The effects of the aft rotor on the inter-rotor flow field of an open rotor propulsion rig were examined. A Particle image Velocimetry (PIV) dataset that was acquired phase locked to the front rotor position has been ...
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ISBN:
(纸本)9780791849682
The effects of the aft rotor on the inter-rotor flow field of an open rotor propulsion rig were examined. A Particle image Velocimetry (PIV) dataset that was acquired phase locked to the front rotor position has been phase averaged based on the relative phase angle between the forward and aft rotors. The aft rotor phase was determined by feature tracking in raw PIV images through an imageprocessing algorithm. The effects of the aft rotor potential field on the inter-rotor flow were analyzed and shown to be in reasonably good agreement with Computational Fluid Dynamics (CFD) simulations. The aft rotor position was shown to have a significant upstream effect, with implications for front rotor interaction noise. It was found that the aft rotor had no substantial effect on the position of the forward rotor tip vortex but did have a small effect on the circulation strength of the vortex when the rotors were highly loaded.
Most of the survey techniques used in archaeology and architecture are currently focused on range-data (laser scanning) and image-based systems (digital photogrammetry/photoscanning). The paper aims to highlight a dif...
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Adaptive radiotherapy is a technique intended to increase the accuracy of radiotherapy. Currently, it is not clinically feasible due to the time required to process the images of patient anatomy. Hardware acceleration...
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ISBN:
(纸本)9782839918442
Adaptive radiotherapy is a technique intended to increase the accuracy of radiotherapy. Currently, it is not clinically feasible due to the time required to process the images of patient anatomy. Hardware acceleration of imageprocessingalgorithms may allow them to be carried out in a clinically acceptable timeframe. This paper presents the experiences encountered using high-level synthesis tools to design an accelerated segmentation algorithm for computed tomography images targeted for implementation on a System on Chip. Hardware coprocessors and their interfaces for optimal threshold generation and 3D mean filter algorithms were synthesised from C++ functions. Hardware acceleration significantly outperformed the software only implementation. The high-level synthesis tools allowed the rapid exploration of different design options. However, hardware design knowledge was still necessary in order to interpret the results effectively.
Given a set S of multidimensional objects and a query object q, a k nearest neighbor (kNN) query finds from S the k closest objects to q. This query is a fundamental problem in database, data mining, and information r...
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Given a set S of multidimensional objects and a query object q, a k nearest neighbor (kNN) query finds from S the k closest objects to q. This query is a fundamental problem in database, data mining, and information retrieval research. It plays an important role in a wide spectrum of real applications such as image recognition and location-based services. However, due to the failure of data transmission devices, improper storage, and accidental loss, incomplete data exist widely in those applications, where some dimensional values of data items are missing. In this paper, we systematically study incomplete k nearest neighbor (IkNN) search, which aims at the kNNquery for incomplete data. We formalize this problem and propose an efficient lattice partition algorithm using our newly developed L alpha B index to support exact IkNN retrieval, with the help of two pruning heuristics, i.e., a value pruning and partial distance pruning. Furthermore, we propose an approximate algorithm, namely histogram approximate, to support approximate IkNN search with improved search efficiency and guaranteed error bound. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of newly designed indexes and pruning heuristics, as well as the performance of our presented algorithms under a variety of experimental settings.
Brain tumor segmentation is an important task in medical imageprocessing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. ...
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Brain tumor segmentation is an important task in medical imageprocessing. Early diagnosis of brain tumors plays an important role in improving treatment possibilities and increases the survival rate of the patients. Manual segmentation of the brain tumors for cancer diagnosis, from large amount of MRI images generated in clinical routine, is a difficult and time consuming task. There is a need for automatic brain tumor image segmentation. The purpose of this paper is to provide a review of MRI-based brain tumor segmentation methods. Recently, automatic segmentation using deep learning methods proved popular since these methods achieve the state-of-the-art results and can address this problem better than other methods. Deep learning methods can also enable efficient processing and objective evaluation of the large amounts of MRI-based image data. There are number of existing review papers, focusing on traditional methods for MRI-based brain tumor image segmentation. Different than others, in this paper, we focus on the recent trend of deep learning methods in this field. First, an introduction to brain tumors and methods for brain tumor segmentation is given. Then, the state-of-the-art algorithms with a focus on recent trend of deep learning methods are discussed. Finally, an assessment of the current state is presented and future developments to standardize MRI-based brain tumor segmentation methods into daily clinical routine are addressed. (C) 2016 The Authors. Published by Elsevier B.V.
