Calculation of mean, variance and standard deviation are often required for segmentation or feature extraction. In imageprocessing, often an integer approximation is adequate. Conventional methods require division an...
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Calculation of mean, variance and standard deviation are often required for segmentation or feature extraction. In imageprocessing, often an integer approximation is adequate. Conventional methods require division and square root operations, which are expensive to realize in hardware in terms of both the amount of required resources and latency. A new class of iterative algorithms is developed based on integer arithmetic. An implementation of the algorithms as a hardware architecture for a Field-Programmable Gate Array (FPGA) is compared with architectures using the conventional approach, which shows a significantly reduced latency while using less hardware resources.
We propose a decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising in the design of wireless multi-user interfering systems. Our main contributions are: i) the dev...
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ISBN:
(纸本)9781479903573
We propose a decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising in the design of wireless multi-user interfering systems. Our main contributions are: i) the development of the first provably convergent Jacobi best-response algorithm, where all users simultaneously solve a suitably convexified version of the original sum-utility optimization problem;ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics;and iii) a framework that can be easily particularized to well-known applications, giving rise to practical algorithms that outperform all existing ad-hoc methods proposed for very specific problems. Our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many block-coordinate descents schemes for convex functions.
Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance betwe...
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ISBN:
(纸本)9781479906505
Nonnegative matrix factorization (NMF) decomposes a nonnegative dataset X into two low-rank nonnegative factor matrices, i.e., W and H, by minimizing either Kullback-Leibler (KL) divergence or Euclidean distance between X and WH. NMF has been widely used in pattern recognition, data mining and computer vision because the non-negativity constraints on both W and H usually yield intuitive parts-based representation. However, NMF suffers from two problems: 1) it ignores geometric structure of dataset, and 2) it does not explicitly guarantee partsbased representation on any datasets. In this paper, we propose an orthogonal nonnegative locally linear embedding (ONLLE) method to overcome aforementioned problems. ONLLE assumes that each example embeds in its nearest neighbors and keeps such relationship in the learned subspace to preserve geometric structure of a dataset. For the purpose of learning parts-based representation, ONLLE explicitly incorporates an orthogonality constraint on the learned basis to keep its spatial locality. To optimize ONLLE, we applied an efficient fast gradient descent (FGD) method on Stiefel manifold which accelerates the popular multiplicative update rule (MUR). The experimental results on real-world datasets show that FGD converges much faster than MUR. To evaluate the effectiveness of ONLLE, we conduct both face recognition and image clustering on real-world datasets by comparing with the representative NMF methods.
Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high...
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ISBN:
(纸本)9780819492791
Thanks to the recent technological advances, a large variety of image data is at our disposal with variable geometric, radiometric and temporal resolution. In many applications the processing of such images needs high performance computing techniques in order to deliver timely responses e. g. for rapid decisions or real-time actions. Thus, parallel or distributed computing methods, Digital Signal Processor (DSP) architectures, Graphical processing Unit (GPU) programming and Field-Programmable Gate Array (FPGA) devices have become essential tools for the challenging issue of processing large amount of geo-data. The article focuses on the processing and registration of large datasets of terrestrial and aerial images for 3D reconstruction, diagnostic purposes and monitoring of the environment. For the image alignment procedure, sets of corresponding feature points need to be automatically extracted in order to successively compute the geometric transformation that aligns the data. The feature extraction and matching are ones of the most computationally demanding operations in the processing chain thus, a great degree of automation and speed is mandatory. The details of the implemented operations (named LARES) exploiting parallel architectures and GPU are thus presented. The innovative aspects of the implementation are (i) the effectiveness on a large variety of unorganized and complex datasets, (ii) capability to work with high-resolution images and (iii) the speed of the computations. Examples and comparisons with standard CPU processing are also reported and commented.
Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. image reconstruction in these systems is usually performed by app...
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Magnetic resonance imaging (MRI) is currently widely used in medical image diagnosis. However, MR scanners are extensively used in clinics and thus are rarely accessible for experimentation. In consequence, the number...
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ISBN:
(纸本)9788372835024
Magnetic resonance imaging (MRI) is currently widely used in medical image diagnosis. However, MR scanners are extensively used in clinics and thus are rarely accessible for experimentation. In consequence, the number of images available for imageprocessingmethods evaluation is too low and there appears a need for a method to generate synthetic images. In their previous works, the authors studied various methods for blood vessels segmentation and tracking. Effectiveness of the designed algorithms requires objective verification which implies repetition of experiments for large number of images and comparing the results with some ground truth models. Therefore, this study aims at designing a computer system which implements numerical routines for generation of synthetic MRA images. In particular, in this paper we study the performance of various configurations of assembled computer grid and analyze their potential in angiographic image synthesis.
This paper presents a rebinning method for the reconstruction from Compton scattered conical projection data. Our rebinning method converts the 3-D conical projection data set into a stack of 2-D parallel projection d...
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ISBN:
(纸本)9781467320306;9781467320283
This paper presents a rebinning method for the reconstruction from Compton scattered conical projection data. Our rebinning method converts the 3-D conical projection data set into a stack of 2-D parallel projection data set so that a variety of existing 2-D analytical reconstruction methods developed for conventional emission tomography can be directly used for Compton imaging. In our rebinning method, the following three steps are performed. (i) A cone surface is sampled with a serial of lines that pass through the apex of the cone. (ii) By using the rotating imaginary planes, which are perpendicular to the transaxial (x-y) plane and parallel to the z axis, the sampled projection lines, which are mostly perpendicular to the imaginary planes, are sorted at each rotation angle. (iii) The sorted lines, most of which are oblique in the transaxial plane, are approximated to equivalent 2-D parallel projections in the transaxial plane using the Fourier rebinning (FORE) technique. Since our method is very fast, it can be useful for rapidly reconstructing Compton scattered data using fast analytical reconstruction methods.
The proceedings contain 361 papers. The topics discussed include: cross-strait information integration application strategy planning-a case study of Kinmen-Xiamen;the observability analysis of aerial moving target loc...
ISBN:
(纸本)9781467314114
The proceedings contain 361 papers. The topics discussed include: cross-strait information integration application strategy planning-a case study of Kinmen-Xiamen;the observability analysis of aerial moving target location based on dual-satellite geolocation system;application of digital imageprocessing in the measurement of casting surface roughness;study of wavelet denoising in images of vacuum switching arc;research on trust mechanism in military information grid;a new algorithm for packing unequal disks in a larger circle;new tool radius compensation algorithm and implementation;parallel decoupling algorithm for solving the block tri-diagonal linear equations;methods analysis of the liquid ion-exchanged to manufacture the optical waveguide on the glass based;experimental study on quantum data stream cipher using homodyne detection;and the optimal model reduction method for spatially distributed system based on simulated annealing algorithm.
The processing of microscopic tissue images and especially the detection of cell nuclei is nowadays done more and more using digital imagery and special immunodiagnostic software products. Since several methods (and a...
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Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. image reconstruction in these systems is usually performed by app...
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Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. Currently there are a strong need to speed-up the reconstruction of XRay CT data in order to extend its clinical applications. We present an efficient modular implementation of an FDK-based reconstruction algorithm that takes advantage of the parallel computing capabilities and the efficient bilinear interpolation provided by general purpose graphic processing units (GPGPU). The proposed implementation of the algorithm is evaluated for a high-resolution micro-CT and achieves a speed-up of 46, while preserving the reconstructed image quality.
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