The accurate detection of static object boundaries such as contours or surfaces and dynamic tunnels of moving objects via deformable models is an ongoing research topic in computer vision. Most deformable models attempt to converge towards a desired solution by minimizing the sum of internal (prior) and external (measurement) energy terms. Such an approach is elegant, but frequently mis-converges in the presence of noise or complex boundaries and typically requires careful semi-dependent parameter tuning and initialization. Furthermore, current deformable model based approaches are computationally demanding which precludes real-time use. To address these limitations, a decoupled deformable model (DDM) is developed which optimizes the two energy terms separately. Essentially, the DDM consists of a measurement update step, employing a Hidden Markov Model (HMM) and Maximum Likelihood (ML) estimator, followed by a separate prior step, which modifies the updated deformable model based on the relative strengths of the measurement uncertainty and the non-stationary prior. The non-stationary prior is generated by using a curvature guided importance sampling method to capture high curvature regions. By separating the measurement and prior steps, the algorithm is less likely to mis-converge; furthermore, the use of a non-iterative ML estimator allows the method to converge more rapidly than energy-based iterative solvers. The full functionality of the DDM is developed in three phases. First, a DDM in 2D called the decoupled active contour (DAC) is developed to accurately identify the boundary of a 2D object in the presence of noise and background clutter. To carry out this task, the DAC employs the Viterbi algorithm as a truncated ML estimator, curvature guided importance sampling as a non-stationary prior generator, and a linear Bayesian estimator to fuse the non-stationary prior with the measurements. Experimental results clearly demonstrate that the DAC is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to three other published methods and across many images, the DAC is found to be faster and to offer consistently accurate boundary identification. Second, a fast decoupled active contour (FDAC) is proposed to accelerate the convergence rate and the scalability of the DAC without sacrificing the accuracy by employing computationally efficient and scalable techniques to solve the three primary steps of DAC. The computational advantage of the FDAC is demonstrated both experimentally and analytically compared to three computationally efficient methods using illustrative examples. Finally, an extension of the FDAC from 2D to 3D called a decoupled active surface (DAS) is developed to precisely identify the surface of a volumetric 3D image and the tunnel of a moving 2D object. To achieve the objectives of the DAS, the concepts of the FDAC are extended to 3D by using a specialized 3D deformable model representation scheme and a computationally and storage efficient estimation scheme. The performance of the DAS is demonstrated using several natural and synthetic volumetric images and a sequence of moving objects.

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.

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.

Background: It has been shown that people travel much more nowadays than ever before. Since the Internet and the e-commerce have been developed the ability to book trips online has been enabled. There are now two dominant ways of buying trips, online or offline. The survival of traditional travel agencies has been questioned and some thinks that there will be no need for them in the future. Therefore it would be interesting to see who books trips at traditional and online travel agencies.  Purpose: The purpose with this study is to understand which segments that are purchasing travels at traditional travel agencies and online travel agencies by examine demographic, psychographic and behaviouristic segmentation in the region of Jönköping. Further, in the region of Jönköping, examine advantages and disadvantages of buying trips at an online versus offline travel agency.  Method: In order to fulfil the purpose and answer the research questions the authors will use a quantitative method. The quantitative data will be gathered through a questionnaire after pilot studies have been conducted. SPSS 19.0 will be used in order to analyse the data from the questionnaires.  Conclusion: The segment of traditional travel agencies is older people with high income while the segment of online travel agencies is younger people with lower income. Advantages of traditional travel agencies are their ability to provide service, customize a trip and give expert advice. Online travel agencies have its availability, price comparison service and comfortable way to buying trips as their advantages

