Consumer Preference for Virtual Reality Advertisements with Human-Scene Interaction: An Intermediary Based on Psychological Needs.

Consumption is undergoing a digital revolution brought about by virtual technology, and the emergence of virtual spokespersons has changed the pattern of advertising endorsement sales. Are virtual character spokespersons better than real celebrity spokespersons? This article aims to explore the relationships between different types of advertising spokespersons, different types of advertising scenarios, and consumers' purchase intentions. The results of 2 pilot experiments and 4 formal experiments show that compared with spokespersons by real people, virtual character spokespersons have a greater positive impact on consumers' purchase intentions, which is achieved through the mediating role of psychological need satisfaction.

The Impact of Consumers' Choice Deferral Behavior on Their Intertemporal Choice Preference.

The purpose of this study is to explore the influence of consumers' choice deferral behavior on their intertemporal choice preference. The empirical study shows that consumers' choice deferral behavior can significantly affect their intertemporal decision preference through the level of hopefulness. Compared with non-choice deferral behavior, choice deferral behavior can improve the level of consumers' sense of hopefulness, which then makes them prefer larger-longer interests in intertemporal decision-making.

Overlay of thermal and visual medical images using skin detection and image registration.

Thermography captures the temperature distribution of the human skin and is employed in various medical applications. Often it is useful to cross-reference the resulting thermograms with visual images of the patient, either to see which part of the anatomy is affected by a certain disease or to judge the efficacy of the treatment. An attractive approach to provide this information is to overlay the two image types and show a composite image to the clinician.

Adopting the DICOM standard for medical infrared images.

In recent years there has been a resurgence of interest in the application of infrared thermal imaging in medicine. Yet fairly little effort has been spent towards standardisation of the field and a common communication and exchange format for thermal images. Most other medical areas where digital imaging is employed have subscribed to DICOM (Digital Imaging and Communication in Medicine) as a common standard for storing and retrieving medical imagery.

Efficient 3-D medical image registration using a distributed blackboard architecture.

A major drawback of 3-D medical image registration techniques is the performance bottleneck associated with re-sampling and similarity computation. Such bottlenecks limit registration applications in clinical situations where fast execution times are required and become particularly apparent in the case of registering 3-D data sets. In this paper a novel framework for high performance intensity-based volume registration is presented.

An evaluation of lossless compression algorithms for medical infrared images.

Several popular lossless image compression algorithms were evaluated for the application of compressing medical infrared images. Lossless JPEG, JPEG-LS,JPEG2000, PNG, and CALIC were tested on an image dataset of 380+ thermal images. The results show that JPEG-LS is the algorithm with the best performance, both in terms of compression ratio and compression speed.

Visualisation of medical infrared image databases.

We introduce a method for visualising and navigating through a collection of medical infrared images. Multidimensional scaling is used to provide an overall picture of a given image database by projecting all images on a plane and arranging them so that images that are visually similar are placed close to each other. Navigation through the image set can then be performed by zooming into an area of interest which could correspond to images describing similar symptoms.

Weighted fuzzy classification with integrated learning method for medical diagnosis.

Medical diagnosis can be viewed as a pattern classification problem: based a set of input features the goal is to classify a patient as having a particular disorder or as not having it. Performance of medical diagnosis is typically assessed in terms of sensitivity and specificity. In this paper we introduce a pattern classification system for medical diagnosis that is based on fuzzy logic and utilises weighted training patterns.