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Test oracles based on metamorphic relations for image processing applications

Test oracles based on metamorphic relations for image proces...

ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Tahir Jameel Mengxiang Lin Liu Chao State key Lab of Software Development Environment Beihang University Beijing China

Testing of image processing applications is a challenging job especially, when evaluating the correctness of output image. Generally, output images are evaluated manually by visual inspection carried out by an expert tester, which is the main hindrance in automation of testing process. Recently, statistical and metamorphic testing approaches are presented to automate output evaluation of image processing applications. The statistical method is dependent on availability of statistical distribution of output images, whereas metamorphic testing require more research efforts to make it widely used in practice. Metamorphic testing is a well-known technique to alleviate the test oracle problem and eliminates the required manual efforts by using relations of input and output images. Follow-up test cases are generated based on these relations and their expected output is evaluated. This paper addresses test oracle problem for image processing applications and demonstrates how properties of implementation under test can be adopted as metamorphic relations. We have studied general and specific metamorphic relations of morphological image operations such as dilation and erosion. Selection of metamorphic relations and their effectiveness by mutation analysis is demonstrated. The results show that metamorphic testing is useful for evaluation of output images in the absence of a perfect test oracle.

关键词： Testing image processing Software algorithms Standards Smoothing methods Noise Software

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Model-based estimation of microscopic anisotropy in macroscopically isotropic substrates using diffusion MRI 24th

Model-based estimation of microscopic anisotropy in macrosco...

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24th International Conference on Information processing in Medical Imaging, IPMI 2015

作者： Ianuş, Andrada Drobnjak, Ivana Alexander, Daniel C. Centre for Medical Image Computing University College London London United Kingdom

Non-invasive estimation of cell size and shape is a key challenge in diffusion MRI. Changes in cell size and shape discriminate functional areas in the brain and can highlight different degrees of malignancy in cancer tumours. Consequently various methods have emerged recently that aim to measure the microscopic anisotropy of porous media such as biological tissue and aim to reflect pore eccentricity, the simplest shape feature. However, current methods assume a substrate of identical pores, and are strongly influenced by non-trivial size distribution. This paper presents a model-based approach that provides estimates of pore size and shape from diffusion MRI data. The technique uses a geometric model of randomly oriented finite cylinders with gamma distributed radii. We use Monte Carlo simulation to generate synthetic data in substrates consisting of randomly oriented cuboids with various size distributions and eccentricities. We compare the sensitivity of single and double pulsed field gradient (sPFG and dPFG) sequences to the size distribution and eccentricity and further compare different protocols of dPFG sequences with parallel and/or perpendicular pairs of gradients. The key result demonstrates that this model-based approach can provide features of pore shape (specifically eccentricity) that are independent of the size distribution unlike previous attempts to characterise microscopic anisotropy. We show further that explicitly accounting for size distribution is necessary for accurate estimates of average size and eccentricity, and a model that assumes a single size fails to recover the ground truth values. We find the most accurate parameter estimates for dPFG sequences with mixed parallel and perpendicular gradients, nevertheless all other sequences, including sPFG, show sensitivity as well. © Springer International Publishing Switzerland 2015.

关键词： Magnetic resonance imaging

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TH-CD-303-02: A GPU-Based Iterative image Reconstruction Solver with 4D Regularization for Low-Dose Helical 4DCT

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Medical Physics 2015年第6PART43期42卷 3728-3729页

作者： M Guo R Li L Xing H Gao Shanghai Jiao Tong University Shanghai Shanghai Stanford University Palo Alto CA Stanford Univ School of Medicine Stanford CA

Purpose: 4DCT is an important technique for motion management in radiotherapy, but involves a significant amount of dose to patients. Low-dose helical 4DCT scans can be done by (a) lowering tube current and pulse duration, (b) increasing helical pitch, and (c) reducing data sampling. Conventional FDK methods may fail for these low-dose scan protocols due to noisy or incomplete data. We aim to develop an accurate and fast image reconstruction solver that enables dose reduction for helical 4DCT. methods: For improved accuracy, the developed solver is based on a novel 4D regularization method for CT image reconstruction, which promotes both the smoothness in spatial variables for each phase and the similarity in temporal variables between phases, in contrast with the 3D regularization method with spatial regularization only. Both 3D and 4D methods here are based on total variation. The formulated optimization problem is solved by alternating direction method of multipliers (ADMM), which is chosen for its effectiveness for solving the convex problem, and more importantly its suitability for distributed *** improved speed, the developed solver utilizes an efficient parallel algorithm for X-ray transform and its adjoint with optimized computational cost per parallel thread O (1) that takes flying focal spots into account, and multi-GPU platforms for solving such a massive parallel computing problem in helical 4DCT. Results: The solver was validated using both simulations (4D NCAT phantoms) and experimental sinogram data (Siemens, Somatom 64-slices) with low-dose, high-pitch and sparse-view helical 4DCT scans. Both the reconstructed images and quantitative results demonstrate that the 4D method improved image quality compared with the 3D method, which is in turn better than FDK. Conclusion: We have developed a fast, accurate solver that synergizes state-of-art iterative reconstruction methods and parallel computing techniques that can potentially enable low-dose,

