Presents a review of 200 references in content-based image retrieval. The paper starts with discussing the working conditions of content-based retrieval: patterns of use, types of pictures, the role of semantics, and the sensory gap. Subsequent sections discuss computational steps for image retrieval systems. Step one of the review is image processing for retrieval sorted by color, texture, and local geometry. Features for retrieval are discussed next, sorted by: accumulative and global features, salient points, object and shape features, signs, and structural combinations thereof. Similarity of pictures and objects in pictures is reviewed for each of the feature types, in close connection to the types and means of feedback the user of the systems is capable of giving by interaction. We briefly discuss aspects of system engineering: databases, system architecture, and evaluation. In the concluding section, we present our view on: the driving force of the field, the heritage from computer vision, the influence on computer vision, the role of similarity and of interaction, the need for databases, the problem of evaluation, and the role of the semantic gap.

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Content-based image retrieval at the end of the early years

With today’s large increase in digital images and automatically generated imagery, such as videos and stills generated from surveillance equipment, the need for efficient image retrieval and indexing has become fundamental. Since text-based information retrieval has been shown to perform very poorly when searching through images, research has been active in the field of content-based image retrieval (CBIR). CBIR systems make use of the properties of images in order to compare them and extract content by matching the query image. Comparing features – such as color, texture, and shape – allows for better retrieval accuracy; however, the algorithms used are still very limited. This paper will provide a survey of CBIR systems and explain the fundamental properties and techniques used in these systems. First, the history of CBIR systems will be discussed together with some typical CBIR systems. After this, the paper will touch on text-based information retrieval and explain why it does not work for searching through collections of images. The latter portion of this document will provides an overview of a typical CBIR system and the main techniques involved in querying such a system. Finally, image features and indexing schemes will be described.

Content-Based Image Retrieval

Learners need to have good reasons to engage and accept e-learning. They need to understand that unless they do, the outcomes will be less favourable. The technology acceptance model (TAM) is the most widely recognized model addressing why users accept or reject technology. This study describes the development and evaluation of a virtual environment, the online 3D world Second Life (SL), for learning rapid sequence intubation (RSI). RSI is an increasingly frequently used method of acute airway management in healthcare settings. The intention of learners to use the system was explored based on the TAM, with the computer self-efficacy construct as an external variable. Two hundred and six nursing students participated in this study. The findings suggest that the system was perceived as useful, and that the students felt confident working with computers and intended to review RSI in SL as often as needed. However, they remained neutral regarding the ease of use of the system. Strategies were suggested for boosting the students' self-confidence in using the system. Overall use of the TAM in this context was successful, indicating the robustness of the model. The limitations of the study were discussed and further areas of research on the TAM were proposed.

Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education

Until very recently, we considered Virtual Reality as something that was very close, but it was still science fiction However, today Virtual Reality is being integrated into many different areas of our lives, from videogames to different industrial use cases and, of course, it is starting to be used in medicine There are two great general classifications for Virtual Reality Firstly, we find a Virtual Reality in which we visualize a world completely created by computer, three-dimensional and where we can appreciate that the world we are visualizing is not real, at least for the moment as rendered images are improving very fast Secondly, there is a Virtual Reality that basically consists of a reflection of our reality This type of Virtual Reality is created using spherical or 360 images and videos, so we lose three-dimensional visualization capacity (until the 3D cameras are more developed), but on the other hand we gain in terms of realism in the images We could also mention a third classification that merges the previous two, where virtual elements created by computer coexist with 360 images and videos In this article we will show two systems that we have developed where each of them can be framed within one of the previous classifications, identifying the technologies used for their implementation as well as the advantages of each one We will also analize how these systems can improve the current methodologies used for medical training The implications of these developments as tools for teaching, learning and training are discussed

