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19th International Conference on image Analysis and processing (ICIAP)

作者： Amirmazlaghani, Maryam Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran

ISBN: (纸本)9783319685489;9783319685472

In this paper, we propose a novel watermark detector in contourlet domain using likelihood ratio test (LRT). Since the accuracy of an LRT based watermark detector is dependent on the efficiency of the applied statistical model, first, we study the statistical properties of the contourlet coefficients. Using different tests, we demonstrate that the marginal distribution of contourlet coefficients is heavy-tailed and heteroscedasticity exists in these coefficients. All of the previously proposed models for contourlet coefficients assume that these coefficients are identically distributed, so they can not capture the characteristics of the contourlet coefficients. To overcome this problem, we propose using two dimensional generalized autoregressive conditional heteroscedasticity (2D-GARCH) model for contourlet coefficients that provides an efficient structure for the dependencies of these coefficients. Based on using 2D-GARCH model, a novel LRT based heteroscedastic watermark detector is designed in contourlet domain. Experimental results confirm the efficiency of the proposed watermark detector under different types of attacks and its outperformance compared with alternative watermarking methods.

关键词： image watermarking Contourlet transform Likelihood ratio test 2D-GARCH model

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Simulation of continuously logical base cells (CL BC) with advanced functions for analog-to-digital converters and image processors 2

Simulation of continuously logical base cells (CL BC) with a...

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Conference on Emerging Imaging and Sensing Technologies for Security and Defence ii

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, Diana V. Vinnitsa Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnitsa Ukraine Vinnitsa Natl Tech Univ Vinnitsa Ukraine

ISBN: (数字)9781510613416

ISBN: (纸本)9781510613416;9781510613409

The paper considers results of design and modeling of continuously logical base cells (CL BC) based on current mirrors (CM) with functions of preliminary analogue and subsequent analogue-digital processing for creating sensor multichannel analog-to-digital converters (SMC ADCs) and image processors (IP). For such with vector or matrix parallel inputs-outputs IP and SMC ADCs it is needed active basic photosensitive cells with an extended electronic circuit, which are considered in paper. Such basic cells and ADCs based on them have a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of the CL BC and ADC of photocurrents and their various possible implementations and its simulations. We consider CL BC for methods of selection and rank preprocessing and linear array of ADCs with conversion to binary codes and Gray codes. In contrast to our previous works here we will dwell more on analogue preprocessing schemes for signals of neighboring cells. Let us show how the introduction of simple nodes based on current mirrors extends the range of functions performed by the image processor. Each channel of the structure consists of several digital-analog cells (DC) on 15-35 CMOS. The amount of DC does not exceed the number of digits of the formed code, and for an iteration type, only one cell of DC, complemented by the device of selection and holding (SHD), is required. One channel of ADC with iteration is based on one DC-(G) and SHD, and it has only 35 CMOS transistors. In such ADCs easily parallel code can be realized and also serial-parallel output code. The circuits and simulation results of their design with OrCAD are shown. The supply voltage of the DC is 1.8 divided by 3.3V, the range of an input photocurrent is 0.1 divided by 24 mu A, the transformation time is 20 divided by 30nS at 6-8 bit binary or Gray codes. The general power consumption of the ADC with iterat

关键词： continuous logical ADC image processor multi-channel sensor systems current mirror equivalence (non-equivalence) functions methods of selection and rank preprocessing

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High-rate Modeling and Computing for Optical Systems: Gigapixel image Formation and X-ray Imaging Physics

High-rate Modeling and Computing for Optical Systems: Gigapi...

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作者： Gong, Qian Duke University

学位级别：Ph.D.

With the rapid development of computational sensing technologies, the volume of available sensing data has been increasing daily as sensor systems grow in scale. This is sometimes referred to as the "data deluge". Many physical computing applications have to spend great effort on meeting the challenges of this environment, which has prompted a need for rapid and efficient processing of massive datasets. Fortunately, many algorithms used in these applications can be decomposed and partially or fully cast into a parallel computing framework. This dissertation discusses three sensing models—gigapixel image formation, X-ray transmission and X-ray scattering—and proposes methods to formulate each task as a scalable and distributed problem which is adapted to the massively parallel architecture of Graphics processing Units (GPUs). For the gigapixel images, this dissertation presents a scalable and flexible image formation pipeline based on the MapReduce framework. The presented implementation was developed to operate on the AWARE multiscale cameras, which consist of microcamera arrays imaging through a shared hemispherical objective. The microcamera field-of-views slightly overlap and are capable of generating high-resolution and high dynamic range panoramic images and videos. The proposed GPU implementation takes advantage of the prior knowledge regarding the alignment between microcameras and exploits the multiscale nature of the AWARE image acquisition, enabling the rapid composition of panoramas ranging from display-scale views to gigapixel-scale full resolution images. On a desktop computer, a 1.6-gigapixel color panorama captured by the AWARE-10 can be delivered in less than a minute, while 720p and 1080p panoramas can be stitched at the video frame rate. We next present a pipeline that rapidly simulates X-ray transmission imaging via ray-tracing on GPU. This pipeline was initially designed for statistical analysis of X-ray threat detection in the context of aviatio

