On-line handwritten segmentation in linear drawings

This paper addresses the problem of tracking line segments corresponding to on-line handwritten obtained through a digitizer tablet. The approach is based on Kalman filtering to model linear portions of on-line handwritten, particularly, handwritten numerals, and to detect abrupt changes in handwritten direction underlying a model change. This approach uses a Kalman filter framework constrained by a normalized line equation, where quadratic terms are linearized through a first-order Taylor expansion. The modeling is then carried out under the assumption that the state is deterministic and time-invariant, while the detection relies on double thresholding mechanism which tests for a violation of this assumption. The first threshold is based on an approach of layout kinetics. The second one takes into account the jump in angle between the past observed direction of layout and its current direction. The method proposed enables real-time processing. To illustrate the methodology proposed, some results obtained from handwritten numerals are presented.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy. The accuracy was reduced in urban areas partly because of TerraSAR-X’s restricted visibility of the ground surface due to radar shadow and layover.

Re-thinking commercial real estate market segmentation

Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

Benchmark datasets for detection and tracking

There is a rising demand for the quantitative performance evaluation of automated video surveillance. To advance research in this area, it is essential that comparisons in detection and tracking approaches may be drawn and improvements in existing methods can be measured. There are a number of challenges related to the proper evaluation of motion segmentation, tracking, event recognition, and other components of a video surveillance system that are unique to the video surveillance community. These include the volume of data that must be evaluated, the difficulty in obtaining ground truth data, the definition of appropriate metrics, and achieving meaningful comparison of diverse systems. This chapter provides descriptions of useful benchmark datasets and their availability to the computer vision community. It outlines some ground truth and evaluation techniques, and provides links to useful resources. It concludes by discussing the future direction for benchmark datasets and their associated processes.

An eye for an eye: robust motion segmentation by applying the latest in human vision research

Automatically extracting interesting objects from videos is a very challenging task and is applicable to many research areas such robotics, medical imaging, content based indexing and visual surveillance. Automated visual surveillance is a major research area in computational vision and a commonly applied technique in an attempt to extract objects of interest is that of motion segmentation. Motion segmentation relies on the temporal changes that occur in video sequences to detect objects, but as a technique it presents many challenges that researchers have yet to surmount. Changes in real-time video sequences not only include interesting objects, environmental conditions such as wind, cloud cover, rain and snow may be present, in addition to rapid lighting changes, poor footage quality, moving shadows and reflections. The list provides only a sample of the challenges present. This thesis explores the use of motion segmentation as part of a computational vision system and provides solutions for a practical, generic approach with robust performance, using current neuro-biological, physiological and psychological research in primate vision as inspiration.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

The Jua Kali and the Mali-Mali: the informal/formal nexus amongst Kenyan micro enterprises

This paper presents a preliminary exploration of the informal/formal economy nexus and entrepreneurial processes amongst a sample of Kenyan roadside vendors who mostly operate in the informal economy. Using semi-structured interviews, data was collected from sixty street vendors across Kenya. In particular the paper focuses on the relationship between the informal and formal economy and the factors that promote formality amongst micro and small enterprises in developing countries. The paper presents a conceptualization of a potential segmentation of the informal economy, considering the implications of this in terms of base of the pyramid initiatives and the promotion of development through enterprise.

Imitation, entrepreneurship and the informal/formal economy nexus: an African perspective

This paper explores a segmentation of micro and small enterprises (MSEs) in developing countries within the formal/informal economy nexus that has wide-ranging implications for the targeting of base-of-the-pyramid initiatives and entrepreneurship theory. This proposed segmentation emerges from the analysis of a sample of Kenyan MSEs utilising current and prior business models; the antecedent influences shaping the business model; barriers to entry associated with knowledge, capital and skills; the degree of innovation or imitation evident in the business model linked to the nature of opportunity recognition; and their relationship with the formal institutional business environment.

