The purpose of this research was to investigate how the German market is segmented and what the German consumers perceive of Nelo’s positioning by assessment of what the Agents corroborate. The insights derived from it points out if Nelo’s image is rightly perceived in the German market.   In this paper a qualitative approach is used. Data collection method used was both interviews and documentation. Telephone interviews were conducted with three different agents operating in southern Germany. The secondary data the authors used were articles and books. Since it is a research based on a qualitative approach, the theories and the findings will be synthesized to make implication regarding the study.   The investigation has shown that the target segment for Nelo in Germany are the middle to high income group in the age 40+, but it is shown that the products offered by Nelo don’t attract this segment in southern Germany. Though product quality is good the design and material used, does not fit with the target customer rendering the brand unknown. Nelo is not well positioned in the target market because it has not been successful in communicating a clear image. To conclude Nelo´s position is not consistent with their image.

<!--[if gte mso 10]> <mce:style><! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normale Tabelle"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} > <! [endif] > This paper focuses on the pricing strategies of international airlines, being the key factor to match the supply with demand and accomplish market equilibrium. The aim of study is to find a pattern of how pricing takes place, if and how airlines implement market segmentation and take demand-related elasticities into account. We specialize on the Scandinavian Airline System (SAS) and German Lufthansa. Their flight prices were collected as primary data from the corresponding websites. We observed the following air travel services: Long-haul international, short-haul international and short-haul domestic; additionally, we differentiated price levels by the time of booking. Based on our findings we can say that the market segmentation model provides a good base for airlines. However, it has to be accompanied by additional strategies to react to arising problems (peak problem, currency fluctuation, etc). The patterns we found implemented by SAS and Lufthansa represent a firm market-responsive approach to the problems in the airline industry. < >< ><-->

According to the latest UNWTO World Tourism Barometer the first six months of 2007 have again shown an unexpected growth in the global tourism with the increase of 6 % in Europe compared with the year before. Swedish politicians have started to see tourism as an opportunity and in an investigation made 2006 showed that eighty percent of the Swedish municipalities gave priority to tourism as an industry. This priority is rather logical since investing in tourism development is profitable since each hundred-krona note an international tourists spends in Sweden 45-48 SEK returns to the state in the form of taxes and fees. There are many rather small tourism actors in Sweden who often cooperate in common activities such as marketing because of equally small financial resources. “Market segmentation is one of the most crucial long-term strategic marketing decisions a destination or organization makes, therefore it is crucial that it is performed in a proper manner since it affects the total planning and a destinations success. I wanted to understand the relationships between various tourism actors in the County of Västerbotten when it came to efficient marketing management and tourism development.

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.

Perceptual grouping is essential to manage the complexity of real world scenes. We explore bottom-up grouping at three different levels. Starting from low-level grouping, we propose a novel method for oversegmenting an image into compact superpixels, reducing the complexity of many high-level tasks. Unlike most low-level segmentation techniques, our geometric flow formulation enables us to impose additional compactness constraints, resulting in a fast method with minimal undersegmentation. Our subsequent work utilizes compact superpixels to detect two important mid-level shape regularities, closure and symmetry. Unlike the majority of closure detection approaches, we transform the closure detection problem into one of finding a subset of superpixels whose collective boundary has strong edge support in the image. Building on superpixels, we define a closure cost which is a ratio of a novel learned boundary gap measure to area, and show how it can be globally minimized to recover a small set of promising shape hypotheses. In our final contribution, motivated by the success of shape skeletons, we recover and group symmetric parts without assuming prior figure-ground segmentation. Further exploiting superpixel compactness, superpixels are this time used as an approximation to deformable maximal discs that comprise a medial axis. A learned measure of affinity between neighboring superpixels and between symmetric parts enables the purely bottom-up recovery of a skeleton-like structure, facilitating indexing and generic object recognition in complex real images.

Conventional ultrasonographic examination of the bovine ovary is based on a sequence of two-dimensional (2D) cross-section images. Day-to-day estimation of the number, size, shape and position of the ovarian follicles is one of the most important aspects of ovarian research. Computer-assisted follicle segmentation of ovarian volume can relieve physicians from the tedious manual detection of follicles, provide objective assessment of spatial relationships between the ovarian structures and therefore has the potential to improve accuracy. Modern segmentation procedures are performed on 2D images and the three-dimensional (3D) visualization of follicles is obtained from the reconstruction of a sequence of 2D segmented follicles. <p>The objective of this study was to develop a semi-automatic 3D follicle segmentation method based on seeded region growing. The 3D datasets were acquired from a sequence of 2D ultrasound images and the ovarian structures were segmented from the reconstructed ovarian volume in a single step. A seed is placed manually in each follicle and the growth of the seed is controlled by the algorithm using a combination of average grey-level, standard deviation of the intensity, newly-developed volumetric comparison test and a termination criterion. One important contribution of this algorithm is that it overcomes the boundary leakage problem of follicles of conventional 2D segmentation procedures. The results were validated against the aspiration volume of follicles, the manually detected follicles by an expert and an existing algorithm.<p>We anticipate that this algorithm will enhance follicular assessment based on current ultrasound techniques in cases when large numbers of follicles (e.g. ovarian superstimulation) obviate accurate counting and size measurement.