Title: A quantitative study of the attitudes towards loyalty programs among students   Course/ course code: 2FE16E – Independent degree project of the marketing program with integrated method, 30 hp   Authors: Sjöberg, Amelie 880410; Svensson, Daniel 890329   Background: In today’s global market, companies strive to gain a competitive position and attract as many customers as possible. One of the most important factors within the retail industry is customer loyalty. Loyalty program have become a common tool for companies to carry out in order to attract and keep customers and make them loyal to the company. However, little research has been conducted to understand what attitude customers have towards the programs. Are they really that value adding and effective for customers as companies think they are?   Purpose: The purpose of this paper is to describe students’ attitudes towards loyalty programs within the grocery retail industry. To get an understanding of what offerings that are attractive within a membership in a loyalty program, as well as to see if a membership affects the behavior of customers.    Method: This quantitative study has been carried out by a deductive approach with questionnaires as the data collection method. The research design has been based on a descriptive design.                                                                                      Conclusion: The study found that students have favorable attitudes towards the usage of loyalty programs. Further, the attitudes toward offering segmented loyalty programs were also favorable from students. The study also found signs that indicated that membership inICA’s loyalty program, lead to an increase in frequency of store visits. Additionally the research also found that student’s value economical benefits as the most attractive offerings in a loyalty program, such as discounts and personally customized discount.                                                                  Keywords:    Attitude, loyalty program, frequency, segmentation, offerings, ICA.

As a non-invasive method, functional MRI (fMRI) has been widely used for human brain mapping. Although many applications have been done, there are still some critical issues associated with fMRI. Perfusion-weighted fMRI (PWI) with exogenous contrast agent suffered from the problems of recirculation, which could contaminate the cerebral blood flow (CBF) estimation and make its ability of prediction "tissue-at-risk" in debate. We propose a rapid and effective method that combines matched-filter-fitting (MFF) and ICA where ICA was used for regions with a prolonged TTP and MFF was utilized for the remaining areas. The calculation of cerebral hemodynamics afterwards demonstrates that the proposed method may lead to a more accurate estimation of CBF. The extent to which CBF is reduced in relationship to normal values has been utilized as an indicator to discern ischemic injury. However, despite the well known difference in CBF between gray and white matter, relatively little attention has been given as to how CBF may be differently altered in gray and white matter during ischemia due to the inability to accurately separate gray and white matter. To this end, we propose a robust clustering method for automatic classification of perfusion compartments. The method is first to apply a robust principal component analysis to reduce dimension and then to use a mixture model of multivariate T distribution for clustering. Our results in ischemic stroke patients at the hyperacute phase show the clear advantage over the conventional technique. BOLD fMRI, as a feasible and preferred method for developmental neuroimaging, is seldom conducted in pediatric subjects and therefore the information about brain functional development in the early age is somewhat lacking. To this end, this dissertation also focuses on how functional brain connectivity may be present in pediatric subjects in a sleeping condition. We propose a statistical method to delineate frequency-dependent brain connectivity among brain activation regions, and an automatic procedure combined with spatial ICA approach to determine the brain functional connectivity. Our results suggest that functional connectivity exists as young as two weeks old for both sensorimotor and visual cortices and that functional connectivity is highly age-dependent. / Dissertation

This dissertation gives a general model for the estimation of shape (image segmentation), appearance, pose (image registration), and movement (tracking). The model can infer parameters for multiple objects in a dynamically changing scene. There are a number of real-world applications. In particular, in visual tracking, moving the camera to keep objects of interest in the field of view may cause the background to move. The objects can move and deform in three dimensions, but they must be captured in two-dimensional images. Each component of the image is represented by a separate layer: one for the background and a layer for each foreground object. Each layer has three components: a contour that bounds the region of the layer, a smooth function that represents the object's appearance, and a transformation that maps that layer into an image. The segmentation for each layer is a contour (embedded as the zero level set of a distance function) that is the average shape of the object computed from multiple images. The smooth function associated with a layer approximates the image data inside the contour, after the contour has been mapped into the image by a similarity transformation (rigid component) plus a vector field (non-rigid component). A practical application of having this model is that one can fix the size of a layer and then construct priors on both shape and appearance for that layer. These priors are constructed using principal components analysis (PCA), which reduces the dimensionality of the image-approximating smooth function and the vector field (non-rigid registration) and allows for more accurate modeling of an object for that layer.

