People-background segmentation with unequal error cost

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Á. García-Martín, A. Cavallaro, J. M. Martínez, "People-background segmentation with unequal error cost", in 19th IEEE International Conference on Image Processing, ICIP 2012, p. 157 - 160We address the problem of segmenting a video in two classes of different semantic value, namely background and people, with the goal of guaranteeing that no people (or body parts) are classified as background. Body parts classified as background are given a higher classification error cost (segmentation with bias on background), as opposed to traditional approaches focused on people detection. To generate the people-background segmentation mask, the proposed approach first combines detection confidence maps of body parts and then extends them in order to derive a background mask, which is finally post-processed using morphological operators. Experiments validate the performance of our algorithm in different complex indoor and outdoor scenes with both static and moving cameras.Work partially supported by the Universidad Autónoma de Madrid (“FPI-UAM”) and by the Spanish Goverment (“TEC2011-25995 EventVideo”). This work was done while the first author was visting Queen Mary University of London

Inequality, Credit Market Imperfection, Segmentation and Economic Growth

This paper investigates how initial inequality can causally affect economic growth when moral hazard problems exist in credit markets.Two regimes of the credit markets aiming at overcoming the moral hazard problems are analyzed.The formal one such as bank relies on intermediary between borrowers and lenders by asking for collateral.The informal one relies on direct yet costly monitoring by the lenders themselves.However, from the social point of view both of them are unfavorable to certain segments of the agents in this heterogenous economy in terms of whether the individual potential productivity could be fully realized. Consequently, the permission of the coexistence of these two regimes could be growth enhancing.The dynamic rise and fall of the formal and informal regimes are implied along the growth process of per capita income.In the empirical part, the negative relationship between initial inequality and long run growth is discovered, using cross-province data in rural China rather than more often used cross-country data sets in literature.Interestingly, the policy dummy variable telling the permission or forbidding of the informal regime presents a positive sign.Both of these two results support our theoretical model empirically. Finally, we argue that this channel to bridge inequality and economic growth is more rural specific.inequality;credit markets;moral hazard;economic growth

Social Mobility in Latin America: A Review of Existing Evidence

This paper reviews evidence on social mobility in Latin America. Several studies have used data sets that collect intergenerational socio economic information. The data, though limited, suggest that social mobility is low in the region, even when compared with low social mobility developed countries like the United States and United Kingdom, with high levels of immobility at the lower and upper tails of the income distribution. While Latin America has improved education mobility in recent decades, which may have translated into higher mobility for younger cohorts, the region still presents, except for Chile, lower education mobility than in developed countries. The paper also reviews studies on the main determinants of the region’s low levels of social mobility, including social exclusion, low access to higher education, and labor market discrimination.Social mobility, Latin America, Inequality, Social Exclusion, Education

Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation

Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this paper we employ a single consumer-grade RGBD camera with an ICP-based registration approach to jointly align all points from a sequence of depth images non-rigidly. Subtle body deformation due to postural sway and respiration is successfully mitigated leading to a higher geometric accuracy through regularised locally affine transformations. We present results from 6 clinical cases where our method compares well with the gold standard and outperforms a previous approach. We show that our method produces better reconstructions qualitatively by visual assessment and quantitatively by consistently obtaining lower landmark error scores and yielding more accurate breast volume estimates

MARKET-BASED LAND REFORM AND FARM EFFICIENCY IN COLOMBIA: A DEA APPROACH

This paper uses Data Envelopment Analysis to measure scale and technical efficiencies of 925 farms in rural Colombia and a Tobit model to identify the effects of land market characteristics on efficiency. Findings indicate that although larger farms are more scale efficient, they are not more technical efficient than small farms. Participation in land markets increases technical efficiency, indicating a positive potential role for market-based land reform. Further results show that intensity of violence in rural areas results in increased scale efficiency, allegedly through consolidation of land ownership.Industrial Organization, Productivity Analysis,

