This paper describes a method of computing volume for 3D objects bounded by Bézier surfaces using example models of biomedical origin. The authors present three different theorems for volume calculation, based, b...
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This two-volume set CCIS 751 and CCIS 752 constitutes the proceedings of the 17th Asia Simulation conference, AsiaSim 2017, held in Malacca, Malaysia, in August/September 2017. The 124 revised full papers presented in...
ISBN:
(数字)9789811065026
ISBN:
(纸本)9789811065019
This two-volume set CCIS 751 and CCIS 752 constitutes the proceedings of the 17th Asia Simulation conference, AsiaSim 2017, held in Malacca, Malaysia, in August/September 2017. The 124 revised full papers presented in thistwo-volume setwere carefully reviewed and selected from 267 submissions. The papers contained in these proceedings address challenging issues in modeling and simulation in various fields such as embedded systems; symbiotic simulation; agent-based simulation; parallel and distributed simulation; high performance computing; biomedical engineering; big data; energy, society and economics; medical processes; simulation language and software; visualization; virtual reality; modeling and Simulation for IoT; machine learning; as well as the fundamentals and applications of computing.
This work presents an improved interactive data visualization interface based on a mixture of the outcomes of dimensionality reduction (DR) methods. Broadly, it works as follows: The user can input the mixture weighti...
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ISBN:
(纸本)9783319597737;9783319597720
This work presents an improved interactive data visualization interface based on a mixture of the outcomes of dimensionality reduction (DR) methods. Broadly, it works as follows: The user can input the mixture weighting factors through a visual and intuitive interface with a primary-light-colors-based model (Red, Green, and Blue). By design, such a mixture is a weighted sum of the color tone. Additionally, the low-dimensional representation space produced by DR methods are graphically depicted using scatter plots powered via an interactive data-driven visualization. To do so, pairwise similarities are calculated and employed to define the graph to simultaneously be drawn over the scatter plot. Our interface enables the user to interactively combine DR methods by the human perception of color, while providing information about the structure of original data. Then, it makes the selection of a DR scheme more intuitive -even for non-expert users.
In the context of laparoscopic interventions involving intracorporeal ultrasound, we present a method to visualize hidden targets in 3D. As the surgeon scans the organ surface, we stitch tracked 2D ultrasound images i...
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In this work, we decompose a first-person action into verb and noun. We then study how the coupling of an action's constituent verb and noun affects the learners' ability to learn them separately and to combin...
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ISBN:
(纸本)9781509048229
In this work, we decompose a first-person action into verb and noun. We then study how the coupling of an action's constituent verb and noun affects the learners' ability to learn them separately and to combine them to perform recognition. We compare different information fusion methods on conventional action recognition and zero-shot learning, of which the latter is a strong indication of the feature's ability to capture one concept (verb/noun) and not be confounded by the other. To achieve the decoupling of verb/noun concepts, we extract features that are specialized for each of them. Specifically, we use improved dense trajectories and convolutional neural network activations. We show that by constructing specialized features for the decomposed concepts, our method succeeds in zero-shot learning. More surprisingly, it also outperforms previous results in conventional action recognition when the performance gaps of different features on verb/noun concepts are significant.
Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The...
Three-dimensional reconstruction of medical images plays a vital role in clinical medicine and medical research. It serves as a powerful tool to help doctors analyze multiple 2D medical images in a 3D perspective. The developed 3D model offers a faster and accurate approach in clinical diagnosis and therapy. Before, the development of 3D reconstruction programs it has always been a challenge to visualize in a 3D perspective but with the advent of powerful computing and graphics processing, medical imaging equipment development is on the rise. The advancement of 3D image reconstruction is not only due to the innovative hardware, but also because of the increase in open-source platforms or libraries, such as visualization toolkit (VTK), that assist on its development to provide an information visualization framework and advanced 3D modeling techniques. VTK is an object-oriented 3D graphics software program that is free to obtain and used both for open- and closed-source applications such as 3D computer graphics, image processing, and visualization. In medical imaging, data from ultrasound systems, magnetic resonance imaging (MRI), and computed tomography (CT) scans can be managed in VTK. Different algorithms of image processing such as color mapping, image re-slicing or resampling, iso-contouring, thresholding, and other algorithms can be utilized to make volume images from the data. Subsequently, data presentation is simplified with the data reconstruction through the use of supplemental software for analysis. VTK library contains a large number of filters, which are multi-threaded to facilitate parallel processing and interpretation. In this paper, recent technologies on the application of VTK for 3D reconstruction of biomedical images will be presented. Moreover, current methodologies will be discussed to clearly understand the different integral steps needed in image data conversion.
A fundamental property of movement is its dynamically changing variability and its adaptive nature. These features seem to be connected to the cognitive control of our actions by the brain. However, it has been a chal...
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As the second leading cause of blindness worldwide, glaucoma impacts a large population of individuals over 40. Although visual acuity often remains unaffected in early stages of the disease, visual field loss, expres...
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ISBN:
(纸本)9783981537093
As the second leading cause of blindness worldwide, glaucoma impacts a large population of individuals over 40. Although visual acuity often remains unaffected in early stages of the disease, visual field loss, expressed by tunnel vision condition, gradually increases. Glaucoma often remains undetected until it has moved into advanced stages. In this paper, we introduce a wearable system for automatic tunnel vision detection using head-mounted sensors and machine learning techniques. We develop several tasks, including reading and observation, and estimate visual field loss by analyzing user's head movements while performing the tasks. An integrated computational module takes sensor signals as input, passes the data through several automatic data processing phases, and returns a final result by merging task-level predictions. For validation purposes, a series of experiments is conducted with 10 participants using tunnel vision simulators. Our results demonstrate that the proposed system can detect mild and moderate tunnel visions with an accuracy of 93.3% using a leave-one-subject-out analysis.
This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first traine...
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ISBN:
(纸本)9781509066223
This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first trained to recognize and classify fundus images of diabetic and non-diabetic patients. The network is then visualized, using a technique of pixel optimization, to discover the features that the trained network looks for to classify the image. Through this novel application of network visualization, we show that critical features for diabetic retinopathy can be re-discovered, leaving great scope for its application in scarcely explored diseases using minimal resources.
Biological networks and visualization of the frequent terms in networks offers a key scenario to visualize and link the biological terms, which are representative of the physiological experiment. Plethora of the publi...
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ISBN:
(纸本)9781509045006
Biological networks and visualization of the frequent terms in networks offers a key scenario to visualize and link the biological terms, which are representative of the physiological experiment. Plethora of the publications recently using the RNA-Seq has widely elucidated the organismal biology. However, the embedded annotations are dispersed across representative publications and present end-user a cumbersome task of visually looking at annotations files for the over-represented categories. In the present paper, we present an R package for the visualization of the most frequent annotation terms using math curve functions. The developed pack-age allows for the visualization of the abundant terms in clustered way using weighted indices and present the visualization of the abundance of the terms in relation to word size, font and coloring as per the frequency of the observed word, prettycloud is implementegd in R, and is supported on Windows, Linux and MAC OSX, and freely available at https://***/ projects/prettycloud/or http://***/research/software/*** (Figure 1).
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