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International Conference on image and Vision Computing New Zealand, ivCNZ

作者： Donald G. Bailey Michael J. Klaiber School of Engineering and Advanced Technology Massey University Palmerston North Institute for Parallel and Distributed Systems University of Stuttgart

Calculation of mean, variance and standard deviation are often required for segmentation or feature extraction. In image processing, 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.

关键词： Hardware Computer architecture Standards Approximation methods Nickel Accuracy Feature extraction

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A parallel solution for high resolution histological image analysis

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COMPUTER methods AND PROGRAMS IN BIOMEDICINE 2012年第1期108卷 388-401页

作者： Bueno, G. Gonzalez, R. Deniz, O. Garcia-Rojo, M. Gonzalez-Garcia, J. Fernandez-Carrobles, M. M. Vallez, N. Salido, J. Univ Castilla La Mancha VISILAB ETS Ingenieros Ind E-13071 Ciudad Real Spain Hosp Gen Univ Ciudad Real Dpt Anat Patol Ciudad Real Spain

This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories. (c) 2012 Elsevier Ireland Ltd. All rights reserved.

关键词： Low and high-level parallel image processing High resolution histological image processing MPI Microscopy Whole slide imaging

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Do you see what i see?

Do you see what i see?

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Applied imagery Pattern Recognition Workshop (AIPR)

作者： P.S. Cerkez DCS Corporation MD USA

Semagrams are a subset of steganography. When a message is transmitted in a non-textual format, (i.e., in the visual content of an image), it is referred to as a semagram. While semagrams are relatively easy to create (as shown in published papers covering hiding techniques), detecting a hidden message in or embedded as an image-based semagram is a greater magnitude of difficultly than typical digital steganography. US Patents issued based on semagram technology show that this feature has been exploited in the copyright/watermarking world to increase protection. In a semagram, the image is the message and they work well for simple messages and dead drops. Attacks on semagrams are primarily visual examinations of artifacts. In the counter-espionage world, the rule of the thumb is that there is always a message hidden in an image or graphic, it is simply up to the steganalyst to find it. In short, detecting semagrams is a matter of recognizing patterns of patterns that represent a hidden message within an image. This presentation provides a brief summary of the technology underlying semagrams, present a short non-technical discussion of the technology used in the attack on semagrams, followed by a discussion on current work and planned future implementations of the proven semagram detection ANN. It will focus on extending the ANN to other domains (e.g., non-visual spectrums, multi/cross spectrum correlation, scene identification, image classification) and efforts to improve the processing speed and throughput via parallel/distributed methods.

关键词： Training Testing image coding Artificial neural networks Context Pattern recognition

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A Multi-granularity parallel Model for Unified Remote Sensing image processing WebServices

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TRANSACTIONS IN GIS 2012年第6期16卷 845-866页

作者： Guo, Wei Zhu, Xinyan Hu, Tao Fan, LiWei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R LIESMARS Wuhan 430079 Peoples R China

The growth of the Web has resulted in the Web-based sharing of distributed geospatial data and computational resources. The Geospatial processing Web (GeoPW) described here is a set of services that provide a wide array of geo-processing utilities over the Web and make geo-processing functionalities easily accessible to users. High-performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high-performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi-granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI-oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub-models. In addition, interfaces, definitions, parallel task scheduling and fault-tolerance mechanisms are described in detail. Based on the model and methods, we propose an open-source online platform named OpenRS-Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi-granularity parallel model for unified remote sensing image processing web services.

关键词： REMOTE-sensing images image processing WEB services parallel programs (Computer programs) GEOSPATIAL data GEOGRAPHIC information systems

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Orthogonal Nonnegative Locally Linear Embedding

Orthogonal Nonnegative Locally Linear Embedding

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Lei Wei Naiyang Guan Xiang Zhang Zhigang Luo Dacheng Tao National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China These authors contributed equally to this paper National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 P.R. China Centre for Quantum Computation and Intelligence Systems and Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

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.

关键词： Nonnegative matrix factorization Locally linear embedding Fast gradient descent Stiefel manifold Dataset Nonnegative matrix decomposition geometrical construction NMF protocol images embedding Flue gas desulfurization Nearest neighbor Orthogonal

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PQH: A Multithreaded parallel NN Search Index for Content-based image Retrieval

PQH: A Multithreaded Parallel NN Search Index for Content-ba...

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26th IEEE International parallel and distributed processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Chen, Hui-zhong Jing, Ning Chen, Yong-guang Chen, Luo Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China Ordnance Engn Coll Shijiazhuang 050003 Peoples R China

ISBN: (纸本)9780769546766

In this paper, we propose a NN search index method called parallel Quantized Hashing (PQH) for relevant retrieval in Content-Based image Retrieval (CBIR) systems. Aiming at improving the performance of query, the PQH partitions the original dataset into several subsets and keeps the pointers to quantized vectors of each subset in a hashing like structure. It only needs to filter parts of the query space to get candidates via neighboring masks and accesses the corresponding real vectors for precise calculation. The neighboring masks are sets of mask vectors indicating cells close to the query point using different metrics. The PQH is multithreaded designed so that the construction and the query can be run in parallel upon multi-core processors. Our extensive experiments confirm that the sequential PQH is of high query efficiency and outperforms the existing methods such as VA+-file and LSH. The parallel run PQH achieves even higher performance and returns appropriate NN results. And more important it is simple for practical implement in real applications.

