We describe the dynamic process of abdominal segment generation in the milkweed bug Oncopeltus fasciatus. We present detailed morphological measurements of the growing germband throughout segmentation. Our data are complemented by cell division profiles and expression patterns of key genes, including invected and even-skipped as markers for different stages of segment formation. We describe morphological and mechanistic changes in the growth zone and in nascent segments during the generation of individual segments and throughout segmentation, and examine the relative contribution of newly formed versus existing tissue to segment formation. Although abdominal segment addition is primarily generated through the rearrangement of a pool of undifferentiated cells, there is nonetheless proliferation in the posterior. By correlating proliferation with gene expression in the growth zone, we propose a model for growth zone dynamics during segmentation in which the growth zone is functionally subdivided into two distinct regions: a posterior region devoted to a slow rate of growth among undifferentiated cells, and an anterior region in which segmental differentiation is initiated and proliferation inhibited.

This thesis discusses the possibilities of using cluster analysis methods for customer segmentation. The theoretical part is focused on description of selected methods of cluster analysis and explanation of other concepts related to this topic, such as CRM, segmentation and targeted communication. In the practical part are applied cluster analysis methods to real data unnamed company with the aim of creating a default substrates useful for planning and implementation of targeted communication. For the main calculations is used program R, for data and output editing is used MS Excel. At the end of the work are evaluated applied methods and summarized lessons learned from the cluster analysis. For a company were created and characterized databases which could be useful for marketing decisions.

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.

The South African National Department of Tourism has recently initiated the National Tourism Sector Strategy aimed at developing a sustainable tourism economy, and making the country a Top 20 global tourism destination by 2020.China is one of South Africa’s major non-African sources of tourist arrivals. To ensure a growing share of this booming market, South African tourism scholars and practitioners have to pay close attention to the behaviour of Chinese outbound tourists, particularly their destination choice behaviour. The Theory of Planned Behaviour (TPB)– an extension of the Theory of Reasoned Action (TRA)– can serve as a basis for researching destination choice. According to the TPB literature, intention is the most immediate and important determinant of behaviour. Three direct predictors of intention, namely, attitude, subjective norms, and perceived control, are functions of latent behavioural, normative, and control beliefs, respectively. The TPB is parsimonious but open to the inclusion of additional predictors if there is evidence that these predictors may explain a significant proportion of the variance in intention and behaviour after the basic predictors (attitude, subjective norms, and perceived control) have been accounted for. The current research successfully extended the TPB model for predicting potential Chinese tourists’ intentions to visit South Africa by adding two additional variables: travel motivation and travel constraints. The push-pull motivation framework discussed in the study postulates that people travel because they are pushed by internal forces (inner needs) and pulled by external forces (destination attributes). Typical barriers to travel include intrapersonal, interpersonal, and structural constraints. The new model makes an important contribution to the literature on destination choice, and provides South Africa’s destination marketers with suggestions for attracting and serving Chinese tourists. In addition, the research shows that both travel motivation and travel constraints can be used as bases for segmenting the outbound Chinese tourist market interested in visiting South Africa. A survey approach and a structured questionnaire distributed electronically to the online panel members of a Chinese market research company were instrumental in collecting the empirical data for the study. The questionnaire was originally written in English and translated into Chinese (Mandarin) via a blind translation-back-translation method. Attitude, subjective norms, perceived control, and visit intention were all operationalised as unidimensional and used scales adapted from previous studies. New scales were developed for travel motivation and travel constraints– both operationalised as multidimensional. Quota sampling, used to identify respondents aged 18 or older and living in Beijing, Shanghai, and Guangzhou, resulted in 630 usable questionnaires obtained from 1,510 sent invitation e-mails, yielding a response rate of 41.7%. The raw data collected were prepared through the sequential steps of editing, coding, and filing, and then analysed using both descriptive and inferential statistics. Descriptive analysis suggested that broadening personal horizons, viewing the natural scenery, and seeing something different were the top motives for visiting South Africa, while language, fear of crime, and lack of travel companions were the top barriers to visiting South Africa. According to the factor analysis, travel motivation had three underlying dimensions – learning, escape, and aesthetics and appreciation, while operational, risk and fear, and social barriers were three underlying dimensions of travel constraints. Regression analysis showed that the proposed extended TPB model had higher predictive power for visit intention than both TRA and TPB models; the basic predictors – attitude, subjective norms, and perceived control – all had a significant impact on visit intention; and in terms of the additional predictors, learning, operational constraints, and social constraints had a significant impact on visit intention. The analysis of variance indicated that travel frequency and age were the most profound background factors with an influence on the extended TPB model. Finally, cluster analysis resulted in two market segments with distinct profiles, that is, High-Motivation/ Low-Constraint (HMLC) tourists and Low-Motivation/High-Constraint (LMHC) tourists. Based on the theoretical and empirical findings of the current research, it is recommended that destination marketers in South Africa: advertise specific benefits of touring South Africa, namely, increasing knowledge, relieving stress, and enjoying high environmental quality, to advance Chinese residents’ perceptions of the country; develop tourism experiences that can be taken in a week or shorter to cater for the unique annual leave and public holiday policy in China; launch a media relations campaign in China to ensure that the facts about South Africa are communicated without distortion; collaborate with other destination stakeholders such as government and businesses, to actively attract and retain Chinese tourists for example by educating the public about Chinese culture and training employees to improve the quality of service; target the HMLC tourists via the Internet (particularly the social media) and by developing holiday packages that include activities related to cultural tourism, rest and relaxation, and nature-based tourism; and target the LMHC tourists by cooperating with local travel agencies and by developing holiday packages that highlight the diversity of tourism activities and offer value-added products/services.