Virtual Reality as an Educational and Training Tool for Medicine

Background: A three-dimensional virtual world (3DVW) is a computer-simulated electronic 3D virtual environment that users can explore, inhabit, communicate, and interact with via avatars, which are graphical representations of the users. Since the early 2000s, 3DVWs have emerged as a technology that has much to offer the health care sector. Objective: The purpose of this study was to characterize different application areas of various 3DVWs in health and medical context and categorize them into meaningful categories. Methods: This study employs a systematic literature review on the application areas of 3DVWs in health care. Our search resulted in 62 papers from five top-ranking scientific databases published from 1990 to 2013 that describe the use of 3DVWs for health care specific purposes. We noted a growth in the number of academic studies on the topic since 2006. Results: We found a wide range of application areas for 3DVWs in health care and classified them into the following six categories: academic education, professional education, treatment, evaluation, lifestyle, and modeling. The education category, including professional and academic education, contains the largest number of papers (n=34), of which 23 are related to the academic education category and 11 to the professional education category. Nine papers are allocated to treatment category, and 8 papers have contents related to evaluation. In 4 of the papers, the authors used 3DVWs for modeling, and 3 papers targeted lifestyle purposes. The results indicate that most of the research to date has focused on education in health care. We also found that most studies were undertaken in just two countries, the United States and the United Kingdom. Conclusions: 3D virtual worlds present several innovative ways to carry out a wide variety of health-related activities. The big picture of application areas of 3DVWs presented in this review could be of value and offer insights to both the health care community and researchers.

A Decade of Research on the Use of Three-Dimensional Virtual Worlds in Health Care: A Systematic Literature Review

This article explores immersive virtual reality as a potential educational strategy for nursing education and describes an immersive learning experience now being developed for nurses. This pioneering project is a virtual reality application targeting speed and accuracy of nurse response in emergency situations requiring cardiopulmonary resuscitation. Other potential uses and implications for the development of virtual reality learning programs are discussed.

Immersive virtual reality simulations in nursing education.

https://www.cs.ou.edu/~database/documents/bg98a.pdf

Tree-Based Indexes for Image Data

To predict NFL game outcomes, we examine the application of technical stock market techniques to sentiment gathered from social media. From our analysis we found a $14.84 average return per sentiment-based wager compared to a $12.21 average return loss on the entire 256 games of the 20152016 regular season if using an odds-only approach. We further noted that wagers on underdogs (i.e., the less favored teams) that exhibit a golden cross pattern in sentiment (e.g., the most recent sentiment signal crosses the longer baseline sentiment), netted a $48.18 return per wager on 41 wagers. These results show promise of cross-domain research and we believe that applying stock market techniques to sports wagering may open an entire new research area. Gathered NFL tweet sentiment to predict outcomes and wagering decisionsUsed difference in moving averages for optimal wagering return ($14.84/game)Sentiment exhibiting a golden cross netted $48.18/game returnsFans were able to signal winning teams by their change in sentiment.

Prediction from regional angst A study of NFL sentiment in Twitter using technical stock market charting

https://isiarticles.com/bundles/Article/pre/pdf/26139.pdf

A Stochastic Dynamic Programming Approach for the Equipment Replacement Optimization under Uncertainty

In order to more efficiently represent image editing in multimedia databases, research has been performed to determine the usefulness of storing one image file as a base along with a set of operations that represent the modifications to the image that occured during editing. Because any editor should be able to produce and interpret them, these operations should be elements of some standardized set. One of the properties that this standardized set must have is completeness so that it is able to represent any of the modifications a software editor may produce. In this paper, we propose a method for testing whether or not any general set of image operations is complete. Part of this proposal includes formal definitions for both images and completeness. In addition, an example of this test is presented for a set of image processing operations.

/pdf/testing-a-set-of-image-processing-operations-for-yznggzhi6d.pdf

Leonard Brown

Papers

Immersive virtual reality simulations in nursing education.

Tree-Based Indexes for Image Data

Prediction from regional angst A study of NFL sentiment in Twitter using technical stock market charting

A Stochastic Dynamic Programming Approach for the Equipment Replacement Optimization under Uncertainty

Testing a Set of Image Processing Operations for Completeness