(s), affecting hippocampal mossy fiber development and spatial learning performance, and that MARCKS plays a significant role in spatial learning processes.

Effect of genetic background on the contribution of New Zealand Black loci to autoimmune lupus nephritis

Autoimmune diseases such as systemic lupus erythematosus are complex genetic traits with contributions from major histocompatibility complex (MHC) genes and multiple unknown non-MHC genes. Studies of animal models of lupus have provided important insight into the immunopathogenesis of disease, and genetic analyses of these models overcome certain obstacles encountered when studying human patients. Genome-wide scans of different genetic crosses have been used to map several disease-linked loci in New Zealand hybrid mice. Although some consensus exists among studies mapping the New Zealand Black (NZB) and New Zealand White (NZW) loci that contribute to lupus-like disease, considerable variability is also apparent. A variable in these studies is the genetic background of the non-autoimmune strain, which could influence genetic contributions from the affected strain. A direct examination of this question was undertaken in the present study by mapping NZB nephritis-linked loci in backcrosses involving different non-autoimmune backgrounds. In a backcross with MHC-congenic C57BL/6J mice, H2z appeared to be the strongest genetic determinant of severe lupus nephritis, whereas in a backcross with congenic BALB/cJ mice, H2z showed no influence on disease expression. NZB loci on chromosomes 1, 4, 11, and 14 appeared to segregate with disease in the BALB/cJ cross, but only the influence of the chromosome 1 locus spanned both crosses and showed linkage with disease when all mice were considered. Thus, the results indicate that contributions from disease-susceptibility loci, including MHC, may vary markedly depending on the non-autoimmune strain used in a backcross analysis. These studies provide insight into variables that affect genetic heterogeneity and add an important dimension of complexity for linkage analyses of human autoimmune disease.

A resistant genetic background leading to incomplete penetrance of intestinal neoplasia and reduced loss of heterozygosity in ApcMin/+ mice

Previous studies of Min/+ (multiple intestinal neoplasia) mice on a sensitive genetic background, C57BL/6 (B6), showed that adenomas have lost heterozygosity for the germ-line ApcMin mutation in the Apc (adenomatous polyposis coli) gene. We now report that on a strongly resistant genetic background, AKR/J (AKR), Min-induced adenoma multiplicity is reduced by about two orders of magnitude compared with that observed on the B6 background. Somatic treatment with a strong mutagen increases tumor number in AKR Min/+ mice in an age-dependent manner, similar to results previously reported for B6 Min/+ mice. Immunohistochemical analyses indicate that Apc expression is suppressed in all intestinal tumors from both untreated and treated AKR Min/+ mice. However, the mechanism of Apc inactivation in AKR Min/+ mice often differs from that observed for B6 Min/+ mice. Although loss of heterozygosity is observed in some tumors, a significant percentage of tumors showed neither loss of heterozygosity nor Apc truncation mutations. These results extend our understanding of the effects of genetic background on Min-induced tumorigenesis in several ways. First, the AKR strain carries modifiers of Min in addition to Mom1. This combination of AKR modifiers can almost completely suppress spontaneous intestinal tumorigenesis associated with the Min mutation. Second, even on such a highly resistant genetic background, tumor formation continues to involve an absence of Apc function. The means by which Apc function is inactivated is affected by genetic background. Possible scenarios are discussed.

Manufacturing DNA microarrays of high spot homogeneity and reduced background signal

Analyses on DNA microarrays depend considerably on spot quality and a low background signal of the glass support. By using betaine as an additive to a spotting solution made of saline sodium citrate, both the binding efficiency of spotted PCR products and the homogeneity of the DNA spots is improved significantly on aminated surfaces such as glass slides coated with the widely used poly-l-lysine or aminosilane. In addition, non-specific background signal is markedly diminished. Concomitantly, during the arraying procedure, the betaine reduces evaporation from the microtitre dish wells, which hold the PCR products. Subsequent blocking of the chip surface with succinic anhydride was improved considerably in the presence of the non-polar, non-aqueous solvent 1,2-dichloroethane and the acylating catalyst N-methylimidazole. This procedure prevents the overall background signal that occurs with the frequently applied aqueous solvent 1-methyl-2-pyrrolidone in borate buffer because of DNA that re-dissolves from spots during the blocking process, only to bind again across the entire glass surface.