Each individual/household progresses through the family life cycle (FLC). This progression, which is characterized by various stages and varying consumption portfolios, can be traditional or non-traditional in nature. In the general marketing sense, the FLC concept has great value. The concept is utilized in a variety of marketing activities, particularly in segmentation, and is also applied in consumer behaviour. The lack of research regarding the FLC in South Africa and the need to investigate the concept’s applicability to different environments motivated this research. The aim of the study was to determine how the FLC within a South African context compared to the theoretical depiction of the concept. The evaluation of literature revealed five distinct traditional stages and an array of non-traditional stages, determined by a combination of life stage determining variables. Regarding the empirical approach, self-administered questionnaires were distributed to a convenience sample consisting of 225 students and staff members of the then Port Elizabeth Technikon. The empirical findings revealed the following. {u100083} Non-traditional stages were more prevalent than traditional stages. ii {u100083} Marital status, the presence/absence of children and living arrangement appear to be sufficient life stage determining variables for both current and prospective life stage classifications. {u100083} The consumption portfolios of individuals in the traditional FLC were similar to theory. The research provided insight into the consumption portfolios of individuals in the non-traditional FLC. {u100083} Based on the intentions of certain individuals regarding marriage, having children and living arrangements, it appears as though the future FLC will include an integration of traditional and non-traditional progressions.

We introduce a new way to accurately segment the 3D femur from pelvic CT scans. The femur is a difficult target for segmentation due to its proximity to the acetabulum, irregular shape and the varying thickness of its hardened outer shell. Atypical bone morphologies, such as the ones present in hips suffering from Femoral Acetabular Impingements (FAIs) can also provide additional challenges to segmentation. We overcome these difficulties by (a) dividing the femur into the femur head and body regions (b) analysis of the femur-head and neighbouring acetabulum’s composition (c) segmentations with two levels of detail – rough and fine contours. Segmentations of the CT volume are performed iteratively, on a slice-by-slice basis and contours are extracted using the morphological snake algorithm. Our methodology was designed to require little initialization from the user and to deftly handle the large variation in femur shapes, most notably from deformations attributed to cam-type FAIs. Our efforts are to provide physicians with a new tool that creates patient-specific and high-quality 3D femur models while requiring much less time and effort. We tested our methodology on a database of 20 CT volumes acquired at the Ottawa General Hospital during a study into FAIs. We selected 6 CT scans from the database, for a total of 12 femurs, considering wide inter-patient variations. Of the 6 patients, 4 had unilateral cam-type FAIs, 1 had a bilateral cam-type FAI and the last was from a control group. The femurs segmented with our method achieved an average volume overlap error of 2.71 ± 0.44% and an average symmetric surface distance of 0.28 ± 0.04 mm compared against the same, manually segmented femurs. These results are better than all comparable literature and accurate enough to be used to in the creation of patient-specific 3D models.

This thesis investigates the evaluation of automatic and manual text segmentation. Text segmentation is the process of placing boundaries within text to create segments according to some task-dependent criterion. An example of text segmentation is topical segmentation, which aims to segment a text according to the subjective definition of what constitutes a topic. A number of automatic segmenters have been created to perform this task, and the question that this thesis answers is how to select the best automatic segmenter for such a task. This requires choosing an appropriate segmentation evaluation metric, confirming the reliability of a manual solution, and then finally employing an evaluation methodology that can select the automatic segmenter that best approximates human performance. A variety of comparison methods and metrics exist for comparing segmentations (e.g., WindowDiff, Pk), and all save a few are able to award partial credit for nearly missing a boundary. Those comparison methods that can award partial credit unfortunately lack consistency, symmetricity, intuition, and a host of other desirable qualities. This work proposes a new comparison method named boundary similarity (B) which is based upon a new minimal boundary edit distance to compare two segmentations. Near misses are frequent, even among manual segmenters (as is exemplified by the low inter-coder agreement reported by many segmentation studies). This work adapts some inter-coder agreement coefficients to award partial credit for near misses using the new metric proposed herein, B. The methodologies employed by many works introducing automatic segmenters evaluate them simply in terms of a comparison of their output to one manual segmentation of a text, and often only by presenting nothing other than a series of mean performance values (along with no standard deviation, standard error, or little if any statistical hypothesis testing). This work asserts that one segmentation of a text cannot constitute a “true” segmentation; specifically, one manual segmentation is simply one sample of the population of all possible segmentations of a text and of that subset of desirable segmentations. This work further asserts that an adapted inter-coder agreement statistics proposed herein should be used to determine the reproducibility and reliability of a coding scheme and set of manual codings, and then statistical hypothesis testing using the specific comparison methods and methodologies demonstrated herein should be used to select the best automatic segmenter. This work proposes new segmentation evaluation metrics, adapted inter-coder agreement coefficients, and methodologies. Most important, this work experimentally compares the state-or-the-art comparison methods to those proposed herein upon artificial data that simulates a variety of scenarios and chooses the best one (B). The ability of adapted inter-coder agreement coefficients, based upon B, to discern between various levels of agreement in artificial and natural data sets is then demonstrated. Finally, a contextual evaluation of three automatic segmenters is performed using the state-of-the art comparison methods and B using the methodology proposed herein to demonstrate the benefits and versatility of B as opposed to its counterparts.