Emerging segmentation practices in the age of the social customer

This paper reports on an exploratory study of segmentation practices of organisations with a social media presence. It investigates whether traditional segmentation approaches are still relevant in this new socio-technical environment and identifies emerging practices. The study found that social media are particularly promising in terms of targeting influencers, enabling the cost-effective delivery of personalised messages and engaging with numerous customer segments in a differentiated way. However, some problems previously identified in the segmentation literature still occur in the social media environment, such as the technical challenge of integrating databases, the preference for pragmatic rather than complex solutions and the lack of relevant analytical skills. Overall, a gap has emerged between marketing theory and practice. While segmentation is far from obsolete in the age of the social customer, it needs to adapt to reflect the characteristics of the new media.

Polar cap patch segmentation of the tongue of ionization in the morning convection cell

Two types of poleward moving plasma concentration enhancements (PMPCEs) were observed during a sequence of pulsed reconnection events, both in the morning convection cell: Type L (low density) was associated with a cusp flow channel and seems likely to have been produced by ionization associated with particle precipitation, while Type H (high density) appeared to originate from the segmentation of the tongue of ionization by the processes which produced the Type L events. As a result, the Type L and Type H PMPCEs were interspersed, producing a complex density structure which underlines the importance of cusp flow channels as a mechanism for segmenting and structuring electron density in the cusp and shows the necessity of differentiating between at least two classes of electron density patches.

Achieving market segmentation from B2B sectorisation

Purpose – The creation of a target market strategy is integral to developing an effective business strategy. The concept of market segmentation is often cited as pivotal to establishing a target market strategy, yet all too often business-to-business marketers utilise little more than trade sectors or product groups as the basis for their groupings of customers, rather than customers' characteristics and buying behaviour. The purpose of this paper is to offer a solution for managers, focusing on customer purchasing behaviour, which evolves from the organisation's existing criteria used for grouping its customers. Design/methodology/approach – One of the underlying reasons managers fail to embrace best practice market segmentation is their inability to manage the transition from how target markets in an organisation are currently described to how they might look when based on customer characteristics, needs, purchasing behaviour and decision-making. Any attempt to develop market segments should reflect the inability of organisations to ignore their existing customer group classification schemes and associated customer-facing operational practices, such as distribution channels and sales force allocations. Findings – A straightforward process has been derived and applied, enabling organisations to practice market segmentation in an evolutionary manner, facilitating the transition to customer-led target market segments. This process also ensures commitment from the managers responsible for implementing the eventual segmentation scheme. This paper outlines the six stages of this process and presents an illustrative example from the agrichemicals sector, supported by other cases. Research implications – The process presented in this paper for embarking on market segmentation focuses on customer purchasing behaviour rather than business sectors or product group classifications - which is true to the concept of market segmentation - but in a manner that participating managers find non-threatening. The resulting market segments have their basis in the organisation's existing customer classification schemes and are an iteration to which most managers readily buy-in. Originality/value – Despite the size of the market segmentation literature, very few papers offer step-by-step guidance for developing customer-focused market segments in business-to-business marketing. The analytical tool for assessing customer purchasing deployed in this paper originally was created to assist in marketing planning programmes, but has since proved its worth as the foundation for creating segmentation schemes in business marketing, as described in this paper.

Implementation rules to bridge the theory/practice divide in market segmentation

Despite an extensive market segmentation literature, applied academic studies which bridge segmentation theory and practice remain a priority for researchers. The need for studies which examine the segmentation implementation barriers faced by organisations is particularly acute. We explore segmentation implementation through the eyes of a European utilities business, by following its progress through a major segmentation project. The study reveals the character and impact of implementation barriers occurring at different stages in the segmentation process. By classifying the barriers, we develop implementation "rules" for practitioners which are designed to minimise their occurrence and impact. We further contribute to the literature by developing a deeper understanding of the mechanisms through which these implementation rules can be applied.