Pricing segmentation and MBA course choice

The consensus from studies of the price-demand relationship for higher education is that this relationship is negative but small. This paper investigates the circumstances in which demand for an MBA is positive to price increases. A survey of currently enrolled MBA students, and prospective MBA students, found that most students displayed the expected price elasticity in a conjoint analysis of hypothetical MBA course ratings. However, 12 per cent of respondents exhibited “reversal” behaviour regarding price. Profiling these respondents using discriminant analysis suggested that “reversals” seemed prepared to pay more for a course at a high prestige university, if they could study off-campus using print-based materials.

Disruption and resonance in the personal essay

The personal essay, as one of the most delightfully subjective manifestations of creative nonfiction, explores what is real and tangible, refined through the intimate perspective and curiosity of the writer. In her best works, the personal essayist has the capacity to disrupt her narratives in ways that will resonate with readers who are themselves adjusting to the disruption of their own personal narrative interactions by social media tools. This paper explores the process by which fragmentary episodes become segments of a linked narrative through the capacity of the personal essayist to leap associatively from personal into universal ‘truths’. Segments coalesce into cogent entities, drawn together as a resonant narrative by themes as echoes, or the deliberate juxtaposition of fragments of story. Such segments-as-narrative are based on perceptions of the essay as a disruptive text, which by the nature of its structure reverberates metaphorically beyond the known and the familiar.

Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique. © 2008 IEEE.

Diversity, ethnicity and consumption

This paper aims to examine diversity and identity issues from a marketing perspective. The traditional marketing practice of segmenting markets could be viewed as the antithesis of diversity as it relies on identifying  homogenous characteristics of a population. It is uneconomical and  generally less effective to market to a broad range of consumers than to do so for a specific group with homogenous characteristics. However, segmentation is not possible without diversify. Segmentation requires the presence of substantial differences in consumer characteristics and behaviour in a population to be truly effective. Marketing and its relationship to diversity, however, extends beyond segmentation and into issues of an individual's sense of identity and belonging. The literature suggests that an individual's identify is expressed through consumption and this can include ethnic identity. With an increasingly diverse, multicultural society in many countries, it is timely to look more closely at cultural identity and its relationship to consumption. Hofstede's work on cultural characteristics inherent in a particular country, continue to be widely used in international business. How evel; cultural identity and characteristics attributed to individuals in their country of birth may change when they immigrate to another country. Acculturation in a host country affects how immigrants see themselves and wish to be perceived. This can be problematic for marketers attempting to segment and reach consumers on the basis of their ethnicity. If consumption is an expression of identity as the literature suggests, then marketing has a role to play in either influencing or responding to issues of diversity and identity in the population at large. This paper examines the current literature on consumption, consumer behaviour and ethnic identify.

The ethical and policy implications of profiling 'vulnerable' customers

In the shadow of the global financial crisis, the issue of the marketing of credit has become an increasing concern in the past 12 months. Outstanding personal debt in the UK currently stands at £1479 billion and is rising by £1 million every 10.6 min. In Australia, there is currently $44.6 billion worth of outstanding credit card debt, and in the US, $2596 billion was owed on credit cards in 2008. At present, the banking sector utilizes sophisticated research methods to profile consumers, including those who might be considered financially vulnerable. However, the policy frameworks in most industrialized countries do not account for this form of target marketing when considering how to protect vulnerable groups. This paper is an initial attempt to examine the different methods by which profiling is conducted and the policy implications of this sophisticated form of segmentation and targeting. We argue that current consumer policies are inadequate in protecting vulnerable consumers from these marketing techniques, and recent recommendations from the Federal Reserve Bank of the United States, and the Australian Law Reform Commission to allow banks and lenders to ‘pre-screen’ potential customers will exacerbate personal debt levels, rather than reducing them.

Brain MRI thresholding using incomparability and overlap functions

In this work we present a new image thresholding algorithm for the segmentation of MRI brain images into two classes: gray matter and white matter. The proposed algorithm is based on the concept of incomparability proposed by Fodor and Roubens for fuzzy preference relations. We test our algorithm for local and global segmentation of brain images. We proof that global segmentation performs better results than local segmentation and improves the results obtained by other thresholding algorithm.

Human action segmentation via controlled use of missing data in HMMs

Segmentation of individual actions from a stream of human motion is an open problem in computer vision. This paper approaches the problem of segmenting higher-level activities into their component sub-actions using Hidden Markov Models modified to handle missing data in the observation vector. By controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. The approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. The advantage of this method over sliding windows and Viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.

Quantitative experimental comparison of single-beam, sidescan, and multibeam benthic habitat maps

Map comparison is a relatively uncommon practice in acoustic seabed classification to date, contrary to the field of land remote sensing, where it has been developed extensively over recent decades. The aim here is to illustrate the benefits of map comparison in the underwater realm with a case study of three maps independently describing the seabed habitats of the Te Matuku Marine Reserve (Hauraki Gulf, New Zealand). The maps are obtained from a QTC View classification of a single-beam echosounder (SBES) dataset, manual segmentation of a sidescan sonar (SSS) mosaic, and automatic classification of a backscatter dataset from a multibeam echosounder (MBES). The maps are compared using pixel-to-pixel similarity measures derived from the literature in land remote sensing. All measures agree in presenting the MBES and SSS maps as the most similar, and the SBES and SSS maps as the least similar. The results are discussed with reference to the potential of MBES backscatter as an alternative to SSS mosaic for imagery segmentation and to the potential of joint SBES–SSS survey for improved habitat mapping. Other applications of map-similarity measures in acoustic classification of the seabed are suggested.