关键词： kNN multithreaded parallelism CBIR

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Reducing application-level checkpoint file sizes: Towards scalable fault tolerance solutions

Reducing application-level checkpoint file sizes: Towards sc...

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2012 10th IEEE International Symposium on parallel and distributed processing with Applications, ISPA 2012

作者： Cores, iván Rodríguez, Gabriel Martín, María J. González, Patricia Computer Architecture Group University of a Coruńa Spain

ISBN: (纸本)9780769547015

Systems intended for the execution of long-running parallel applications require fault tolerant capabilities, since the probability of failure increases with the execution time and the number of nodes. Checkpointing and rollback recovery is one of the most popular techniques to provide fault tolerance support. However, in order to be useful for large scale systems, current checkpoint-recovery techniques should tackle the problem of reducing checkpointing cost. This paper addresses this issue through the reduction of the checkpoint file sizes. Different solutions to reduce the size of the checkpoints generated at application level are proposed and implemented in a checkpointing tool. Detailed experimental results on two multicore clusters show the effectiveness of the proposed methods. © 2012 IEEE.

关键词： parallel programming

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High-performance computing in image registration

High-performance computing in image registration

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Conference on High-Performance Computing in Remote Sensing II

作者： Zanin, Michele Remondino, Fabio Dalla Mura, Mauro FBK Via Sommar 18 I-38123 Trento Italy Grenoble Inst Technol GIPSA Iab Grenoble France

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.

关键词： High Performance Computing Graphical processing Unit photogrammetry image registration

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parallel AND ADAPTivE UNIFORM-distributed REGISTRATION METHOD FOR CHANG'E-1 LUNAR REMOTE SENSED imageRY 22

PARALLEL AND ADAPTIVE UNIFORM-DISTRIBUTED REGISTRATION METHO...

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22nd Congress of the International-Society-for-Photogrammetry-and-Remote-Sensing

作者： Ning, X. Hao, M. Chinese Acad Surveying & Mapping Inst Photogrammetry & Remote Sensing Beijing Peoples R China

On the October 24th 2007, China launched its first Lunar Probe Satellite "Chang'E I". After the 494 days travelling, the probe vehicle landed accurately at its predetermined landing site on the moon at 52.36 degrees east longitude and 1.5 degrees south latitude. It sent back the first imagery of the lunar surface on 26 November 2007 and accomplished all the scheduled scientific tasks successfully. As the first lunar Probe Satellite, the major goal of Chang'E I mission is to obtain three-dimensional images of the landforms and geological structures of the lunar surface, so as to provide a reference for planned future soft landings. However, due to the dramatic change of the radiation information of the CE-1imagery, the traditional methods that are based on the gray and line characters show the limitation achieving a satisfied result. Moreover, the registration processing between lunar images that cover the whole moon has proved to be very time-consuming. To resolve the above-mentioned registration difficulties, a parallel and adaptive uniform-distributed registration method for CE-1 lunar remote sensed imagery is proposed in this paper. Based on 6 pairs of randomly selected images, both the standard SIFT algorithm and the parallel and adaptive uniform-distributed registration method were executed, the versatility and effectiveness were assessed. The experimental results indicate that: by applying the parallel and adaptive uniform-distributed registration method, the efficiency of CE-1 lunar remote sensed imagery registration were increased dramatically. Therefore, the proposed method in the paper could acquire uniform-distributed registration results more effectively, the registration difficulties including difficult to obtain results, time-consuming, non-uniform distribution could be successfully solved.

关键词： Chang'E-1 registration SIFT parallel calculation uniform distribution

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Computer Simulation of Magnetic Resonance Angiography Imaging. parallel Implementation in Heterogeneous Computing Environment

Computer Simulation of Magnetic Resonance Angiography Imagin...

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Joint Conference on New Trends in Audio and Video And Signal processing - Algorithms, Architectures, Arrangements and Applications (NTAV/SPA)

作者： Klepaczko, Artur Szczypinski, Piotr Dwojakowski, Grzegorz Kocinski, Marek Strzelecki, Michal Lodz Univ Technol Inst Elect Ul Wolczanska 211-215 PL-90924 Lodz Poland

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 image processing methods 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.

关键词： Magnetic Resonance Angiography Computer Simulation images image diagnosis image synthesis Computer systems experimentation computational grids ground truth Blood Vessels Repetition network computing environments distributed computing environment image processing method Magnetic Resonance Imaging

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