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One of the most important activities carried out by human resource management is personnel selection, concerned with identifying an individual from a pool of candidates suitable for a vacant position. Traditionally, personnel selection is a group decision-making problem under multiple criteria containing subjectivity, imprecision, and vagueness, which are best represented with fuzzy data. In this article, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method extended to intuitionistic fuzzy environments is proposed to select appropriate personnel among candidates. An intuitionistic fuzzy set (IFS), which is characterized by membership function, nonmembership function, and hesitation margin, is a more suitable way to deal with vagueness when compared to a fuzzy set. To demonstrate the applicability and effectiveness of the intuitionistic fuzzy TOPSIS method, a numerical example of personnel selection in a manufacturing company for a sales manager position is given. C 2011 Wiley Periodicals, Inc.

Using one’s hands in human–computer interaction increases both the effectiveness of computer usage and the speed of interaction. One way of accomplishing this goal is to utilize computer vision techniques to develop hand-gesture-based interfaces. A video database system is one application where a hand-gesture-based interface is useful, because it provides a way to specify certain queries more easily. We present a hand-gesture-based interface for a video database system to specify motion and spatiotemporal object queries. We use a regular, low-cost camera to monitor the movements and configurations of the user’s hands and translate them to video queries. We conducted a user study to compare our gesture-based interface with a mouse-based interface on various types of video queries. The users evaluated the two interfaces in terms of different usability parameters, including the ease of learning, ease of use, ease of remembering (memory), naturalness, comfortable use, satisfaction, and enjoyment. The user study showed that querying video databases is a promising application area for hand-gesture-based interfaces, especially for queries involving motion and spatiotemporal relations. © 2014, Springer-Verlag London

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There is a natural trend in modeling a scene on a computer with minimum effort of the user. Wireframe modeling and texturing is the main two factors that affect the quality of results in computer graphics. In this paper, instead of 3D model reconstruction, automatical texture extraction and texture registering for surfaces are discussed. Deficiency of the artificial textures to create photorealistic results leads to using real textures for rendering. Assuming that camera parameters, lighting conditions, 3D model and its surface properties are known and the image sequences of the scene are provided by the user, textures for surfaces can be extracted from images. Naturally, textures coming from different images need to be enhanced. Some artifacts i.e. highlights, distortion from perspective projection should be removed. Unlike traditional texture mapping which generates a single texture for a surface, multiple textures are used in rendering. Selection mechanism enables us to choose the best texture from multiple textures according to the orientation of the viewpoint. The texture, which is mapped, is not unique and static. Therefore, it is called dynamic texture mapping. Experiments show that the results are promising.

Abstract. Hand is a very convenient interface for immersive human-computer interaction. Users can give commands to a computer by hand signs (hand postures, hand shapes) or hand movements (hand gestures). Such a hand interface system can be realized by using cameras as input devices, and software for analyzing the images. In this hand interface system, commands are recognized by analyzing the hand shapes and its trajectories in the images. Therefore, success of the recognition of hand shape is vital and depends on the discriminative power of the hand shape representation. There are many shape representation techniques in the literature. However, none of them are working properly for all shapes. While a representation leads to a good result for a set of shapes, it may fail in another one. Therefore, our aim is to find the most appropriate shape representation technique for hand shapes to be used in hand interfaces. Our candidate representations are Fourier Descriptors, Hu Moment Invariant, Shape Descriptors and Orientation Histogram. Based on widely-used hand shapes for an interface, we compared the representations in terms of their discriminative power and speed.

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