Detection of fingertips based on the combination of color information and circle detection

Fingertips of human hand play an important role in hand-based interaction with computers. Therefore, identification of fingertips' positions on hand image is vital for developing a human computer interaction system. All most all of the research works for fingertips detection, initially isolate hand image from the background image. Most of these techniques develop color based segmentation methods because human skin color possess an exceptional characterises that can be used to isolate hand from the rest of the image quite easily. Sometimes color image segmentation becomes difficult due to illumination and background variations. To make it simple and reliable, this paper proposes a robust method for detecting fingertips of a hand image based on the combination of color segmentation and circle detection. Due to the characteristics of circularity of fingertips regions of hand boundary, any existing circle detection algorithms can be applied to detect circles at fingertips region. It is difficult to detect fingertips solely based on the circle detection method. For this reason, initially the proposed method detects all the circular regions on the image applying Circle Hough Transformation (CHT) then the fingertips are selected based on the color characteristics of the fingertips regions. Experimental results show that the proposed approach is promising.

Machine vision system for automatic inspection of surface defects in aluminum die casting

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Machine vision system for automatic inspection of surface defects in aluminium die casting

A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Socio-demographic constraints to travel behavior

Purpose – This study aims to ascertain the effect of socio-demographic constraints on dimension of travel choice. This study also seeks to derive personal ecological explanations for variation in travel preference, travel intention and travel choice behavior of a wide range of destinations.

Design/methodology/approach – A large representative sample of 49,105 Australian respondents is utilized. Binary logistic regression is used to determine the impact of constraint variables.

Findings – Age, income and life stage have significant differential and interactive effects on travel behavior. Socio-demographic variables act in different ways to constrain/free different types of travel behavior. However there are significant levels of travel by even the most constrained groups as well as significant amounts of non-travel by the least constrained sectors of our society. These impacts are country specific.

Research limitations/implications – The travel motivations of constraint groups need to be considered to order better understand travel behavior. Investigation of psychological and ecological facilitators and constraints to travel is needed.

Practical implications – This information is most useful for market segmentation and the development of constraint group destination marketing plans. Managers can use utilize such results to minimize the barriers to travel by particular groups.

Originality/value – This paper utilizes a large database to provide insights into the personal ecological constraints to travel.

A generalized joint inference approach for citation matching

Citation matching is the problem of extracting bibliographic records from citation lists in technical papers, and merging records that represent the same publication. Generally, there are three types of data- sets in citation matching, i.e., sparse, dense and hybrid types. Typical approaches for citation matching are Joint Segmentation (Jnt-Seg) and Joint Segmentation Entity Resolution (Jnt-Seg-ER). Jnt-Seg method is effective at processing sparse type datasets, but often produces many errors when applied to dense type datasets. On the contrary, Jnt-Seg-ER method is good at dealing with dense type datasets, but insufficient when sparse type datasets are presented. In this paper we propose an alternative joint inference approach–Generalized Joint Segmentation (Generalized-Jnt-Seg). It can effectively deal with the situation when the dataset type is unknown. Especially, in hybrid type datasets analysis there is often no a priori information for choosing Jnt-Seg method or Jnt-Seg-ER method to process segmentation and entity resolution. Both methods may produce many errors. Fortunately, our method can effectively avoid error of segmentation and produce well field boundaries. Experimental results on both types of citation datasets show that our method outperforms many alternative approaches for citation matching.

Automatic labeling of sports video using umpire gesture recognition

We present results on an extension to our approach for automatic sports video annotation. Sports video is augmented with accelerometer data from wrist bands worn by umpires in the game. We solve the problem of automatic segmentation and robust gesture classification using a hierarchical hidden Markov model in conjunction with a filler model. The hierarchical model allows us to consider gestures at different levels of abstraction and the filler model allows us to handle extraneous umpire movements. Results are presented for labeling video for a game of Cricket.

Visual object clustering via mixed-norm regularization

Many vision problems deal with high-dimensional data, such as motion segmentation and face clustering. However, these high-dimensional data usually lie in a low-dimensional structure. Sparse representation is a powerful principle for solving a number of clustering problems with high-dimensional data. This principle is motivated from an ideal modeling of data points according to linear algebra theory. However, real data in computer vision are unlikely to follow the ideal model perfectly. In this paper, we exploit the mixed norm regularization for sparse subspace clustering. This regularization term is a convex combination of the l1norm, which promotes sparsity at the individual level and the block norm l2/1 which promotes group sparsity. Combining these powerful regularization terms will provide a more accurate modeling, subsequently leading to a better solution for the affinity matrix used in sparse subspace clustering. This could help us achieve better performance on motion segmentation and face clustering problems. This formulation also caters for different types of data corruptions. We derive a provably convergent algorithm based on the alternating direction method of multipliers (ADMM) framework, which is computationally efficient, to solve the formulation. We demonstrate that this formulation outperforms other state-of-arts on both motion segmentation and face clustering.