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Orthophoto suffers from the relief displacement effects magnified by high resolution imaging sensors especially when mapping urban areas. True orthophotos eliminating relief displacement with digital surface model (DSM) are presented to assure reliable interpretability and maintain the high quality of the available data. Previous efforts did not provide accurate and fast ways for generating true othorphoto. The purpose of this paper is to try to solve the problem by analyzing the complexity of algorithm processes and finding the optimum manner to allocate them.

In this paper, an optimum segmentation number for radial sweep is presented to achieve minimum complexity. First, the scan area, number of azimuth lines and visibility judgment area of radial sweep and spiral sweep method have been discussed with rigorous geometric theory, and then algorithm complexities of both methods are estimated with mathematical computation theory. Finally, minimum complexity of the methods is obtained with extreme point theory of differential calculus.

Experiments have demonstrated that the proposed method has the best efficiency, and is efficient to avoid “M‐potion” problem, and false occlusions and false visibilities caused by the rolling area, the incompatibility between the DSM and ground image resolution.

The deduction and experiments indicate that the proposed method is a robust, accurate, fast, and effective approach to generate high quality, true orthophoto at a large‐scale.

Current segmentation systems almost invariably focus on linear segmentation and can only divide text into linear sequences of segments. This suits cohesive text such as news feed but not coherent texts such as documents of a digital library which have hierarchical structures. To overcome the focus on linear segmentation in document segmentation and to realize the purpose of hierarchical segmentation for a digital library’s structured resources, this paper aimed to propose a new multi-granularity hierarchical topic-based segmentation system (MHTSS) to decide section breaks.

MHTSS adopts up-down segmentation strategy to divide a structured, digital library document into a document segmentation tree. Specifically, it works in a three-stage process, such as document parsing, coarse segmentation based on document access structures and fine-grained segmentation based on lexical cohesion.

This paper analyzed limitations of document segmentation methods for the structured, digital library resources. Authors found that the combination of document access structures and lexical cohesion techniques should complement each other and allow for a better segmentation of structured, digital library resources. Based on this finding, this paper proposed the MHTSS for the structured, digital library resources. To evaluate it, MHTSS was compared to the TT and C99 algorithms on real-world digital library corpora. Through comparison, it was found that the MHTSS achieves top overall performance.

With MHTSS, digital library users can get their relevant information directly in segments instead of receiving the whole document. This will improve retrieval performance as well as dramatically reduce information overload.

This paper proposed MHTSS for the structured, digital library resources, which combines the document access structures and lexical cohesion techniques to decide section breaks. With this system, end-users can access a document by sections through a document structure tree.

For large projects, project segmentation and planning the size of contract packages in construction bids is a complex and critical issue. Due to the nature of construction projects, which frequently have large budgets, long durations and many activities with complex procedures, project segmentation involves complicated decision-making. To fill this gap, this study aims to develop an integrated model for planning project segmentation.

The proposed model integrates a simulation and multiple attribute decision-making method. The simulation is used to evaluate the bidding outcome of various project segmentations. The owner can then determine the bid-price behavior of contractors in response to varying work package sizes. The multiple attribute decision-making method is used to select the optimal segmentation solution from the simulated scenarios.

The proposed model is applied to a large road preservation project in Indonesia and incorporates bid participants and market conditions. The model provides seven scenarios for segmentation. The range of scenarios captures increasing competitiveness in the construction with the average bid price becoming gradually more beneficial for the owner. The model also utilizes a multiple attribute decision-making method to select the optimum scenario for the owner.

This study presents an applicable model for project segmentation that is useful for both project owners and contractors. By utilizing the proposed model, a project owner can segment a large project into smaller contract packages to create improved project pricing.

The purpose of this paper is to present a Differential Immune Clone Clustering Algorithm (DICCA) to solve image segmentation.

DICCA combines immune clone selection and differential evolution, and two populations are used in the evolutionary process. Clone reproduction and selection, differential mutation, crossover and selection are adopted to evolve two populations, which can increase population diversity and avoid local optimum. After extracting the texture features of an image and encoding them with real numbers, DICCA is used to partition these features, and the final segmentation result is obtained.

