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.

Consumers' attitudes, trust and willingness to pay for food information

Market failure can be corrected using different regulatory approaches ranging from high to low intervention. Recently, classic regulations have been criticized as costly and economically irrational and thus policy makers are giving more consideration to soft regulatory techniques such as information remedies. However, despite the plethora of food information conveyed by different media there appears to be a lack of studies exploring how consumers evaluate this information and how trust towards publishers influence their choices for food information. In order to fill such a gap, this study investigates questions related to topics which are more relevant to consumers, who should disseminate trustful food information, and how communication should be conveyed and segmented. Primary data were collected both through qualitative (in depth interviews and focus groups) and quantitative research (web and mail surveys). Attitudes, willingness to pay for food information and trust towards public and private sources conveying information through a new food magazine were assessed using both multivariate statistical methods and econometric analysis. The study shows that consumer attitudes towards food information topics can be summarized along three cognitive-affective dimensions: the agro-food system, enjoyment and wellness. Information related to health risks caused by nutritional disorders and food safety issues caused by bacteria and chemical substances is the most important for about 90% of respondents. Food information related to regulations and traditions is also considered important for more than two thirds of respondents, while information about food production and processing techniques, life style and food fads are considered less important by the majority of respondents. Trust towards food information disseminated by public bodies is higher than that observed for private bodies. This behavior directly affects willingness to pay (WTP) for food information provided by public and private publishers when markets are shocked by a food safety incident. WTP for consumer association (€ 1.80) and the European Food Safety Authority (€ 1.30) are higher than WTP for the independent and food industry publishers which cluster around zero euro. Furthermore, trust towards the type of publisher also plays a key role in food information market segmentation together with socio-demographic and economic variables such as gender, age, presence of children and income. These findings invite policy makers to reflect on the possibility of using information remedies conveyed using trusted sources of information to specific segments of consumers as an interesting soft alternative to the classic way of regulating modern food markets.

Biologically-inspired robust motion segmentation using mutual information

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.

Site-specific and geographical segmental social, environmental and ethical disclosures by the Ghanaian mining sector

Purpose: This paper explores the extent of site-specific and geographic segmental social, environmental and ethical reporting by mining companies operating in Ghana. We aim to: (i) establish a picture of corporate transparency relating to geographic segmentation of social, environmental and ethical reporting which is specific to operating sites and country of operation, and; (ii) gauge the impact of the introduction of integrated reporting on site-specific social, environmental and ethical reporting. Methodology/Approach: We conducted an interpretive content analysis of the annual/integrated reports of mining companies for the years 2009, 2010 and 2011 in order to extract site-specific social, environmental and ethical information relating to the companies’ mining operations in Ghana. Findings and Implications: We found that site-specific social, environmental and ethical reporting is extremely patchy and inconsistent between the companies’ reports studied. We also found that there was no information relating to certain sites, which were in operation, according to the Ghana Minerals Commission. This could simply be because operations were not in progress. Alternatively it could be that decisions are made concerning which site-specific information is reported according to a certain benchmark. One policy implication arising from this research is that IFRS should require geographic segmental reporting of material social, environmental and ethical information in order to bring IFRS into line with global developments in integrated reporting. Originality: Although there is a wealth of sustainability reporting research and an emergent literature on integrated reporting, there is currently no academic research exploring site-specific social, environmental and ethical reporting

A robust sclera segmentation algorithm

Sclera segmentation is shown to be of significant importance for eye and iris biometrics. However, sclera segmentation has not been extensively researched as a separate topic, but mainly summarized as a component of a broader task. This paper proposes a novel sclera segmentation algorithm for colour images which operates at pixel-level. Exploring various colour spaces, the proposed approach is robust to image noise and different gaze directions. The algorithm’s robustness is enhanced by a two-stage classifier. At the first stage, a set of simple classifiers is employed, while at the second stage, a neural network classifier operates on the probabilities’ space generated by the classifiers at stage 1. The proposed method was ranked the 1st in Sclera Segmentation Benchmarking Competition 2015, part of BTAS 2015, with a precision of 95.05% corresponding to a recall of 94.56%.

On fusion for robust motion segmentation

While a multitude of motion segmentation algorithms have been presented in the literature, there has not been an objective assessment of different approaches to fusing their outputs. This paper investigates the application of 4 different fusion schemes to the outputs of 3 probabilistic pixel-level segmentation algorithms. We performed an extensive experimentation using 6 challenge categories from the changedetection.net dataset demonstrating that in general simple majority vote proves to be more effective than more complex fusion schemes.

Segmentation-level fusion for iris recognition

This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

Tensor LRR and sparse coding-based subspace clustering

Subspace clustering groups a set of samples from a union of several linear subspaces into clusters, so that the samples in the same cluster are drawn from the same linear subspace. In the majority of the existing work on subspace clustering, clusters are built based on feature information, while sample correlations in their original spatial structure are simply ignored. Besides, original high-dimensional feature vector contains noisy/redundant information, and the time complexity grows exponentially with the number of dimensions. To address these issues, we propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures. TLRR seeks the lowest rank representation over original spatial structures along all spatial directions. Sparse coding learns a dictionary along feature spaces, so that each sample can be represented by a few atoms of the learned dictionary. The affinity matrix used for spectral clustering is built from the joint similarities in both spatial and feature spaces. TLRRSC can well capture the global structure and inherent feature information of data, and provide a robust subspace segmentation from corrupted data. Experimental results on both synthetic and real-world data sets show that TLRRSC outperforms several established state-of-the-art methods.