In the last few years, integrated multi-modality systems have been developed, aimed at improving the accuracy of medical diagnosis correlating information from different imaging techniques. In this contest, a novel du...
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In the last few years, integrated multi-modality systems have been developed, aimed at improving the accuracy of medical diagnosis correlating information from different imaging techniques. In this contest, a novel dual modality probe is proposed, based on an ultrasound detector integrated with a small field of view single photon emission gamma camera. The probe, dedicated to visualize small organs or tissues located at short depths, performs dual modality images and permits to correlate morphological and functional information. The small field of view gamma camera consists of a continuous NaI:Tl scintillation crystal coupled with two multi-anode photomultiplier tubes. Both detectors were characterized in terms of position linearity and spatial resolution performances in order to guarantee the spatial correspondence between the ultrasound and the gamma images. Finally, dual-modality images of custom phantoms are obtained highlighting the good co-registration between ultrasound and gamma images, in terms of geometry and imageprocessing, as a consequence of calibration procedures.
The problem of obtaining 3-D tomographic images from geometries involving sparse sets of illuminators and detectors arises in applications like digital breast tomosynthesis, security inspection, non-destructive evalua...
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image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms a...
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ISBN:
(纸本)9781467388511
image segmentation is a key component in many computer vision systems, and it is recovering a prominent spot in the literature as methods improve and overcome their limitations. The outputs of most recent algorithms are in the form of a hierarchical segmentation, which provides segmentation at different scales in a single tree-like structure. Commonly, these hierarchical methods start from some low-level features, and are not aware of the scale information of the different regions in them. As such, one might need to work on many different levels of the hierarchy to find the objects in the scene. This work tries to modify the existing hierarchical algorithm by improving their alignment, that is, by trying to modify the depth of the regions in the tree to better couple depth and scale. To do so, we first train a regressor to predict the scale of regions using mid-level features. We then define the anchor slice as the set of regions that better balance between over-segmentation and under-segmentation. The output of our method is an improved hierarchy, re-aligned by the anchor slice. To demonstrate the power of our method, we perform comprehensive experiments, which show that our method, as a post-processing step, can significantly improve the quality of the hierarchical segmentation representations, and ease the usage of hierarchical image segmentation to high-level vision tasks such as object segmentation. We also prove that the improvement generalizes well across different algorithms and datasets, with a low computational cost.
With the development of multimedia processing and applications, multiple types of media security and forensic have been broadly taken into consideration. The media data includes audio, video, graphic, image and etc. T...
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This paper proposes a novel model, called Similarity Based on Visual Attention Features (SimVisual), to enhance the similarity analysis between images by considering features extracted from salient regions mapped by v...
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ISBN:
(纸本)9781467390361
This paper proposes a novel model, called Similarity Based on Visual Attention Features (SimVisual), to enhance the similarity analysis between images by considering features extracted from salient regions mapped by visual attention models. Visual attention models have demonstrated to be very useful for encoding perceptual semantic information of the image content. Thus, aggregating saliency features into the final image representation is a powerful asset to enhance the similarity analysis between images, while increasing the accuracy in retrieval tasks. The goal of SimVisual is to combine different saliency models with traditional image descriptors, aimed at increasing the descriptive power of these descriptors without modifying the original algorithms. We performed some experiments using a large dataset composed of 32 different biomedical images categories, and the results show that SimVisual boosts the retrieval accuracy up to 13% considering simple image descriptors, such as Color Histograms. The experiments on SimVisual shows that it is a valuable approach to increase the efficacy of content-based image retrieval systems, without user interactions.
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