This approach is applied to segment all sorts of images into homogeneous regions, including artificial synthetic texture images, natural images and remote sensing images, and the experimental results show the effectiveness of the proposed algorithm.

The method presented in this paper represents a new approach to solving clustering problems. The novel method applies the idea two populations are used in the evolutionary process. The proposed clustering algorithm is shown to be effective in solving image segmentation.

Additive manufacturing technology significantly simplifies the production of complex three-dimensional (3 D) parts directly from the computer-aided design (CAD) model. Although additive manufacturing (AM) processes have unexampled flexibility, they still have restrictions inhibiting engineers to easily generate some specific geometric shapes, easily. Some of these problems pertain to the consumption of materials as supports, the inferior surface finish of some surfaces with certain angles, etc. One of the approaches to overcome these problems is designing by segmentation.

The proposed methodology consists of two steps: (1) segmentation of the 3 D model and (2) exploring the best orientation for each segment. In the first step, engineers consider the possible number of segments and the connection method of segments. In this paper, a series of segments, called a segmentation pattern (SP), is obtained by the recognition of features and separating them automatically (or manually when needed) with one or more appropriate planes. In the second step, the best fabrication orientation should be chosen. The criteria for choosing the best SP and OPs are minimizing the support volume, building time (directly affected by segments’ height in layer-wise AM processes) and surface roughness. Both steps are performed automatically (or manually when needed) by the algorithm created based on principles of particle swarm optimization (PSO) algorithm using Visual C#.

Experimental tests show that the segmentation design improves AM processes from the aspects of building time, material consumption and the surface quality. Segmentation design empowers users of AM technologies to reduce consumption of material by decreasing the support structures, to decrease the time of building by lowering the segments height and to decrease the surface roughness.

This paper presents an original approach in efficiency improvement of AM technologies, thus bringing the AM one step closer to maturity.

Today, medical models can be made by the use of medical imaging systems through modern image processing methods and rapid prototyping (RP) technology. In ultrasound imaging systems, as images are not layered and are of lower quality as compared to those of computerized tomography (CT) and magnetic resonance imaging (MRI), the process for making physical models requires a series of intermediate processes and it is a challenge to fabricate a model using ultrasound images due to the inherent limitations of the ultrasound imaging process. The purpose of this paper is to make high quality, physical models from medical ultrasound images by combining modern image processing methods and RP technology.

A novel and effective semi‐automatic method was developed to improve the quality of 2D image segmentation process. In this new method, a partial histogram of 2D images was used and ideal boundaries were obtained. A 3D model was achieved using the exact boundaries and then the 3D model was converted into the stereolithography (STL) format, suitable for RP fabrication. As a case study, the foetus was chosen for this application since ultrasonic imaging is commonly used for foetus imaging so as not to harm the baby. Finally, the 3D Printing (3DP) and PolyJet processes, two types of RP technique, were used to fabricate the 3D physical models.

The physical models made in this way proved to have sufficient quality and shortened the process time considerably.

It is still a challenge to fabricate an exact physical model using ultrasound images. Current commercial histogram‐based segmentation method is time‐consuming and results in a less than optimum 3D model quality. In this research work, a novel and effective semi‐automatic method was developed to select the threshold optimum value easily.

The purpose of this paper is to describe a segmentation technique based on geostatistical modeling methods utilizing geographically weighted regression (GWR) to identify submarkets which could be applied within the mass appraisal environment.

Given the spatial dimension within which neighbourhoods/submarkets exist, this paper has sought to utilize the geostatistical technique of GWR to identify them.

The efficacy of the procedure is established by demonstrating improvements in predictive accuracy of the resultant segmented market models as compared to a baseline global unsegmented model for each of the study areas. Optimal number of segments is obtained by measures of predictive accuracy, spatial autocorrelation in the residual errors and the Akaike information criterion.