The population of late-married or non-married women is growing in the current society. Considering single women¡¦s unique consumer psychology and consumer behaviors, the researcher aimed to investigate the feasibility of a single women¡¦s virtual community by employing the web 2.0 and the visual community with a focus on its economic characteristics. The data were collected through the observations of related industries, interviews with experts and targeted groups, questionnaire investigation, and operation proposals. The results of the study were classified into two categories: the business model and the position strategy. The business model (a) can enhance increasing returns effects of the dynamic circle and the lock-in circle and accumulate members, (b) plays a role of informational intermediatry who integrates contents with strategic alliances, and (c) offers E-commence with which to satisfy users¡¦ dynamic needs. Three main phases¡Xexperience economy, reputation economy, and profit-earning¡Xare developed through such an advantage. The position strategy is to provide single women with an informational platform which addresses convenience, affinity, diversity, accuracy, safe-transaction, and customized-services. The platform includes four main topics: health, entertainment, self-growth, and investment. Therefore, it is able to meet single women¡¦s needs regarding health, travel, learning, and finance. The study also offers four suggestions for people who want to manage the virtual community. First, the virtual community should be developed based on market segmentation and take customers¡¦ intentions into account. Second, it should offer customized services and promote the worth of using experiences. Third, it should be a friendly, risk-free platform in which varied, up-dated information is given. Fourth, it should offer customers easy-using ways and allow them to share information as well as interact with others.

Taiwan¡¦s mango peasants and industries can gain from trade, especially by exporting high-quality mangos to high-income countries of the world ( like Japan ). But there are still some Taiwanese mango peasants who suffer from low bargaining power in a nearly perfectly competitive market and the downward trend of market prices. By interviewing peasants and middlemen about marketing channels of mangos of Yujing Township in Tainan County, I found the following aspects as crucial for a high-profit marketing of mango: agricultural technology, grading and packaging, quality control and logistical conditions as well as a better interaction between peasants, farmers¡¦ associations, enterprises and governmental institutions. Thus, I consider using markets segmentation as a good strategy in order to increase the competitiveness of agricultural products and lower nontariff trade barriers, therefore help raising peasants¡¦ bargaining power and solute the problem of downward trend of market prices. Markets segmentation is a term used in order to describe the segmentation of agricultural products according to their characteristics. The four aspects mentioned above make the markets segmentation efficient. Good interactions between peasants, farmers¡¦ associations, enterprises and governmental institutions can facilitate the operation in the marketing system and thus increase the value of agricultural products. This study also inspires China¡¦s peasants as well as makes China government the scholar who research problems about agriculture, rural areas and peasantry understand the problems of Mainland China¡¦s agricultural products marketing.

Abstract The research focus on the study the market segmentation and product strategy of ultra-high purity anhydrous liquefied ammonia. Liquefied ammonia gas has wide applications in current industrial markets, ultra high purity ammonia gas is a key process material in light emission diode, solar cell and integrated circuit industries. How to develop a new generation NH3 purification process, select multi-type high pressure containers with global logistic supply ability, and qualified international standard iso-container supply system are key successful factors to develop a competitive product promotion and marketing strategy. The research can help domestic green product manufacturers improve their final products market value. Based on expert interview records and secondary data collection, Michael Porter¡¦s five forces model was used to analyze external technical environment challenges and the SWOT analysis was used to analyze the interior condition of special gas. The result shows that the ultra-high purity ammonia gas is facing a highly dynamic environment. The internal human resource management, manufacturing process update flexibility, and diversity in product mix are key factors that affect the relationship between gas suppliers and customers in electronics. Therefore, specialty gas providers need to have a comprehensive gas manufacturing capability, adequate storage capability, and global operational channel in order to meet the need of end customers. Moreover, the speed and capability of the supply system is the key to the future product strategy of high purity ammonia gas.