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There is increasing desire and need in research of water region detection owing to the unexpected natural disaster that lead to financial, environment and human losses. Surveying of water region and research on its feature is very basic step for many planning, especially for countries like Japan, where tsunami has caused the changes on water region in March, 2011. Essentially, identifying water region from satellite images is one of the grand steps of water resources management for a country. Professional and academic institutions play a vital role in the management of water resources as they are instrumental in research. The objective of this paper is to identify the water region from satellite image. In this paper, the segmentation algorithm based on SOM (self-organizing map) neural network with compression pre-processing by wavelet transform and image smoothing using Gaussian low-pass frequency domain filters are presented.

The market for sport tourists is very diverse and motivations of sport tourists are manifold. This also applies to the field of water sport tourism, which has not yet intensely been analysed by researchers. In order to analyse motivations and to reach target groups such as water sport tourists adequately, market segmentation is necessary. The purpose of this paper is to investigate sport motivations of tourists during their holidays as well as the tourist's participation in water sport activities, using the example of the German North Sea island Sylt. Thereby, consumer profiles will be established.

A quantitative research paradigm was chosen. A survey was conducted using a standardised self‐administered questionnaire. The sample comprised n=263 participants. Two indices, one for sport motivation and one for travel motivation were constructed. Factor, as well as cluster, analysis was applied to segment the sample. Furthermore, discriminant analysis was used to identify differences between the two clusters. Finally, cross tabulations underlined the differences between the clusters.

Overall, 47.1 per cent of the sample takes part in water sports. The cluster analysis based on motivational factors revealed two groups, the casuals and the committed. Significant differences were detected between the groups with regard to sport and holiday consumption patterns and sport expenditures. However, there were no significant socio‐demographic differences between the clusters.

It is shown that segmentation of travellers is useful to reach the different target groups and offer unique products and services, depending on, e.g. age and sport preferences.

The results of this research indicate the diverse nature of water sport tourists and their underlying motivations. It is shown that holiday sport motivation depends on the actual sports practiced by the travellers, implying that prior sport motivation and involvement influence travellers' sport motivation. Overall, this research highlights the importance of segmenting sport tourists.

The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection.

This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed.

Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results.

The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm.

This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Index by subjects, compiled by K.G.B. Bakewell covering the following journals: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

Compiled by K.G.B. Bakewell covering the following journals published by MCB University Press: Facilities Volumes 8‐18; Journal of Property Investment & Finance Volumes 8‐18; Property Management Volumes 8‐18; Structural Survey Volumes 8‐18.

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.

The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.

The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.

The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

Most firms have traditionally offered one level of logistics service to all customers. This often results in some customer groups receiving more service than necessary, while other groups receive less service than necessary. The emerging concept of logistical segmentation suggests that companies can structure their logistical offerings to meet the needs and requirements of different customer groups. Uses the “nested” approach from industrial marketing to illustrate an application of logistical segmentation. More specifically, examines the importance of selection factors in modal choice for international sourcing according to geographic location, primary mode of transport, and job responsibilities. Uses empirical data from a group of regional purchasing managers to facilitate understanding of the nested approach.