Spaceborne Synthetic Aperture Radar (SAR) is commonly considered a powerful sensor to detect sea ice. Unfortunately, the sea-ice types in SAR images are difficult to be interpreted due to speckle noise. SAR image denoising therefore becomes a critical step of SAR sea-ice image processing and analysis. In this study, a two-phase approach is designed and implemented for SAR sea-ice image segmentation. In the first phase, a Gamma-based bilateral filter is introduced and applied for SAR image denoising in the local domain. It not only perfectly inherits the conventional bilateral filter with the capacity of smoothing SAR sea-ice imagery while preserving edges, but also enhances it based on the homogeneity in local areas and Gamma distribution of speckle noise. The Gamma-based bilateral filter outperforms other widely used filters, such as Frost filter and the conventional bilateral filter. In the second phase, the K-means clustering algorithm, whose initial centroids are optimized, is adopted in order to obtain better segmentation results. The proposed approach is tested using both simulated and real SAR images, compared with several existing algorithms including K-means, K-means based on the Frost filtered images, and K-means based on the conventional bilateral filtered images. The F1 scores of the simulated results demonstrate the effectiveness and robustness of the proposed approach whose overall accuracies maintain higher than 90% as variances of noise range from 0.1 to 0.5. For the real SAR images, the proposed approach outperforms others with average overall accuracy of 95%.

Adipose tissue volume and distribution is related to metabolic diseases such as diabetes and atherosclerosis. This relationship is in focus for much research, much due to a worldwide increase in obesity. It is in many cases of interest to calculate the amount of adipose tissue in different compartments within the body. Commonly used methods are however prone to introduce errors due to partial volume effects. Previous studies have successfully segmented three adipose tissue compartments from abdominal two-point Dixon fat-water MRI volumes using Morphon registration and atlas segmentation. This thesis extends upon the previous work by enabling segmentation of whole-body MRI volumes and by improving the registration with the use of both fat and water data. Possible methods for bone marrow segmentation are also tested and evaluated. The methods presented seem to be sufficient for creating whole-body volumes from a set of smaller volumes. The adipose tissue segmentation was adequate for subjects with relatively small volumes of adipose tissue, whereas segmentation of subjects with large amounts of adipose tissue require further improvement. Of the evaluated methods for bone marrow segmentation one seemed to perform adequately on all the tested datasets. Due to the few datasets available for testing it was not possible to draw any general conclusions as to how well the presented methods perform.

The objective of this thesis is to detect and identify moving objects in a video sequence. The currently available techniques for motion estimation can be broadly categorized into two main classes: block matching methods and optical flow methods.<p>This thesis investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas Kanade Optical Flow Algorithm has been used in this thesis for detection of moving objects in a video sequence. Derivatives of image brightness with respect to x-direction, y-direction and time t are calculated to solve the Optical Flow Constraint Equation. The algorithm produces results in the form of horizontal and vertical components of optical flow velocity, u and v respectively. This optical flow velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequences.<p>This method has been modified to include parameters such as neighborhood size and Gaussian pyramid filtering which improve the motion estimation process. The concept of Gaussian pyramids has been used to simplify the complex video sequences and the optical flow algorithm has been applied to different levels of pyramids. The estimated motion derived from the difference in the optical flow vectors for moving objects and stationary background has been used to segment the moving objects in the video sequences. A combination of erosion and dilation techniques is then used to improve the quality of already segmented content.<p>The Lucas Kanade Optical Flow Algorithm along with other considered parameters produces encouraging motion estimation and segmentation results. The consistency of the algorithm has been tested by the usage of different types of motion and video sequences. Other contributions of this thesis also include a comparative analysis of the optical flow algorithm with other existing motion estimation and segmentation techniques. The comparison shows that there is need to achieve a balance between accuracy and computational speed for the implementation of any motion estimation algorithm in real time for video surveillance.

Perceptual grouping is essential to manage the complexity of real world scenes. We explore bottom-up grouping at three different levels. Starting from low-level grouping, we propose a novel method for oversegmenting an image into compact superpixels, reducing the complexity of many high-level tasks. Unlike most low-level segmentation techniques, our geometric flow formulation enables us to impose additional compactness constraints, resulting in a fast method with minimal undersegmentation. Our subsequent work utilizes compact superpixels to detect two important mid-level shape regularities, closure and symmetry. Unlike the majority of closure detection approaches, we transform the closure detection problem into one of finding a subset of superpixels whose collective boundary has strong edge support in the image. Building on superpixels, we define a closure cost which is a ratio of a novel learned boundary gap measure to area, and show how it can be globally minimized to recover a small set of promising shape hypotheses. In our final contribution, motivated by the success of shape skeletons, we recover and group symmetric parts without assuming prior figure-ground segmentation. Further exploiting superpixel compactness, superpixels are this time used as an approximation to deformable maximal discs that comprise a medial axis. A learned measure of affinity between neighboring superpixels and between symmetric parts enables the purely bottom-up recovery of a skeleton-like structure, facilitating indexing and generic object recognition in complex real images.