Neighbourhood choice and neighbourhood reproduction

Although we know a lot about why households choose certain dwellings, we know relatively little about the mechanisms behind their choice of neighbourhood. Most studies of neighbourhood choice focus only on one or two dimensions of neighbourhoods: typically poverty and ethnicity. In this paper we argue that neighbourhoods have multiple dimensions and that models of neighbourhood choice should take these dimensions into account. We propose the use of a conditional logit model. From this approach we can gain insight into the interaction between individual and neighbourhood characteristics which lead to the choice of a particular neighbourhood over alternative destinations. We use Swedish register data to model neighbourhood choice for all households which moved in the city of Uppsala between 1997 and 2006. Our results show that neighbourhood sorting is a highly structured process where households are very likely to choose neighbourhoods where the neighbourhood population matches their own characteristics. We find that income is the most important driver of the sorting process, although ethnicity and other demographic and socioeconomic characteristics play important roles as well.PostprintPeer reviewe

Post-processing approaches for improving people detection performance

This is the author’s version of a work that was accepted for publication in Computer Vision and Image Understanding. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Vision and Image Understanding, 133 (2015) DOI: 10.1016/j.cviu.2014.09.010People detection in video surveillance environments is a task that has been generating great interest. There are many approaches trying to solve the problem either in controlled scenarios or in very specific surveillance applications. We address one of the main problems of people detection in video sequences: every people detector from the state of the art must maintain a balance between the number of false detections and the number of missing pedestrians. This compromise limits the global detection results. In order to reduce or relax this limitation and improve the detection results, we evaluate two different post-processing subtasks. Firstly, we propose the use of people-background segmentation as a filtering stage in people detection. Then, we evaluate the combination of different detection approaches in order to add robustness to the detection and therefore improve the detection results. And, finally, we evaluate the successive application of both post-processing approaches. Experiments have been performed on two extensive datasets and using different people detectors from the state of the art: the results show the benefits achieved using the proposed post-processing techniques.This work has been partially supported by the Spanish Government (TEC2011-25995 EventVideo)

Modeling the Impact of Big Data Analysis Investments on the Dynamics of Customer Acquisition: A Case Study of Telecommunication Sector in the United States

Postponed access: the file will be accessible after 2022-08-14In the age of data explosion, many firms are heavily investing in big data and big data analytics (BDA) without being able to anticipate how much value they will receive. Thus, there is a growing body of research that has been focusing on the impact of big data and BDA investments on firm performance. Nevertheless, most of these studies use self-reported data and none of them has addressed the dynamics in the firm outcomes as well as the continuous feedback processes between BDA investment, firm performance, and other intermediate variables. In this thesis, I collected data about two telecommunication firms in the U.S., namely T-Mobile and Verizon, to build up a system dynamics model that helps to answer two research questions that have not been properly investigated hitherto: 1) How do BDA investments dynamically influence firm performance? and 2) Which policies can help large and small firms to enhance the outcomes of their BDA investments? My simulation results reveal that when the industry develops in favor of BDA activities (i.e., lower data acquisition and data storage costs, more data generated by customers), small firms will be put at a disadvantage. In contrast, large firms with larger customer bases will be able to exploit their economies of scale in BDA investments to quickly increase their market share and gain higher profits. Thus, large firms are advised to increase their investments in BDA and data acquisition, in addition to increase their data volume more quickly even at the cost of lower data quality. As an increase in data volume will typically lead to a decrease in data storage cost, this policy will help large firms effectively increase their total number of customers, which will lead to a further decrease in the data acquisition cost, resulting in higher firm revenues and firm profits. Small firms, instead, are advised to sacrifice their profits for market share. Specifically, they should invest more heavily than large firms to lift the volume of their data up to the point that it can nullify the cost advantage of large firms. It is unclear that, though, whether small firms can survive when making such a big trade-off. Future research might explore whether the intervention from governments might help resolve this inequality between small and large firms.Master's Thesis in System DynamicsGEO-SD351MASV-SYSD