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parallel and Efficient Sensitivity Analysis of Microscopy image Segmentation Workflows in Hybrid Systems

Parallel and Efficient Sensitivity Analysis of Microscopy Im...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Barreiros, Willian, Jr. Teodoro, George Kurc, Tahsin Kong, Jun Melo, Alba C. M. A. Saltz, Joel Univ Brasilia Dept Comp Sci Brasilia DF Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA Emory Univ Biomed Informat Dept Atlanta GA 30322 USA

ISBN: (纸本)9781538623268

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2x. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46x on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

关键词： image segmentation Sensitivity analysis Optimization Algorithm design and analysis Runtime image analysis

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Analysis of Implementing Mobile Heterogeneous Computing for image Sequence processing

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KSii TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2017年第10期11卷 4928-4947页

作者： Baek, Aram Lee, Kangwoon Kim, Jae-Gon Choi, Haechul Hanbat Natl Univ Dept Multimedia Engn Daejeon South Korea Korea Aerosp Univ Sch Elect Telecommun & Comp Engn Gyonggi Do South Korea

On mobile devices, image sequences are widely used for multimedia applications such as computer vision, video enhancement, and augmented reality. However, the real-time processing of mobile devices is still a challenge because of constraints and demands for higher resolution images. Recently, heterogeneous computing methods that utilize both a central processing unit (CPU) and a graphics processing unit (GPU) have been researched to accelerate the image sequence processing. This paper deals with various optimizing-techniques such as parallel processing by the CPU and GPU, distributed processing on the CPU, frame buffer object, and double buffering for parallel and/or distributed tasks. Using the optimizing techniques both individually and combined, several heterogeneous computing structures were implemented and their effectiveness were analyzed. The experimental results show that the heterogeneous computing facilitates executions up to 3.5 times faster than CPU-only processing.

关键词： image processing mobile platform heterogeneous computing embedded GPU GPGPU

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Decoding Gene Expression in 2D and 3D 20th

Decoding Gene Expression in 2D and 3D

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20th Scandinavian Conference on image Analysis (SCIA)

作者： Bombrun, Maxime Ranefall, Petter Lindblad, Joakim Allalou, Amin Partel, Gabriele Solorzano, Leslie Qian, Xiaoyan Nilsson, Mats Wahlby, Carolina Uppsala Univ Div Visual Informat & Interact Dept Informat Technol Sci Life Lab Lagerhyddsvagen 2 S-75137 Uppsala Sweden Stockholm Univ Dept Biochem & Biophys Sci Life Lab Tomtebodavagen 23 S-17165 Solna Sweden

ISBN: (纸本)9783319591292;9783319591285

image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical "slicing" by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.

关键词： 2D and 3D signal detection Microscopy based in situ sequencing image processing & analysis Crosstalk compensation

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MR image Reconstruction Using PMRI and Compressed Sensing

MR Image Reconstruction Using PMRI and Compressed Sensing

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作者： Hassan Shahzad COMSATS Institute of Information Technology

学位级别：博士

Magnetic Resonance Imaging (MRI) is a safe and non-ionizing medical imaging modality that provides excellent soft tissue contrast. The main limitation in MRI is its long data acquisition time. parallel MRI (pMRI) and Compressed Sensing (CS) provide a framework to lessen the MRI data acquisition time. Both pMRI and CS acquire less data in k-space and then minimize the aliasing artifacts by using dedicated reconstruction algorithms. PMRI was introduced in late 1990's and some of its reconstruction algorithms are commercially available in the latest MRI scanners. CS was introduced in 2007 and it is mostly used in research. Recently, Graphical processing Units (GPU) have been used to reduce the computation time in the parallel algorithms. GPU can also be used in MRI reconstruction algorithms to reduce the computation time of reconstruction algorithm (one of the main limitation in MRI). The objective of this thesis is to explore and develop new methods to improve the performance of pMRI and CS. There are four main contributions in this regard. (i) A Graphical processing Unit (GPU) based design is developed in Compute Unified Device Architecture (CUDA) for fast implementation of Sensitivity Encoding (SENSE), a pMRI reconstruction algorithm. The results show that the proposed GPU implementation of SENSE significantly reduces its computation time while maintaining the reconstructed image quality as compared to the conventional SENSE implementation. (ii) Receiver coil sensitivity information plays an important role in SENSE reconstruction. Eigen-value method is used for sensitivity estimation in the proposed work for SENSE reconstruction on GPU and the results are compared with the conventional Pre-scan method. The results show that Eigen-value method considerably improves the SNR of the reconstructed images as compared to the results obtained using Pre-scan method. (iii) Conjugate Gradient SENSE (CG-SENSE), proposed by Pruessmann uses both the Non-Cartesian (NC) and Cartesi

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GNMF Revisited: Joint Robust k-NN Graph and Reconstruction-Based Graph Regularization for image Clustering 26th

GNMF Revisited: Joint Robust <i>k</i>-NN Graph and Reconstru...