关键词： Medical image reconstruction parallel processing Computer simulation Three dimensional image processing Dosimetry Computed tomography Medical image contrast Optimization

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An Algebraic parallel Treecode in Arbitrary Dimensions

An Algebraic Parallel Treecode in Arbitrary Dimensions

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International Symposium on parallel and distributed processing (IPDPS)

作者： William B. March Bo Xiao Chenhan D. Yu George Biros Institute for Computational Engineering and Sciences The University of Texas at Austin Austin TX

We present a parallel tree code for fast kernel summation in high dimensions -- a common problem in data analysis and computational statistics. Fast kernel summations can be viewed as approximation schemes for dense kernel matrices. Tree code algorithms (or simply tree codes) construct low-rank approximations of certain off-diagonal blocks of the kernel matrix. These blocks are identified with the help of spatial data structures, typically trees. There is extensive work on tree codes and their parallelization for kernel summations in three dimensions, but there is little work on high-dimensional problems. Recently, we introduced a novel tree code, ASKIT, which resolves most of the shortcomings of existing methods. We introduce novel parallel algorithms for ASKIT, derive complexity estimates, and demonstrate scalability on synthetic, scientific, and image datasets. In particular, we introduce a local essential tree construction that extends to arbitrary dimensions in a scalable manner. We introduce data transformations for memory locality and use GPU acceleration. We report results on the "Maverick" and "Stampede" systems at the Texas Advanced Computing Centre. Our largest computations involve two billion points in 64 dimensions on 32, 768 x86 cores and 8 million points in 784 dimensions on 16, 384 x86 cores.

关键词： Kernel Approximation methods Skeleton Approximation algorithms Algorithm design and analysis Complexity theory Scalability

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Real-Time Depth-image-Based Rendering on GPU

Real-Time Depth-Image-Based Rendering on GPU

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International Conference on Cyber-Enabled distributed Computing and Knowledge Discovery, CyberC

作者： Zengzeng Sun Cheolkon Jung Samsung Electronics SRC-Xian DMC Lab/SSG Xi'an China School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781467392013

In this paper, we propose real-time depth-image-based rendering (DIBR) on GPU for 1280×720 resolution. We utilize depth adaptive preprocessing and super-resolution to achieve high-quality DIBR. Moreover, we employ GPU-based parallel computing to achieve real-time DIBR. Experimental results demonstrate that the proposed method achieves superior performance in comparison with the existing methods with respect to rendering quality and computing time.

关键词： Three-dimensional displays Graphics processing units Rendering (computer graphics) Instruction sets Real-time systems Videos Filling

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Classification of Tiangong-1 hyperspectral remote sensing image via contextual sparse coding

Classification of Tiangong-1 hyperspectral remote sensing im...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi Lv Yong Dou Xin Niu Jiaqing Xu Jinbo Xu School of Computer National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781467372220

The hyperspectral remote sensing is one of the frontier techniques in the remote sensing research fields. Applying the sparse coding model to the hyperspectral remote sensing image processing is a hot topic in hyperspectral information processing. To improve the accuracy of hyperspectral image classification, we propose a classification method based on the spatial-spectral join-t contextual sparse coding. Firstly, a dictionary is obtained by training using samples selected from the ground-truth reference data. Then, the sparse coefficients of each pixel are calculated based on the learned dictionary. Afterward, the sparse coefficients are input to the classifier and the final classification result is obtained. The visible and near-infrared hyperspectral remote sensing image collected by Tiangong-1 in Chaoyang District of Beijing is used to evaluate the performance of the proposed approach. Experimental results show that the proposed method yields the best classification performance with the overall accuracy of 95.74% and the Kappa coefficient of 0.9476 in comparison with other classification methods.

关键词： Hyperspectral image Remote sensing Sparse coding Sparse coding Classification methods remote sensing hyperspectral remote sensing hyperspectral imagery image classification

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Permutation enhanced parallel reconstruction for compressive sampling

Permutation enhanced parallel reconstruction for compressive...