The microwave background anisotropies: Observations

Most cosmologists now believe that we live in an evolving universe that has been expanding and cooling since its origin about 15 billion years ago. Strong evidence for this standard cosmological model comes from studies of the cosmic microwave background radiation (CMBR), the remnant heat from the initial fireball. The CMBR spectrum is blackbody, as predicted from the hot Big Bang model before the discovery of the remnant radiation in 1964. In 1992 the cosmic background explorer (COBE) satellite finally detected the anisotropy of the radiation—fingerprints left by tiny temperature fluctuations in the initial bang. Careful design of the COBE satellite, and a bit of luck, allowed the 30 μK fluctuations in the CMBR temperature (2.73 K) to be pulled out of instrument noise and spurious foreground emissions. Further advances in detector technology and experiment design are allowing current CMBR experiments to search for predicted features in the anisotropy power spectrum at angular scales of 1° and smaller. If they exist, these features were formed at an important epoch in the evolution of the universe—the decoupling of matter and radiation at a temperature of about 4,000 K and a time about 300,000 years after the bang. CMBR anisotropy measurements probe directly some detailed physics of the early universe. Also, parameters of the cosmological model can be measured because the anisotropy power spectrum depends on constituent densities and the horizon scale at a known cosmological epoch. As sophisticated experiments on the ground and on balloons pursue these measurements, two CMBR anisotropy satellite missions are being prepared for launch early in the next century.

Cosmic microwave background theory

A long-standing goal of theorists has been to constrain cosmological parameters that define the structure formation theory from cosmic microwave background (CMB) anisotropy experiments and large-scale structure (LSS) observations. The status and future promise of this enterprise is described. Current band-powers in ℓ-space are consistent with a ΔT flat in frequency and broadly follow inflation-based expectations. That the levels are ∼(10−5)2 provides strong support for the gravitational instability theory, while the Far Infrared Absolute Spectrophotometer (FIRAS) constraints on energy injection rule out cosmic explosions as a dominant source of LSS. Band-powers at ℓ ≳ 100 suggest that the universe could not have re-ionized too early. To get the LSS of Cosmic Background Explorer (COBE)-normalized fluctuations right provides encouraging support that the initial fluctuation spectrum was not far off the scale invariant form that inflation models prefer: e.g., for tilted Λ cold dark matter sequences of fixed 13-Gyr age (with the Hubble constant H0 marginalized), ns = 1.17 ± 0.3 for Differential Microwave Radiometer (DMR) only; 1.15 ± 0.08 for DMR plus the SK95 experiment; 1.00 ± 0.04 for DMR plus all smaller angle experiments; 1.00 ± 0.05 when LSS constraints are included as well. The CMB alone currently gives weak constraints on Λ and moderate constraints on Ωtot, but theoretical forecasts of future long duration balloon and satellite experiments are shown which predict percent-level accuracy among a large fraction of the 10+ parameters characterizing the cosmic structure formation theory, at least if it is an inflation variant.

Extremely sensitive, background-free gene detection using binary probes and beta replicase.

We have developed a specific and sensitive nucleic acid amplification assay that is suitable for routine gene detection. The assay is based on a novel molecular genetic strategy in which two different RNA probes are hybridized to adjacent positions on a target nucleic acid and then ligated to form an amplifiable reporter RNA. The reporter RNA is then replicated up to a hundred billion-fold in a 30-min isothermal reaction that signals the presence of the target. The assay can detect fewer than 100 nucleic acid molecules; it provides quantitative results over a wide range of target concentrations and it employs a universal format that can detect any infectious agent.