This dissertation describes a research project concerned with establishing the topical structure of long informal documents. In this research, we place special emphasis on literary data, but also work with speech transcripts and several other types of data. It has long been acknowledged that discourse is more than a sequence of sentences but, for the purposes of many Natural Language Processing tasks, it is often modelled exactly in that way. In this dissertation, we propose a practical approach to modelling discourse structure, with an emphasis on it being computationally feasible and easily applicable. Instead of following one of the many linguistic theories of discourse structure, we attempt to model the structure of a document as a tree of topical segments. Each segment encapsulates a span that concentrates on a particular topic at a certain level of granularity. Each span can be further sub-segmented based on finer fluctuations of topic. The lowest (most refined) level of segmentation is individual paragraphs. In our model, each topical segment is described by a segment centre -- a sentence or a paragraph that best captures the contents of the segment. In this manner, the segmenter effectively builds an extractive hierarchical outline of the document. In order to achieve these goals, we use the framework of factor graphs and modify a recent clustering algorithm, Affinity Propagation, to perform hierarchical segmentation instead of clustering. While it is far from being a solved problem, topical text segmentation is not uncharted territory. The methods developed so far, however, perform least well where they are most needed: on documents that lack rigid formal structure, such as speech transcripts, personal correspondence or literature. The model described in this dissertation is geared towards dealing with just such types of documents. In order to study how people create similar models of literary data, we built two corpora of topical segmentations, one flat and one hierarchical. Each document in these corpora is annotated for topical structure by 3-6 people. The corpora, the model of hierarchical segmentation and software for segmentation are the main contributions of this work.

In this thesis a new framework is proposed for obtaining the spongy bone, cortical bone, muscle and adipose tissue from MRI data. The method focuses on the accurate extraction of the edges of the target tissues, which is the main drawback of previous works. In this framework six new methods, as listed in section 1.3, are utilized together for improving the result of the segmentation by detecting the relational position of the tissues, acquiring the best possible contribution from the operator in terms of time and efficiency, forward and backward transfer of the segmented tissues at the seed slice and using the newly proposed Deformable Kernel Fuzzy-C Mean (DKFCM) method for improving the result of segmentation on the edges. This method first limits the searching area for the voxels of the target tissue from the whole data to a small strip around the edges of the target tissue. Then, it applies a very accurate segmentation on the searching area, using a deformable kernel, which is capable of adapting itself with the shape of the edge. Comparing the results of this work with some popular MRI segmentation methods like active contour, watershed, FCM and also some heuristic methods, which are proposed in literature for segmenting the MRI to four tissues, demonstrates the superiority of the proposed method especially on the edges.

Scene parsing, or segmenting all the objects in an image and identifying their categories, is one of the core problems of computer vision. In order to achieve an object-level semantic segmentation, we build upon the recent superparsing approach by Tighe and Lazebnik, which is a nonparametric solution to the image labeling problem. Superparsing consists of four steps. For a new query image, the most similar images from the training dataset of labeled images is retrieved based on global features. In the second step, the query image is segmented into superpxiels and 20 di erent local features are computed for each superpixel. We propose to use the SLICO segmentation method to allow control of the size, shape and compactness of the superpixels because SLICO is able to produce accurate boundaries. After all superpixel features have been extracted, feature-based matching of superpixels is performed to nd the nearest-neighbour superpixels in the retrieval set for each query superpxiel. Based on the neighbouring superpixels a likelihood score for each class is calculated. Finally, we formulate a Conditional Random Field (CRF) using the likelihoods and a pairwise cost both computed from nonparametric estimation to optimize the labeling of the image. Speci cally, we de ne a novel pairwise cost to provide stronger semantic contextual constraints by incorporating the similarity of adjacent superpixels depending on local features. The optimized labeling obtained with the CRF results in superpixels with the same labels grouped together to generate segmentation results which also identify the categories of objects in an image. We evaluate our improvements to the superparsing approach using segmentation evaluation measures as well as the per-pixel rate and average per-class rate in a labeling evaluation. We demonstrate the success of our modi ed approach on the SIFT Flow dataset, and compare our results with the basic superparsing methods proposed by Tighe and Lazebnik.