Foreground Object Segmentation from Binocular Stereo Video

Moving cameras are needed for a wide range of applications in robotics, vehicle systems, surveillance, etc. However, many foreground object segmentation methods reported in the literature are unsuitable for such settings; these methods assume that the camera is fixed and the background changes slowly, and are inadequate for segmenting objects in video if there is significant motion of the camera or background. To address this shortcoming, a new method for segmenting foreground objects is proposed that utilizes binocular video. The method is demonstrated in the application of tracking and segmenting people in video who are approximately facing the binocular camera rig. Given a stereo image pair, the system first tries to find faces. Starting at each face, the region containing the person is grown by merging regions from an over-segmented color image. The disparity map is used to guide this merging process. The system has been implemented on a consumer-grade PC, and tested on video sequences of people indoors obtained from a moving camera rig. As can be expected, the proposed method works well in situations where other foreground-background segmentation methods typically fail. We believe that this superior performance is partly due to the use of object detection to guide region merging in disparity/color foreground segmentation, and partly due to the use of disparity information available with a binocular rig, in contrast with most previous methods that assumed monocular sequences

Foreground Object Segmentation from Binocular Stereo Video

Moving cameras are needed for a wide range of applications in robotics, vehicle systems, surveillance, etc. However, many foreground object segmentation methods reported in the literature are unsuitable for such settings; these methods assume that the camera is fixed and the background changes slowly, and are inadequate for segmenting objects in video if there is significant motion of the camera or background. To address this shortcoming, a new method for segmenting foreground objects is proposed that utilizes binocular video. The method is demonstrated in the application of tracking and segmenting people in video who are approximately facing the binocular camera rig. Given a stereo image pair, the system first tries to find faces. Starting at each face, the region containing the person is grown by merging regions from an over-segmented color image. The disparity map is used to guide this merging process. The system has been implemented on a consumer-grade PC, and tested on video sequences of people indoors obtained from a moving camera rig. As can be expected, the proposed method works well in situations where other foreground-background segmentation methods typically fail. We believe that this superior performance is partly due to the use of object detection to guide region merging in disparity/color foreground segmentation, and partly due to the use of disparity information available with a binocular rig, in contrast with most previous methods that assumed monocular sequences

A Case Study of Atmospheric Dynamics and Thermodynamics in Derechos and the Societal Impacts

The word “derecho” is used to differentiate a storm having straight‐line winds as opposed to rotational, tornadic winds. Although the term “derecho” is relatively old, derechos were not readily recognized by the general public until recent outbreaks caused significant widespread damage and associated fatalities. Most notably, the 2012 Mid‐Atlantic Derecho in the USA brought these types of storms to the public\u27s attention as a variety of societal impacts including infrastructural damage, power outages, and fatalities occurred over an extensive area from outside of Chicago to Washington, DC The associated damage can be more widespread than tornadoes, and the number of fatalities is comparable to those found in medium‐intensity tornadoes

The Impact of Oceanic Heat Content on the Rapid Intensification of Atlantic Hurricanes

With the increased infrastructure and amount of people living along the United States coastline, it is imperative to improve the accuracy of Atlantic hurricane intensity forecasts. Over the last 10 years, there have been many Atlantic hurricanes, including Hurricanes Katrina and Charley that surprised many forecasters with their rapid intensification and power. The rapid intensification of tropical cyclones is the most serious aspect, when it comes to forecasting. It is generally accepted that sufficient surface ocean temperatures (approximately 26°C) are needed to produce and sustain tropical cyclone formation. However, the sea-surface temperature (SST) has shown not to be critical in intensity forecasting by itself, particularly with rapid intensification (Schade & Emmanuel, 1999; Law & Hobgood 2007). Tropical cyclones derive much of their energy from warm, deep ocean water. Therefore, a quantified measure of the amount of this warm, deep water is a better way to measure the amount of energy available to the storm. The oceanic heat content (OHC) is such a variable to measure the amount of warm water available for the tropical cyclone to convert into energy and has been shown to be a much better predictor than SST alone (Zebiak, 1989; McDougall, 2003; Wada & Usui 2007; Palmer & Haines, 2009; Shay & Brewster, 2010)