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IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive processing (CAMSAP)

作者： Hao Fang Serigy A. Vorobyov Hai Jiang Department of Electrical Engineering University of Washington Seattle WA USA Department of Signal Processing and Acoustics Aalto University AALTO Finland Department of Electrical and Computer Engineering University of Alberta AB Canada

In this paper, a simple but efficient permutation enhanced parallel reconstruction architecture for compressive sampling (CS) is proposed. In this architecture, a measurement matrix is constructed from a block-diagonal sensing matrix, the sparsifying basis of the target signal, and a pre-defined permutation matrix. In this way, the projection of the signal onto the sparsifying basis can be divided into several segments and all segments can be reconstructed in parallel. Thus, the computational complexity and the time for reconstruction can be reduced significantly. With a good permutation matrix, the error performance of the proposed method can be improved compared with the option without permutation. The proposed method can be used in applications where the computational complexity and time for reconstruction are crucial evaluation criteria and centralized sampling is acceptable. Simulation results show that the proposed method can achieve comparable results to the centralized reconstruction methods (i.e., standard CS and distributed CS), while requiring much less reconstruction time.

关键词： Sensors image reconstruction Sparse matrices Computational complexity Decoding Computer architecture Simulation

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Robust Weighted Supervised Sparse Coding for image Classification

Robust Weighted Supervised Sparse Coding for Image Classific...

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2015 IEEE 16th International Conference on Communication Technology(ICCT 2015)

作者： Xiang Zhang Naiyang Guan Zhigang Luo College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding method(RWSSC) to address this ***, RWSSC distinguishes different classes' contributions to the sparse coding by a novel weighting strategy meanwhile removes the out liers by imposing1 l-regularization over the noisy entries. Benefitting from these strategies, RWSSC can effectively boost performance of sparse coding in image ***, we developed the block coordinate descent algorithm to optimize it, and proved its *** results of image classification on two popular datasets show that RWSSC outperforms the representative sparse coding methods in quantities.

关键词： Sparse coding Supervised learning image classification

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Robust weighted supervised sparse coding for image classification

Robust weighted supervised sparse coding for image classific...

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International Conference on Communication Technology (ICCT)

作者： Xiang Zhang Naiyang Guan Zhigang Luo College of Computer National University of Defense Technology Changsha China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (纸本)9781467370066

Sparse coding has shown its great potential in learning image feature representation. Recent developed methods such as group sparse coding prefer discovering the group relationships among examples and have achieved the state-of-the-art results in image classification. However, they suffer from poor robustness shortcomings in practice. This paper proposes a robust weighted supervised sparse coding method (RWSSC) to address this deficiency. Particularly, RWSSC distinguishes different classes' contributions to the sparse coding by a novel weighting strategy meanwhile removes the out liers by imposing l1-regularization over the noisy entries. Benefitting from these strategies, RWSSC can effectively boost performance of sparse coding in image classification. Besides, we developed the block coordinate descent algorithm to optimize it, and proved its convergence. Experimental results of image classification on two popular datasets show that RWSSC outperforms the representative sparse coding methods in quantities.

关键词： Support vector machines Robustness Classification algorithms Dictionaries Manganese

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基于MPI的大规模遥感影像金字塔并行构建方法

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地球信息科学学报 2015年第5期17卷 515-522页

作者：赫高进熊伟陈荦吴秋云景宁国防科学技术大学电子科学与工程学院长沙410073

影像金字塔是实现影像数据多分辨率组织的重要方式,是提高影像可视化性能的有效手段。传统串行金字塔构建算法,对大规模影像数据的构建性能已无法满足遥感影像快速浏览的预处理需求。故此,其成为一个亟待解决的问题,而利用多核、多节点... 详细信息

影像金字塔是实现影像数据多分辨率组织的重要方式,是提高影像可视化性能的有效手段。传统串行金字塔构建算法,对大规模影像数据的构建性能已无法满足遥感影像快速浏览的预处理需求。故此,其成为一个亟待解决的问题,而利用多核、多节点的高性能集群计算环境和并行机制是一个重要的技术途径。本文在共享外存的高性能集群环境下,提出使用消息传递接口(MPI)的金字塔并行构建算法,对构建遥感影像金字塔过程中的重采样与I/O过程进行并行处理,大大缩短了遥感影像金字塔构建时间。实验结果表明:(1)该算法比传统串行构建方法的加速效果明显,对于单波段遥感影像,其加速效果可达到GDAL的5倍以上,而对于多波段遥感影像,加速效果可达到GDAL的2倍以上;(2)遥感影像数据量越大,并行构建算法加速效果越显著,对于大规模的遥感影像,本文提出的金字塔并行构建算法的速度可达到GDAL的10倍左右。

关键词：遥感影像金字塔消息传递接口并行集群

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