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26th International Conference on Artificial Neural Networks (ICANN)

作者： Gu, Feng Zhang, Wenju Zhang, Xiang Wang, Chenxu Huang, Xuhui Luo, Zhigang Natl Univ Def Technol Coll Comp Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Inst Software Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Dept Comp Sci & Technol Changsha Peoples R China

ISBN: (纸本)9783319686127;9783319686110

Clustering has long been a popular topic in machine learning and is the basic task of many vision applications. Graph regularized NMF (GNMF) and its variants as extensions of NMF decompose the whole dataset as the product of two low-rank matrices which respectively indicate centroids of clusters and cluster memberships for each sample. Although they utilize graph structure to reveal the geometrical structure within datasets, these methods completely ignore the robustness of graph structure. To address the issue above, this paper jointly incorporates a novel Robust Graph and Reconstruction-based Graph regularization into NMF (RG(2)NMF) to promote the gain in clustering performance. Particularly, RG(2)NMF stabilizes the objective of GNMF through the reconstruction regularization, and meanwhile exploits a learning procedure to derive the robust graph. Experiments of image clustering on two popular datasets illustrate the effectiveness of RG(2)NMF compared with the baseline methods in quantities.

关键词： k-NN graph Graph regularization Nonnegative matrix factorization Clustering

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Comparison of parallel linear genetic programming implementations 22

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22nd International Conference on Soft Computing: Evolutionary Computation, Genetic Programming, Swarm Intelligence, Fuzzy Logic, Neural Networks, Chaos, Bayesian methods, Intelligent image processing, Bio-Inspired Robotics, MENDEL 2016

作者： Grochol, David Sekanina, Lukas Faculty of Information Technology IT4Innovations Centre of Excellence Brno University of Technology Božetĕchova 2 Brno Czech Republic

ISBN: (纸本)9783319580876

Linear genetic programming (LGP) represents candidate programs as sequences of instructions for a register machine. In order to accelerate the evaluation time of candidate programs and reduce the overall time of evolution, we propose various parallel implementations of LGP suitable for the current multi-core processors. The implementations are based on a parallel evaluation of candidate programs and the island model of the parallel evolutionary algorithm in which the subpopulations are evolved independently, but some genetic material can be exchanged by means of the migration. Proposed implementations are evaluated using three symbolic regression problems and a hash function design problem. © Springer International Publishing AG 2017.

关键词： Genetic algorithms

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Optimized distributed systems achieve significant performance improvement on sorted merging of massive VCF files

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GIGASCIENCE 2018年第6期7卷 giy052页

作者： Sun, Xiaobo Gao, Jingjing Jin, Peng Eng, Celeste Burchard, Esteban G. Beaty, Terri H. Ruczinski, Ingo Mathias, Rasika A. Barnes, Kathleen Wang, Fusheng Qin, Zhaohui S. Emory Clin Dept Comp Sci Atlanta GA 30322 USA Emory Univ Sch Med Dept Med Informat Atlanta GA 30322 USA Emory Univ Sch Med Dept Human Genet Atlanta GA 30322 USA Univ Calif San Francisco Dept Med San Francisco CA 94143 USA JHU Bloomberg Sch Publ Hlth Dept Epidemiol Baltimore MD 21205 USA JHU Bloomberg Sch Publ Hlth Dept Biostat Baltimore MD 21205 USA Johns Hopkins Univ Dept Med Baltimore MD 21224 USA Univ Colorado Dept Med Aurora CO 80045 USA SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Emory Univ Dept Biostat Atlanta GA 30322 USA

Background: Sorted merging of genomic data is a common data operation necessary in many sequencing-based studies. It involves sorting and merging genomic data from different subjects by their genomic locations. In particular, merging a large number of variant call format (VCF) files is frequently required in large-scale whole-genome sequencing or whole-exome sequencing projects. Traditional single-machine based methods become increasingly inefficient when processing large numbers of files due to the excessive computation time and Input/Output bottleneck. distributed systems and more recent cloud-based systems offer an attractive solution. However, carefully designed and optimized workflow patterns and execution plans (schemas) are required to take full advantage of the increased computing power while overcoming bottlenecks to achieve high performance. Findings: In this study, we custom-design optimized schemas for three Apache big data platforms, Hadoop (MapReduce), HBase, and Spark, to perform sorted merging of a large number of VCF files. These schemas all adopt the divide-and-conquer strategy to split the merging job into sequential phases/stages consisting of subtasks that are conquered in an ordered, parallel, and bottleneck-free way. In two illustrating examples, we test the performance of our schemas on merging multiple VCF files into either a single TPED or a single VCF file, which are benchmarked with the traditional single/parallel multiway-merge methods, message passing interface (MPI)-based high-performance computing (HPC) implementation, and the popular VCFTools. Conclusions: Our experiments suggest all three schemas either deliver a significant improvement in efficiency or render much better strong and weak scalabilities over traditional methods. Our findings provide generalized scalable schemas for performing sorted merging on genetics and genomics data using these Apache distributed systems.

关键词： sorted merging whole-genome sequencing MapReduce Hadoop HBase Spark

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