A Statistical Model to Forecast Short-Term Atlantic Hurricane Intensity

An alternative 24-h statistical hurricane intensity model is presented and verified for 13 hurricanes during the 2004–05 seasons. The model uses a new method involving a discriminant function analysis (DFA) to select from a collection of multiple regression equations. These equations were developed to predict the future 24-h wind speed increase and the 24-h pressure drop that were constructed from a dataset of 103 hurricanes from 1988 to 2003 that utilized 25 predictors of rapid intensification. The accuracy of the 24-h wind speed increase models was tested and compared with the official National Hurricane Center (NHC) 24-h intensity forecasts, which are currently more accurate on average than other 24-h intensity models. Individual performances are shown for Hurricanes Charley (2004) and Katrina (2005) along with a summary of all 13 hurricanes in the study. The average error for the 24-h wind speed increase models was 11.83 kt (1 kt = 0.5144 m s-1) for the DFA-selected models and 12.53 kt for the official NHC forecast. When the DFA used the correctly selected model (CSM) for the same cases, the average error was 8.47 kt. For the 24-h pressure reduction models, the average error was 7.33 hPa for the DFA-selected models, and 5.85 hPa for the CSM. This shows that the DFA performed well against the NHC, but improvements can still be made to make the accuracy even better

The Weather and Climate of West Virginia

West Virginia is a geographically small state with a distinctive climate. The article describes the climatological patterns of the state plus describes some historical weather events

TransCom 3 experimental protocol

July 2000.Includes bibliographical references.Sponsored by NSF award OCE-9900310, and NOAA NA67RJ0152 Amend. 30

Overlapping Structures in Sensory-Motor Mappings

This paper examines a biologically-inspired representation technique designed for the support of sensory-motor learning in developmental robotics. An interesting feature of the many topographic neural sheets in the brain is that closely packed receptive fields must overlap in order to fully cover a spatial region. This raises interesting scientific questions with engineering implications: e.g. is overlap detrimental? does it have any benefits? This paper examines the effects and properties of overlap between elements arranged in arrays or maps. In particular we investigate how overlap affects the representation and transmission of spatial location information on and between topographic maps. Through a series of experiments we determine the conditions under which overlap offers advantages and identify useful ranges of overlap for building mappings in cognitive robotic systems. Our motivation is to understand the phenomena of overlap in order to provide guidance for application in sensory-motor learning robots

Empirical bounds for the ionizing fluxes of Wolf-Rayet stars.

Hα photometry and spectroscopic data were obtained for 10 Wolf-Rayet nebula, representing a wide variety of WN spectral types. The authors use these data to constrain the ionizing flux of the exciting Wolf-Rayet star, calcg. lower bounds for the Lyman continuum flux (Q0) and for the He0- and He+- ionizing fluxes (Q1 and Q2). Q0 appears independent of WN spectral type, and lower bound ests. tend to cluster around 48 dex. Finally, the authors discuss the effects of potential shock excitation and d. bounding on these nebula and compare the authors' results to recent models. The authors' results are consistent with the predictions of line-blanketed ISA-wind models and nonblanketed CMFGEN models but are consistent with only some of the line-blanketed CMFGEN models. [on SciFinder(R)

An Examination of the Impact of Gender and Culture on Facebook Privacy and Trust in Guam

Facebook, the world’s largest social network, allows users to develop a profile containing personal information. Users may choose privacy settings to control information access, but improper settings risk personal exposure. Several US studies examining gender differences in privacy management found that females have more privacy concerns. This study investigates gender differences in Facebook privacy settings among college students in Guam, a US territory whose ethnicity and culture differ from mainland US. Results show that neither gender trusts Facebook nor feels Facebook protects them. Significant differences in number and type of privacy settings indicate females are more security conscious. Gender differences exist in three of Hofstede’s five cultural factors, but only Masculinity-Femininity significantly influences perceptions of Facebook Privacy and Facebook Trust. There is also evidence of “privacy paradox” phenomenon. Summarizing for the case in Guam, the findings indicate that culture, in particular Masculinity-Femininity, influences the formation of perceptions regarding both trust in social media and the privacy protection provided by social media, while gender can influence the protective measures actually taken by individuals