The three datasets used allowed for an evaluation of the robustness of the method. Nonetheless it would be beneficial to test it on other datasets, particularly from different regions of the world.

Many researchers and mass appraisal practitioners have established the benefit of segmenting a study area into two or more submarkets as a means of incorporating the effects of location within mass valuation models. This approach develops the existing knowledge.

The research ultimately is developing more accurate valuation models upon which the property tax is based. This should create an environment of fair and acceptable assessed values by the tax paying community.

The contribution of this work lies in the methodological approach adopted which incorporates a market basket approach developed through a process of GWR. The importance of the research findings illustrate that submarket segmentation need no longer be an arbitrary process.

To achieve a full understanding of the role of marketing from plan to profit requires a knowledge of the basic building blocks. This textbook introduces the key concepts in the art or science of marketing to practising managers. Understanding your customers and consumers, the 4 Ps (Product, Place, Price and Promotion) provides the basic tools for effective marketing. Deploying your resources and informing your managerial decision making is dealt with in Unit VII introducing marketing intelligence, competition, budgeting and organisational issues. The logical conclusion of this effort is achieving sales and the particular techniques involved are explored in the final section.

The purpose of this paper is to present a novel approach for niche-market tour identification, with the objective to obtain a better segmentation of target tourists and support the design of tourism products. A proposed system, namely the Niche Tourism Identification System (NTIS) was implemented based on the proposed scheme and its functionality was showcased in a case study undertaken with a local travel agency.

The proposed system implements automated customer market segmentation, based on similar characteristics that can be collected from potential customers. After that, special-interest tourism-based market strategies and products can be designed for the potential customers. The market segmentation is conducted using a GA-based k-means clustering engine (GACE), while the parameter setting is controlled by the travel agents.

The proposed NTIS was deployed in a real-world case study which helps a local travel agency to determine the various types of niche tourism found in the existing market in Hong Kong. Its output was reviewed by experience tour planners. It was found that with the niche characteristics can be successfully revealed by summarizing the possible factors within the potential clusters in the existing database. The system performed consistently compared to human planners.

To the best of the authors’ knowledge, although some alternative methods for segmenting travel markets have been proposed, few have provided any effective approaches for identifying existing niche markets to support online inquiry. Also, GACE has been proposed to compensate for the limitations that challenge k-means clustering in binding to a local optimum and for its weakness in dealing with multi-dimensional space.

In tourism, the adoption of Information and Communication Technologies (hereinafter ICT) and variables concerning firms’ links with suppliers have been recognized as key determinants to improve companies’ competitiveness. From the perspective of efficient management of company resources, segmentation has become a key tool and is particularly significant and current in the business-to-business context. The purpose of this paper is to study the segmentation of firms in the tourist industry according to perceived ICT use and relationship value and benefits. In addition, from the management approach, the authors seek to describe the segments that enable the development of differentiated strategies aimed at consolidating relationship benefits in the long term.

Using a sample of 310 travel agencies who evaluated the relationship with their main supplier, the authors attempt to examine the utility of these variables as segmentation criteria for identifying heterogeneous groups.

The estimation of a finite mixture model suggests that these bases are able to discriminate firms into four latent segments with different levels of ICT use and relationship variables.

This research contributes to the understanding of the role that ICT and relationship variables have in the segmentation processes of tourism companies. Literature on segmentation in the business-to-business (B2B) context is limited and it is hard to find studies which apply latent methodology using behavioral criteria related to the use of ICT and relationship variables.

Segmentation of the tourism organizational market based on valuations of supplier relations and ICT use can help suppliers to design or adapt differentiation marketing strategies. Since agencies place the most value on confidence and value, tourism service suppliers should focus their efforts on improving the elements of service provision that increase perceived trust/confidence and value (i.e. growing the number of contacts, proximity to customers or sincerity, etc.). If agencies feel they can rely more on their providers, they will value their relationship more positively thereby favoring its long-term continuity.

The novelty in this work lies in the application of latent segmentation methodology and the simultaneous use of bases associated with ICT and relationship variables in B2B tourism.